Optical Sensing and Detection VIII

Novel real-time measurements of near infrared chromatic refractive index signature of a liquid medium in the 1100-1700 nm range is presented. Two fibre long-period gratings (Bi-LPG) written adjacent to each other in a single-mode fibre by femtosecond laser inscription create eight independent spectral attenuation bands. It is tested on model-wine solutions containing chemical compounds found in commercial wine. The device yielded distinct chromatic signatures for each compound and low limits of detection (1×10-3 to 6×10-5 mol/L, depending on compound). The results obtained from this Bi-LPG fibre device demonstrates its potential of real-time monitoring of substance’s effective chromatic dispersion for real-world applications, such as beverage production.

The primary objective of this study was to develop a fiber optic sensor (FOS) featuring an optical microsphere for the purpose of detecting SARS-CoV-2 in sewage. Through surface modifications to the FOS, it can be tailor-made to exclusively target the specific virus under consideration, which, in this instance, is SARS-CoV-2. Additionally, the measurement signals acquired from the sensor were employed to generate datasets for utilization in machine learning models. This predictive capacity holds significant promise in furnishing early warnings and identifying potential infection outbreaks, thereby enabling timely intervention and control measures to mitigate the dissemination of viral particles. The amalgamation of biosensors and machine learning within the context of Wastewater-Based Epidemiology (WBE) holds the potential to augment the efficacy and efficiency of monitoring infectious diseases within communities. Nevertheless, it is imperative to underscore that the development and validation of such technologies necessitate rigorous testing to assure their reliability and precision.

We propose to use a fiber with a nanostructured square core with a top-hat square-shaped fundamental mode. We present the results of the D-shape fiber fabrication with top-hat square-shaped fundamental mode. We will also show the numerical and experimental analysis of sensors based on such a fiber. A sensor without additional layers is only based on the study of changes in transmitted power. And, SPR and LMR sensors where a layer of metal or dielectric is deposited on the flat surface of the fiber and where the measurement is related to the change in the resonant wavelength.

This research introduces a method that enhances the sensitivity of optical sensors by combining graphene oxide coating and self-image theory. The focus of this study is to develop a sensor for quantitatively measuring glucose concentrations in aqueous solutions that mimic human saliva. Using COMSOL Multiphysics 6.0, the self-imaging phenomenon was simulated with a coreless silica fiber (CSF). The primary emphasis was on the first and second self-imaging points due to their higher coupling efficiency, which boosts sensor sensitivity. The sensor was constructed to match the second self-image point (29.12 mm) and coated with an 80 µm/mL graphene oxide layer using the Layer-by-Layer method post-simulation. The sensor's refractive index characterization demonstrated a wavelength sensitivity of 200 ± 6 nm/RIU. When compared to uncoated sensors, the graphene oxide-coated sensor exhibited an eight-fold improvement in sensitivity for measuring glucose concentrations from 25 to 200 mg/dL, with a low standard deviation of 0.6 pm/min and a maximum theoretical resolution of 1.90 mg/dL.

A distributed temperature and strain sensing (DTSS) system based on Brillouin backscatter was used to measure changes during a 5 month period on an 9km length of fibre optic cable deployed next to a railway line including on an embankment and a bridge. By measuring the amplitude and frequency of the Brillouin backscatter it was possible to determine both temperature and strain changes. Most of the strain changes occurred slowly over many weeks, however some rapid strain changes occurred on part of the embankment on one particular day. Temperature effects were dominated by the diurnal and seasonal variations of the air temperature. At a number of points along the cable there were small optical losses that varied with time, but the ability of the system to also carry out regular Rayleigh OTDR measurements meant that these losses could be accounted for, so they had minimal effect on the data.

We demonstrate the application of distributed fiber optic strain sensing based on optical frequency-domain reflectometry for the early detection and location of fatigue cracks in welds in steel tubular test specimens. To do so, we subjected two welded tubular specimens, equipped with surface-mounted optical fiber sensors, to a resonant bending load and continuously measured the strain distributions in the test specimens without any interruption throughout the entire duration of the test. We show that our method allows for on-the-fly detection and location of fatigue cracks originating from the inner surface of the specimens' wall.

Composite materials are increasingly used in aerospace structures, because of their superior stiffness-to-mass ratio. Due to their ply-stacking nature, they show excellent mechanical performance in the in-plane directions but are much weaker in the out-of-plane direction. Manufacturers and end-users are therefore keen to acquire strength information along that direction. Here, we report on the measurement of three-dimensional strain using fibre Bragg gratings (FBGs) in highly birefringent microstructured optical fibers (MOFs). By combining two MOF-based FBG sensors, we demonstrated three-dimensional strain-based cure monitoring of a fiber reinforced composite subcomponent manufactured by a resin transfer molding (RTM) technique. We also investigated the influence of the presence of the fiber by embedding the MOF in composite coupons manufactured by liquid resin infusion (LRI) and performing a multitude of mechanical tests with these coupons and comparing the mechanical resistance to pristine coupons. It is the first time that such a comparison is carried out for this specific combination of optical fiber and composite material system.

In this study, a tailored hybrid sensing configuration integrating fiber Bragg grating and intrinsic Fabry-Perot interferometer is presented. It enables simultaneous discrimination and tracking of internal temperature and pressure changes within 18650 Li-ion batteries (LiBs). The battery undergoes rigorous cycling tests at different operating conditions and a comparison between them is presented. The optical fiber sensors instrumented into the LiB reveal, throughout overall cycling tests, a compelling correlation between internal and external temperature behavior. The application of systematic Incremental Capacity Analysis derivative curves during battery operation exposes crucial insights into the relationship between pressure and temperature physical parameters, and their electrochemical behavior. This optical sensing approach contributes in this way to a nuanced understanding of internal LiB dynamics, with implications for their optimizing performance and safety in diverse applications.

This work introduces a mathematical model for designing optical fiber displacement sensors (OFDS) capable of simultaneously measuring the displacement and angle of a finite reflective target such as a mirror or plate. Recently, fiber optical sensors have found applications across industries like medical, military, or aerospace. The advantages of fiber optics, including simple structure, low manufacturing cost, high sensitivity, and immunity to electromagnetic interference, make them suitable for extreme environments. Reflective fiber sensors, which modulate reflected light intensity to measure displacement, have a simple structure and a wide measurement range. Currently, attention is directed towards OFDS. We developed the mathematical model for the OFDS design and performed a detailed simulation analysis, including the angular and linear displacement between the reflective target and the OFDS tip, as well as the light intensity distribution profile. We also validated experimentally the proposed mathematical model with custom-made OFDS designs. Finally, we discussed the basic principles and the influence of the operational parameters on the performance of the measurement system.

I will explore various facets of photonic quantum systems and their application in photonic quantum technologies. Firstly, I will focus into quantum foundations and by discuss quantum interference, a key element in photonic quantum technologies. I will highlight how the distinguishability and mixedness of quantum states influence the interference of multiple single photons – and demonstrate novel schemes for generating multipartite entangled quantum states. I will then address photonic quantum computing, specifically focusing on the building blocks of photonic quantum computers. This includes the generation of resource states essential for photonic quantum computing. I will then shift to photonic quantum networks, covering both their hardware aspects and showcasing quantum-network applications that extend beyond bi-partite quantum communication. Lastly, I will outline how photonic integration facilitates the scalability of these systems and discuss the associated challenges.

Joining the rich photophysics of organic light-emitting materials with the exquisite sensitivity of optical resonances to geometry and refractive index enables a plethora of devices with unusual and exciting properties. Examples from my team include biointegrated microlasers for real time sensing of cellular activity and long-term cell tracking, as well as the development of photonic implants with extreme form factors and wireless power supply that support thousands of individually addressable organic LEDs and thus allow optogenetic targeting of neurons deep in the brain with unprecedented spatial control. Very recently, by driving the interaction between excited states in organic materials and resonances in thin optical cavities into the strong coupling regime, we unlocked new tuning parameters which may play a crucial role in the next generation of TVs and computer displays to achieve even more saturated colour while retaining angle-independent emission characteristics.

Accurate and precise measurement of the radiation energy delivered to and absorbed by the patients' tissues is of great importance in radiation therapy quality assurance. Utilizing radioluminescence of selected materials for radiation detection has shown great potential for developing high spatiotemporal resolution dosimeters for radiation therapy applications. Optical fiber-based scintillator dosimeters are promising candidates for small-field and in vivo dosimetry. Scintillation imaging, on the other hand, is a promising method for two-dimensional dosimetry. There are, however, challenges associated with these techniques, such as mitigating the impact of Cherenkov radiation and ionization quenching of scintillators in certain radiation fields. In this work, we review the progress in radioluminescence dosimetry techniques for radiation therapy applications.

Scintillation-based fiber dosimeters are a powerful tool for minimally invasive localized real-time monitoring of the dose rate during Low Dose Rate (LDR) and High Dose Rate (HDR) brachytherapy (BT). This paper shows the design, fabrication and characterization of such dosimeters, consisting of scintillating sensor tips attached to polymer optical fiber (POF). The scintillating sensor tips consist of inorganic scintillators, dispersed in a polymer host. We present the design, fabrication and characterization of those sensor tips. The manufacturing is done by means of a custom compression and transfer moulding process implemented on a commercially available hot embossing machine. We show the manufacturing of 237 sensor tips, which are subsequently attached to the end of the POF using UV-curable adhesive. Finally, we perform dosimetry experiments in water phantoms which show a great potential for in-vivo dosimetry for brachytherapy.

Laser Ablation (LA) is a minimally invasive tumor treatment, particularly valuable when conventional methods are unsuitable. LA utilizes laser energy delivered using an optical fiber, offering advantages like electromagnetic immunity and flexibility of the applicator. Challenges in achieving complete tumor destruction and minimizing collateral damage prompt the need for improved monitoring and LA control. Fiber optic sensors offer advantages over conventional methods, such as small size, electromagnetic immunity and biocompatibility. This study employs fiber Bragg grating array sensors for precise temperature monitoring and implements closed-loop temperature control based on these measurements. Preliminary findings show effective control of temperature, indicating the potential of fiber sensors use in LA as a promising technique to enhance the efficacy of cancer treatment.

We present and discuss an experimental fiber-optic sensing system developed for application in a coal mine. One of the major issues of coal mine engineering is the condition of the rock mass above the roadways in which miners extract coal. The condition is typically monitored using measurement anchors with electronic strain gauges. In the demonstrated system, the electronic sensors are replaced with FBGs. A fundamental cell of the network consists of gratings that are glued to a special groove on the anchor, while the output signal is read out with an interrogation system. The interrogator is realized either using a commercially available device or with a photonic integrated circuit. The system has been assembled and tested both in the anchor laboratory and in a test site in a roadway of a mine. The first characterization results are promising and prove the feasibility of fiber-optic sensors for rock mass monitoring.

We report on the design and development of a compact, easily produced low cost, Fabry Perot (FP) optical fiber sensor that exhibits distinct wavelength shift response in the presence of methanol, ethanol and isopropanol vapours. A 25μm cavity is formed at the end face of a single mode optical fiber using commercially available UV photopolymerizable resin. The recorded sensitivities, before saturation, for the aforementioned VOCs vapours are 20, 100 and 180 pm/mbar respectively. Regarding the detectivity of the probe, based on experimental results and a moderate wavelength estimation accuracy of 10 pm, a projected limit of detection of 0.5 ppb under saturated methanol vapours is deducted following a Langmuir isotherm equation fitting

Volatile organic compounds (VOCs) show great potential as biomarkers in non-invasive disease detection. We propose a cascaded Fabry-Perot (FP) fiber sensor for characterizing ethanol and 2-propanol. The sensor fabrication involves splicing a single mode fiber to a silica capillary (SC), followed by cleaving the SC. The SC tip is immersed in a PMMA solution and heated for rapid solvent evaporation, forming a dual cavity. The sensor was tested in the gas phase, using binary hydroalcoholic mixtures containing ethanol and 2-propanol. The response attained was nonlinear, with a maximum sensitivity of -133.7 pm/vol.% and -173.0 pm/vol.% for 2-propanol and the ethanol, respectively, in a range between 35-50 vol.%.

In this work, we present the development of a porphyrin-embedded graphene oxide-based optical fiber sensor using evanescent wave absorption (EWA) spectroscopy for the detection of multiple environmentally hazardous volatile gases. The changes in absorption spectra in the visible region were procured when exposed to the target gases, and the obtained spectroscopic signatures for various gases were examined using an unsupervised machine-learning method. The analysis provides a protocol for grouping the spectra of similarities and forming classes to identify gas molecules. The porphyrin-embedded graphene oxide-coated U-bent fiber probe showed enhanced selection capability compared to the graphene oxide-coated probe. This low-cost photonics platform has enormous potential for developing on-field gas monitoring devices.

Detection of volatile organic compounds (VOCs) with particular attention to the BTEX group consisting of benzene, toluene, ethylbenzene, and xylene has risen as an important task in fields as environmental monitoring and breath analysis. Sensors based on optical detection techniques may represent an alternative to traditional approaches. However, optical sensing has been limited in terms of measurements selectivity as BTEX point out absorption features around 3.3 µm, where strong interferences from hydrocarbons occur. This issue can be eliminated operating at longer wavelength in the region 13 µm-15 µm, where BTEX show distinct and isolated absorption features. However, the investigations in this region have been limited by the lack of suitable laser sources as well as by the low performances of commercial detectors. In this work, a new approach to benzene detection is proposed, employing a long wavelength InAs-based QCLs as light sources and a quartz tuning fork as detector for TDLAS measurements, in light-induced thermoelastic spectroscopy (LITES) detection scheme. Analytes detection at part-per-billion level with a shoe-box size design is demonstrated.

Atmospheric composition varies both spatially and temporally, notably in terms of relative humidity and greenhouse gases. Current methods rely on massive and expensive sensors deployed sparsely. Here, we are interested in developing a multi-gas sensor that targets very small atmospheric composition variations. In addition, it intends to be gas specific, lightweight, autonomous and compact. Targeted gases are either greenhouse gases such as CO2, CH4 , and N2O, or toxic gases such as CO and SO2. For this purpose, we have developed a Photoacoustic Spectroscopy sensor, using Mid Infrared QCL laser sources, which achieve a very narrow spectral linewidth and have a good potential for miniaturization. We developed a dedicated electronic device to perform data acquisition. Our prototype is beginning its calibration phase in a lab environment, prior further integration for real time measurement.

We report on the development of a quantum-cascade-laser-based infrared sensors aiming for backscattering spectroscopy in forensics and addressing a large variety of different samples. By multiplexing two MOEMS-EC-QCLs, which provide kilohertz scan speed, we almost doubled the spectral coverage without sacrificing the measurement speed. Here we report on the system design and show some first results on forensic relevant samples.

Methane is a significant contributor to global warming so reducing methane emissions, particularly from oil and gas operations, is among the most cost effective, impactful actions governments can take to achieve climate goals. In this work, the manufacture and performance of a low-cost optical NDIR methane sensor with <1 mW power consumption, compatible with wireless deployment in industrial environments, is presented.

In this work, we explore the use of a matrix of integrated Mach-Zehnder interferometers (MZI) for detection of volatile organic compounds (VOCs) separated by gas chromatography. We successfully achieve separation and detection of three compounds with similar molecular weight: heptane, octane and toluene. The photonic system is able to run at more than 200 Hz thanks to a coherent detection scheme with a fixed wavelength laser. This fast acquisition rate should allow to analyze complex mixtures by detection of tens of chromatographic peaks in only a few tens of seconds.

The monitoring of certain gases is crucial for obtaining Air quality indicators promoting the environmental monitoring. The detection of greenhouse gases (GHG) is especially on the forefront of heath related issues, also dictated by ambitious climate worldwide accords. Currently used spectroscopic solutions remain coslty and bulky, limiting their widespread adoption. We present a study of a MID-IR spectroscopy system developed on a silicon photonics platform, utilizing a Bragg grating mirror cavity, between an optical Bragg source and grating mirror.

Surface plasmon resonance detections based on phase changes have demonstrated superior sensitivities over the intensity, spectral and angular methods due to the singularity effect (abrupt change of phase value) observed at resonance. The Goos–Hänchen effect, a higher first order derivative of the phase, can be observable as a lateral displacement of the reflected wave at total internal reflection and magnified by the surface plasmons. The GH sensitivity can be further improved through the addition of a phase change material nanolayer beneath the gold. Vanadium dioxide (VO2) belongs to the family of phase change materials that exhibit reversible insulator-metal behavior when heated above 68℃. Adding a thin layer of VO2 below the metal proved to theoretically enhance the sensitivity of a conventional gold-based surface plasmon biosensor (up to 28 times of improvement in comparison with the bare gold configuration).

Environmental sensing is a topic of increasing interest and has triggered much research towards fully integrated sensor solutions. In this context optical measurement approaches in the infrared can provide intrinsic selectivity and sensitivity for integrated gas sensors. Recently, we proposed the possibility of the incorporation of IR-active plasmonic materials to photonic crystal waveguides, which could allow to increase sensitivities and significantly reduce the size of such sensors. Here, we will first present the overall approach and then compare two possible specific realizations.

This comparative study investigates the refractive index sensitivity of gold (Au), titanium nitride (TiN), and hafnium nitride (HfN) nanodisk arrays in exciting surface lattice resonances and localized plasmon resonance modes. The plasmonic nanodisk arrays are fabricated using electron beam lithography and optimized for disk diameter and array period. This work provides valuable insights into the comparative performance of TiN, HfN, and Au nanodisk arrays for refractive sensing-based applications.

Plasmonic biosensing is an optical technique that based on refractive index change when the target molecules interact with the sensing surface. Main plasmonic material used in this type of biosensors is gold. Our work is dedicated to design a novel sensing SPR chip with vanadium dioxide (VO2) nanolayer, known for its unique insulator-to-metal phase transition in the near-infrared region. VO2 thin film is deposited using Cross-Beam Pulsed Laser Deposition (CB-PLD) method and gold layer deposition is performed by sputtering. By employing the VO2 nanolayer, we create a highly responsive biosensing interface (with a much-improved sensitivity and also a wide dynamic measurement range). The VO2 layer's ability to modulate the refractive index enables precise control of the excited plasmon resonance. This interaction results in enhancing sensitivity and the capability to detect low-concentration analytes with high accuracy.

Surface plasmon resonance (SPR) optical sensors have attracted considerable attention, finding diverse applications, particularly in the fields of biomedicine and chemistry. While single metal layer based SPR sensors are commonly used, their resolution is limited by broad resonances. Consequently, researchers have dedicated efforts to achieve sharper resonances and heightened sensitivity. In this study, we investigate the use of multilayer structures for bulk sensing and elucidate how they contribute to improving sensor sensitivity. Additionally, we explore thin film sensing, conducting a thorough comparative analysis of monolayer and multilayer structures. Our findings reveal that thin film sensing based on monolayer structures outperforms multilayer stacks. This superiority is linked to the reduced sensitivity observed in the latter, stemming from the compromised coupling between Surface Plasmon Polariton (SPP) modes within the multilayer structure upon the application of the sensing layer. This research not only advances our understanding of SPR sensors but also highlights the potential of tailored multilayer structures in optimizing sensor performance.

Integrating Photonic-Integrated Circuits (PICs) into microfluidic devices for diagnostics faces several challenges such as handling small PICs, ensuring interface access, managing temperature & UV coatings, and bubble-free sample transfers. CSEM offers unique services merging cleanroom packaging, microfluidic design, and prototyping, aiding PIC and diagnostics industry partners. Collaborations with numerous partners have led to tailored PIC integration solutions for sepsis detection, bioreactor contamination detection, food safety testing, and extracellular vesicle detection. CSEM's packaging for small PICs drastically reduces costs (up to 10-fold) and enhances market competitiveness. Leveraging simulations for fluid dynamics and chemical interaction optimizes development, increasing detection speed in one case by around 4000 times. CSEM also pioneers in-line degassing, on-cartridge heating, and liquid storage solutions. Our upcoming talk will explore these challenges and present solutions within specific use cases.

The presentation focuses on allergens detection in foods through a specialized silicon photonics platform developed at CEA-Leti. This platform utilizes arrays of biofunctionalized Mach Zehnder interferometers with coherent detection at λ ≈ 850 nm. We will show how these label-free and multiplexed biosensors can detect allergens at extremely low concentrations, even at ppm level. The performance benchmark will cover component and system-level optimizations, including sensitivity, drift correction, compactness, and microfluidic packaging.

This study introduces an innovative approach to enhance the utilization of carbon fiber thermosetting composites in advanced structural engineering by addressing the challenges of high manufacturing costs and limited production rates. We develop, deploy and test an ML pipeline utilizing PIC-based sensors (SOI technology, 220 nm thick, fabricated at IMEC’s MPW). They are based on a Bragg structure, packaged using ball lenses and suitable for operating at 180 degrees Celsius and 5 bar pressure. The focus is on accurately predicting two crucial parameters: Cure time and Temperature Overshoot, vital for determining the process duration and part quality. Using advanced tools and sensors, this study achieves a high prediction accuracy of 98% in millisecond scale while effectively handling the outliers. The ML pipeline allows the real-time process optimization of manufacturing process, minimizing the cost, and providing insights into the quality of the composite part through the in-depth monitoring of the process.

This work presents the designs and performance of a series of photonic integrated interrogators developed and investigated for the last few years by the Eastern Europe Design Hub team at Warsaw University of Technology. The interrogators were designed to monitor dynamic signals from fiber Bragg gratings (FBGs) and manufactured in the generic indium phosphide (InP) platform. In particular, the main design assumptions will be presented, along with parameters initially proven by versatile electrical and optical characterization. Possible applications and further development of solutions for integrated photonic interrogators will also be discussed.

The Total Solar Irradiance (TSI) is critical to the Earth's climate system, and its variability plays a crucial role in its long-term evolution. This study proposes a new approach to investigating irradiation and advancing our understanding of solar radiation's impact on Earth's climate, using photonic integrated circuits (PICs) and Mach Zender Interferometer (MZI) technology. The Brazilian National Institute for Space Research (INPE) initiated a GSST project comprising two instrument designs: a small radiometer constructed on a photonic integrated circuit. The primary objective of this research is to reduce instrument uncertainty and improve temperature measurement techniques in radiometers. These findings contribute to the ongoing efforts to enhance temperature measurement techniques, aiming to deepen our comprehension of how the Sun's energy influences Earth.

Multiplexed structures out of dual optically coupled 4D microtoroids fabricated with two-photon polymerization and their biosensing-relevant application are discussed. We analyze the sensing performance for detection of small pH value alteration around the neutral solution for independent and optically coupled resonators that are characterized by appearance of the additional signal for back-radiated mode. We demonstrate detectability of pH value changes at the level of 0.01 by using the signal back-radiated mode in the coupled regime together with the material-enhanced spectral shift.

Optical sensing is of preeminent importance for a variety of applications: it can enable detection of harmful or desired contaminants, it can confirm that expected reactions have taken place and it can be used for quantitative analysis of the processes under study. We report on photonic crystal optical sensors based on disorder-induced light confinement in photonic crystal waveguides in silicon nitride, showing that we can make use of fabrication imperfections as a means to add functionalities to the fabricated devices. We prove their suitability for the detection of liquid contaminants at room temperature and investigate their response to refractive index changes. We also show that temperature can be used to tune and modify the quality factor of the cavity resonances, allowing local temperature sensing. Compared to engineered photonic crystal cavities, making use of disorder as a resource allows the spontaneous formation of tens of high-quality optical cavities in a fabricated device that does not require time-consuming optimizations or exact repeatability of the fabrication process - an important result in view of scalability of photonic crystal sensors.

Frequency comb cavity-enhanced spectroscopy has proven itself extremely useful in terms of the effective bandwidth, measurement precision, and acquisition speed. At the same time integrated solutions for both comb source and cavity, based on silicon nitride microring resonators, are readily available. The combination of these two factors motivates our work, in which we demonstrate comb-cavity coupling for cavity-enhanced wideband spectroscopy on-chip. By means of a thermo-optic tuning we can characterize the integrated dispersion of the microring resonator as well as to assess the corresponding lineshape data. THz Kerr frequency comb spanning up to 800 nm makes it possible to overperform industrial table-top tunable sources in terms of the available bandwidth, power consumption, and compactness. We analyze a simple model for the evaluation of the cavity-enhanced spectroscopy performance and perform numerical simulations, which are shown to be in a good agreement with experimental results. Our platform offers intriguing applications in diverse fields, spanning from biology and medicine to photonic design and fundamentals of light-matter interaction.

Optical and acoustic resonators based on distributed Bragg reflectors (DBRs) hold significant potential in both applied and fundamental research [1]. In the presence of perfectly flat material interfaces and surfaces, the DBR resonator quality factor primarily depends on the number of DBR pairs and can be arbitrarily increased by adding more pairs. However, the reality of material growth and fabrication always entails some surface roughness, even in samples produced using cutting-edge techniques. In this work, we present an analysis of the impact of surface roughness and top layer thickness variations on the performance of both Fabry-Perot and open-cavity resonators based on -optical and acoustic DBRs. Our findings illustrate that even a small, nanometer-scale surface roughness can appreciably reduce the quality factor of a given cavity. These effects hold direct relevance for practical applications which we investigate through two illustrative case studies [2]. References [1] N. Somaschi, et al, Nature Photonics 10, 340 (2016). [2] K. Papatryfonos, E. R. C. de Oliveira, N. D. Lanzillotti-Kimura, arXiv:2309.13649 (2023).

Bovine brucellosis is an infectious illness caused mainly by Brucella abortus that may affect domestic and wild animals. Accurate and fast diagnosis is critical for disease control and eradication. Thus, we have identified Brucella abortus antibodies in bovine serum by exploring the agglutination process that is carried out when positive samples are mixed with the antigen. In this case, when placed above the on-chip integrated waveguide, the reaction led to scattering spots that indicated the positive serum. The monitoring was performed through optical images over time and analyzed by artificial neural network. Classification models were able to differentiate the positive from the negative samples with 81.25% accuracy. This work may represent a breakthrough for the diagnosis of other infectious diseases.

The sensing behavior of graphitic carbon nitride (g-C3N4) allotropes (fabricated with thermal polymerization of urea or melamine) by monitoring their laser induced room temperature photoluminescence (PL) emission, is demonstrated. Their sensing behavior has been investigated at oxygen atmosphere, upon optical excitation with a laser source (248 and 355 nm) in wavelength for different energy densities. The observed variability of PL features of g-C3N4, such as spectral intensity and wavelength, offers a systematic way to monitor the environmental changes in a reliable manner, opening the path for exploiting g-C3N4, as optical sensing material.

Optical cavities with nonlinear elements and delayed self-coupling are widely explored candidates for photonic reservoir computing (RC). For time series prediction applications that appear in many real-world problems, energy efficiency, robustness and performance are key indicators. With this contribution I want to clarify the role of internal dynamic coupling and timescales on the performance of a photonic RC system and discuss routes for optimization. By numerically comparing various delay-based RC systems e.g., quantum-dot lasers, spin-VCSEL (vertically emitting semiconductor lasers), and semiconductor amplifiers regarding their performance on different time series prediction tasks, to messages are emphasized: First, a concise understanding of the nonlinear dynamic response (bifurcation structure) of the chosen dynamical system is necessary in order to use its full potential for RC and prevent operation with unsuitable parameters. Second, the input scheme (optical injection, current modulation etc.) crucially changes the outcome as it changes the direction of the perturbation and therewith the nonlinearity. The input can be further utilized to externally add a memory timescale that is needed for the chosen task and thus offers an easy tunability of RC systems.

Programmable photonic circuits manipulate the flow of light on a chip by electrically controlling a set of tunable analog gates connected by optical waveguides. Light is distributed and spatially rerouted to implement various linear functions by interfering signals along different paths. A general-purpose photonic processor can be built by integrating this flexible hardware in a technology stack comprising an electronic monitoring and controlling layer and a software layer for resource control and programming. This processor can leverage the unique properties of photonics in terms of ultra-high bandwidth, high-speed operation, and low power consumption while operating in a complementary and synergistic way with electronic processors. This talk will review the recent advances in the field and it will also delve into the potential application fields for this technology including, communications, 6G systems, interconnections, switching for data centers and computing.

Authentication of milk is considered of high importance, since milk is among the top products facing food fraud and adulteration. We present the use of broadband diffuse reflection spectroscopy (350 – 1700 nm) with Linear Discriminant Analysis (LDA) to achieve a rapid and non-destructive milk identification. Specifically, we demonstrate the differentiation between whole cow milk, semi-skimmed cow milk, skimmed cow milk, goat milk, and lactose-free milk. The diffuse reflection spectrum was captured using a diffuse reflection integrating sphere capturing all reflected light independent on the reflection angle. Subsequent data processing included a Standard Normal Variate transformation, followed by the application of a feature selection algorithm in combination with LDA, and 5-fold cross-validation. The key influencing wavelengths were identified to be 465 nm and 936 nm. In general, classification performances exceeding 99.8% were achieved. Consequently, we believe these presented results contribute to an improved milk classification, enhancing food quality.

Archaeometry research on historic glasses most often relies on getting insights on the chemical composition of the material. In the last decades the B-PHOT research group has worked intensively on the in situ use of UV-Vis-NIR absorption spectroscopy to this study field. In our recent research we focused on naturally coloured window glasses with a post-medieval glass signature from the Low Countries. We successfully demonstrated how this non-destructive technique can distinguish two High Lime-Low Alkali glass subgroups linked to two different time frames, i.e., the end of the 15th-middle of the 16th centuries and the second half 16th-17th centuries, respectively. This classification is based on a well selected set of optical metrics reflecting differences in ionic and atomic iron and cobalt contents. In this paper we present the further validation of the optical technique by studying glass material from different post-medieval archaeological Flemish sites.

The need for non-invasive methods able to identify the composition of various semiconductor types in material streams arises at production, recovery and recycling phases of their life cycles. In this work, we propose spectral proxies based on electronic properties (i.e. band gap values) from HSI-reflectance sensors and vibrational fingerprints (i.e. Raman-active phonon modes) for identification of semiconductors. We present a multi-sensor application on several semiconductor types (GaAs, GaSb, InP, 4H-SiC, and Borosilicate), followed by a discussion regarding the limitations of each spectral proxy.

We will show that two-photon (2P) excitation is very suitable for selectively characterizing pollution primary nanoparticles (NP) produced by combustion. The NP presents 2P absorption, scattering, and emission associated with the spatial arrangement of molecular aggregates.

The application of surface treatments to cork stoppers is presently a common practice in the wine industry, designed to achieve maximum performance and optimal costumer experience of premium products. Unfortunately, current coating techniques lack efficient process control tools, often resulting in faulty products being detected to late, already in use, compromising performance, product quality and mining consumer confidence. In this work a fully automated system equipped with machine vision and automatic feeding of corks, was coupled with an imaging LIBS setup and used to perform a benchmarking against conventional quality control methods. Results clearly demonstrate the capability of the new LIBS system to effectively evaluate in real time the quality of silicone based surface coatings in cork stoppers, effectively working as a tool for process control providing a route for effective optimization.

The global demand for lithium is rising because the light metal is indispensable for battery production. Geothermal plants are considered a potential source of lithium, as the geothermal brines often contain significant lithium concentrations. The most promising approach for lithium extraction from geothermal brines so far appears to be the selective sorption of lithium to a sorbent. To ensure efficient and safe extraction of lithium, especially at an industrial scale, the constant monitoring of the process is essential. Traditionally, this monitoring has been performed using time-shifted and cumbersome laboratory processes. We suggest a fast, contactless and in-situ optical measuring technology based on laser-induced breakdown spectroscopy (LIBS), for the inline monitoring of the lithium concentration during the lithium extraction process from geothermal brines. Our system allows for the accurate quantification of lithium in saline brines, with an average relative uncertainty of less than 2 %, paving the way for its integration into Li extraction facilities in geothermal power plants.

Prefabricated concrete has gained significant popularity in the construction industry due to advantages, including cost-effectiveness, reduced construction time, and enhanced quality control. The curing process of prefabricated concrete elements is particularly crucial as it determines their strength development, durability, and overall quality. In this study, we present our work on the development of an optical measurement system for the monitoring and optimization of the prefabrication process. We compare multispectral camera-based methods with the application of multispectral laser scanners in wavelength regions covering the visible and near infrared spectrum. Based on our measurements, we propose the ideal setup to monitor the surface of the concrete elements and capture precise and continuous data on the hydration. We further evaluate the possibility to derive process parameters from it such as the ideal time for dismantling of formwork. The remote monitoring capability of our system enables real-time data acquisition and analysis, allowing to improve the prefabrication process.

Spectral imaging allows seeing subtle optical signatures or light wavelengths invisible for human vision. This has potential for numerous industrial applications and a large economic impact by improving quality inspection, increasing automation and the development of innovative applications. However, current commercial spectral imaging systems (both hyper- and multi- spectral) are either too expensive or inflexible to be used in industrial applications, whereas standard RGB cameras are often difficult and mostly impractical to classify those materials that are looking visually similar. In this paper, we propose a new framework for a multispectral camera design. In particular, we first build a digital twin to simulate the output of a camera given a hardware configuration and details on the scene/object, then we optimize the wavelengths according to a specific application defined by end-users for the best filters selections. By combining the filters, illumination, sensor, lenses, pixel size, etc. from digital twin and optimization, we finally design a multispectral camera for the end-users, which provides a low-cost, flexible, extendable solution for industrial users.

Spectral imaging is a powerful technology that uses spatially referenced spectral signatures to create informative visual maps of sample surfaces that can reveal more than conventional RGB-visual images. Different spectroscopy modalities can provide different information about the same sample, such as elemental composition, molecular structure, and molecular composition. However, each modality has its own limitations and challenges, such as low spatial resolution, high noise, or complex data analysis. In this work, we will go over multimodal spectral knowledge distillation, a disruptive approach that tries to overcome these limitations by combining techniques to capitalize on their individual strengths. In this approach, one technique acts as an autonomous supervisor for the other, leveraging the higher degree of knowledge and interpretability of one of the techniques to increase the performance and transparency of the other. We present some example scenarios with LIBS, HSI, and Raman spectroscopy, discussing the impact of this new approach for scientific and technological applications.

The excitation-emission matrix (EEM) is the fluorescence intensities at each excitation and emission wavelength combinations and provides valuable information. In real environments, eliminating the effects of ambient light is important because it can interfere the EEM measurements. A simple idea calculates the difference between the EEM obtained with and without the excitation light. However, this kind of conventional method can remove only time-invariant components but not time-varying components. To solve the problem, we propose a novel dynamic lighting using spread spectrum technique. In the proposed method, multiple wavelengths are modulated according to different orthogonal codes and simultaneously irradiated. The fluorescence intensities are then detected. Subsequently, the EEM is reconstructed based on the orthogonal codes. The proposed method allows us to not only eliminate the time-invariant components but also average the fluorescence intensities to suppress the time-varying components. We experimentally verified that the proposed method removes the ambient light effects.

This study explores the application of machine learning (ML) to fluorescence spectroscopy for the detection of aging in extra virgin olive oil (EVOO). EVOOs were tested for quality deterioration over time using UV-absorption according to the European regulations. Two wavelengths (480 nm and 300 nm) were identified as most relevant in assessing EVOO quality. ML algorithms applied to fluorescence spectral data could distinguish between fresh and aged EVOO with over 90% accuracy, suggesting an alternative to traditional chemical analysis. The findings pave the road to the development of a portable device for quick, reliable olive oil quality testing.

Scientific analysis of historic glasses is needed to increase our understanding of glass evolutions and to distinguish fake from authentic. In addition, glass researchers are often confronted with the need to perform non-destructive analytical methods in situ. A recent focus of our research concerned the identification of the optical signatures to distinguish different post-medieval glass families. UV-Vis-NIR absorption spectroscopy appeared to be extremely useful as a non-destructive technique to discern two High Lime-Low Alkali glass subgroups. However, since HLLA-glass is not the only characteristic glass composition, a pre-requisite is to first exclude the non-HLLA material. For that we have worked out a methodology applying portable X-ray fluorescence spectrometry (p-XRF). Although p-XRF has a poor detection for elements with a low atomic number, we succeeded in demonstrating its usability. The rationale behind the clustering of the historic glasses, is to use heavy elements as proxies for the lighter ones.

We demonstrated a novel technique to improve the signal to noise ratio (SNR) of the frequency domain interferogram of a dual comb spectroscopy (DCS) setup, by about 5 dB, based on temporal shifting of the interfering pulse trains in the two channels. The experimental setup utilized a fiber based DCS architecture where electro-optic modulators (EOM) were used to generate two optical combs (or pulse trains). Due to this carefully adjusted periodic phase shifts, the interferogram now was time-multiplexed, or in other words included a larger number of peaks within a given time frame compared to when an unshifted case. There is nevertheless a tradeoff between the peak power and the bandwidth of the interferogram comb envelope in the spectral domain. The experimental results were also confirmed numerically and a relationship between the SNR improvement and the rate of phase shifting was established. These results open new possibilities in SNR improvement of EOM based multidimensional spectroscopic techniques and provide a powerful resource to execute sparse sampling and other complex techniques to maximize the amount of useful information in the interferogram data of a DCS setup.

Diagnosis of SARS-CoV-2 infection allows for intervention, control, and disease management for COVID-19. The gold standard to detect the virus is the real-time reverse transcriptase-polymerase chain reaction. It also requires expensive equipment, involves highly trained staffs, and has a long turnaround time. Other fast diagnostic methods have limitations such as lower specificity and sensitivity. Thus, there is a critical need for a rapid and low-cost point-of-care (POC) diagnostic method from patients. Here we present three rapid, portable, and cost-effective methods to detect SARS-CoV-2 in human nasopharyngeal swab specimens using surface enhance Raman spectroscopy (SERS) and deep learning: direct detection, RNA hybridization, and ACE-2 capture. All three strategies can achieve > 99% accuracy to classify the positive and negative specimens and the test-to-answer time is within 20 min. These results indicate that SERS combining with deep learning could serve as a potential rapid POC COVID-19 diagnostic platform.

Surface-Enhanced Raman spectroscopy (SERS) has proven its powerful ability to precisely characterize biological substances at very low concentrations without the use of a specific biofunctionalization. In this work, SERS substrate fabrication based on two-photon polymerization (2PP), metrology, and biosensing characteristics are discussed. Raman spectra of 1,2-bis-(4-pyridyl) ethylene (BPE) molecule at various concentrations are analyzed. The Asymmetric Least Squares (ALS) method is applied to subtract the baselines from the spectra. Next, the standard deviation (STD), enhancement factor (EF), and limit of detection (LOD) are calculated. The SERS substrates show up to 10^6 Raman signal enhancement factor, comparable with commercial substrates. The fabrication of SERS substrates based on the 2PP technique takes from less than a minute to 2 hours. The process is well-controlled and reproducible for reaching a uniform distribution of nanostructure arrays. Finally, the SERS substrates can be used for a broader range of applications and the characterization of different molecules.

A highly reproducible SERS substrate is designed and built based on two-dimensional Ag grating structures. The periodic substrate gives ultrahigh SERS enhancement due to coupling between localized and extended plasmons, while its uniform surface minimizes signal variability. Moreover, the two-dimensional structure eliminates the polarization dependency of the incident light, ensuring polarization-independent SERS, hence higher reproducibility. The substrate is designed using the finite element method (FEM) to achieve similar enhancement for both TE and TM polarized light. The chip is fabricated using electron beam lithography and characterized for surface-enhanced fluorescence (SEF) and SERS using R6G as the target indicator molecule. The results show an eight-fold enhancement compared to a bare Ag thin film for SEF and a staggering enhancement of 2.5x106 times for SERS. To evaluate the chemical sensing performance of the sensor surface, we characterize the chip for picric acid (PA) detection in a concentration range from nM to mM. The minimum detection limit for PA is found to be 3.04 nM, Acknowledgement: This research is funded by NATO project NOOSE #MYP G5814

In this work, we have employed several novel large-area nanofabrication methods to fabricate different surface-enhanced Raman scattering (SERS) sensor chips. The SERS sensor chips can be uniformly developed over a large-area with high reliability and reproducibility. These SERS sensor chips are employed for the detection of chemical molecules (such as pesticides and dyes) and biological molecules. Furthermore, numerical modeling of the proposed substrates was carried out using Finite Difference Time Domain (FDTD) modeling to study the effect of structural parameters of the plasmonic nanostructures on the resonance wavelengths and the electromagnetic (EM) enhancement factors.

Surface Enhanced Raman Spectroscopy (SERS) is a rapid, precise, and sensitive approach for ultra-trace analyte detection and analysis. Our work employed an EBL-fabricated Au-gratings nanopattern as a SERS-active substrate to detect Rhodamine 6G and bio-molecule/biomarker urea, which is a biomarker of kidney disease at concentrations up to 10-9 M.

Energy consumption has increased exponentially due to societal growth, leading to ecological issues. Green hydrogen (H2) offers a safer alternative to fossil fuels, making it a promising alternative for sustainable energy storage and consumption. However, its flammability makes it crucial to monitor concentrations for safer use. Optical sensors have been developed to monitor H2 concentration in harsh environments with high sensitivity and remote measurement. A numerical study and experimental confirmation of a fiber sensor based on Surface Plasmon Resonance (SPR) for H2 detection are presented. This sensor is composed of a multimode fiber with an SPR structure of a metal/dielectric/Pd, where the Pd acts as a sensitive layer. The plasmonic active materials studied are Ag and Au, while TiO2 and SiO2 are used as dielectric buffer materials. The optimized configurations were characterized to confirm the implementation as a sensor, namely in terms of response time, sensitivity, and selectivity.

In this study, we combined an optical tweezer capable of precisely placing single cells at desired locations with a capacitance sensor and cell culture chamber to observe the capacitance response during single-cell proliferation at the cellular scale. The changes in capacitance were proportional to the levels of epidermal growth factor receptors (EGFR) on the cells when examining cells with different levels of EGF expression. The biosensor developed in this study not only enables the rapid manipulation of single cells to desired locations in a non-invasive manner but also allows for the continuous measurement of real-time interactions of individual cells during culture with external ligands, enabling the discrimination of specific responses between cancer and normal cells.

The pursuit of highly sensitive yet cost-effective technologies has intensified in the realm of optical sensors. This paper presents an innovative approach to refractive index measurement by employing a polished perfluorinated polymer optical fiber sensor. Leveraging the distinct optical properties of speckle patterns formed on the fiber's endface, this sensor accurately discerns variations in refractive index within its surrounding media. The specific material properties of the fiber render it particularly suited for liquid analysis. The proposed sensors were successfully evaluated for refractive index values up to 1.511, developing robust calibration curves in the process. This research introduces a promising avenue for precise and efficient refractive index measurements, demonstrating the potential of speckle pattern-based perfluorinated polymer optical fiber sensors in diverse applications requiring meticulous liquid analysis.

This work aims to develop polymer-based optical micro-resonator sensors, operating in the visible range and sensitive for homogeneous in-situ detection of pollutants in aqueous medium. This paper demonstrates that using a porous silica cladding (ns = 1.2) enhances significantly the interaction of the evanescent field with the analytes by modifying the propagation properties of the guided optical mode. The results improve sensitivity without complicating the design and avoiding surface chemical functionalization classically used for such application. Detection experiments based on real part refractive index change in the visible range have been conducted using different glucose concentrations. A sensitivity at the state-of-the-art of 255 ± 12 nm/RIU has been achieved at 760 nm for micro-resonator polymer waveguides on porous silica. These promising results enable the use of our devices in sensors to detect both real and imaginary parts of the analyzed medium refractive index, as well as analysis of complex environments.

This work presents the development and application of high-birefringent optical fiber sensors for the internal monitoring of Li-ion pouch cells during their operation. The instrumented optical fiber sensing configuration simultaneously discriminates and tracks temperature and strain shifts at different and consecutive cycling operation conditions which includes a Worldwide Harmonised Light Vehicle Test Procedure. A correlation of the strain parameter from the optical fiber sensors data signals with the Differential Voltage Analysis (DVA) is performed. This study is integrated into the INSTABAT EU-project, and it demonstrates the potential of this advanced technology based on optical fiber in the field of Li-ion cells monitoring, making a substantial contribution to the development of more sustainable energy solutions by improving their safety issues and evaluating their performance in real-time.

We demonstrate an method based on a unique sinusoidally-shaped phased grating for efficient and nearly alignment free detection of both signs and the modulus of the orbital angular momentum (OAM) of light. The OAM detection efficiency is almost same over the whole grating area. The capability and robustness of this method is demonstrated by detection of the optical vortices with OAM topological charge up to 150 using a reflective phase-only liquid crystal on silicon spatial light modulator (SLM).

The research focuses on metal hydride-coated Tilted Fiber Bragg Grating (TFBG) sensors for hydrogen detection. To overcome hysteresis issues linked to traditional sensing materials like palladium, tantalum (Ta) is introduced as an innovative alternative. The study details the establishment of optical constants for these novel materials via ellipsometry, involving the deposition of a nanometer-scale metal stack on a glass substrate. This thin film stack serves as the adhesion, sensing, and capping layer, enabling the selective absorption and desorption of hydrogen molecules, resulting in discernible changes in optical properties. The research includes comprehensive optical constant data, such as the complex refractive index, derived from ellipsometry measurements. Furthermore, the study incorporates these optical constants into a numerical model, examining mode propagation in TFBG sensors coated with Ta-based hydrogen-sensing materials. It explores mode coupling phenomena, characterizes transverse modes, and delves into key parameters, including electric field profiles, mode field diameters, and grating parameters essential for optimizing sensor performance.

This paper introduces Visible Light Communication (VLC) as an integrated approach to improving traffic signal efficiency and vehicle trajectory management at urban intersections. By combining VLC localization services with learning-based traffic signal control, a multi-intersection traffic control system is proposed. VLC utilizes light communication between connected vehicles and infrastructure, enabling joint transmission and data collection via mobile optical receivers. Atmospheric conditions affecting communication quality are considered, with an analysis of outdoor coverage maps. The system aims to reduce waiting times for pedestrians and vehicles while enhancing overall traffic safety. Flexible and adaptive, it accommodates diverse traffic movements during multiple signal phases. Cooperative mechanisms, transmission ranges, and queue/response interactions balance traffic flow between intersections, improving road network performance. Evaluated using the SUMO urban mobility simulator, the multi-intersection scenario demonstrates reduced waiting and travel times for both vehicles and pedestrians.

An optical fiber sensor with a single mode-coreless-single mode (SCS) structure is developed to detect volatile organic compound (VOC) biomarkers associated with diabetes. Zinc oxide (ZnO) is introduced as a sensing layer coated on the coreless fiber (CSF) segment via cost-effective airbrush to absorb the VOC biomarkers utilizing evanescent sensing mechanism. The study investigates VOC biomarkers, which are acetone, isopropanol, and ethanol, between 100-500 ppm vapor concentrations. The proposed sensor display highest sensitivity and selectivity to acetone vapor (sensitivity of 0.0028 nm/ppm) with morphology of ZnO layer playing a significant role. The achieved results offer the potential for real-time and non-invasive monitoring for diabetes management and diagnosis.

C-reactive protein (CRP) is a protein made by the liver in response to inflammation anywhere in the body. Early and accurate detection of CRP levels is essential for diagnosing various diseases. The proposed method uses a spectroscopy to analyze urine samples and machine learning to classify them as infected or non-infected based on CRP levels. Three machine learning models were employed: Extra Trees, Random Forest, XGBoost, K-Nearest Neighbors and Decision Tree. These models aimed to classify urine samples into two categories: infected (CRP level above 10−4 μg/mL) and non-infected (CRP level below or equal 10−4 μg/mL). The accuracy of the best model, Extra Trees is up to 68%. This method has the potential for faster and more user-friendly CRP detection compared to traditional methods.

Sepsis and cancer are some of the causes of morbidity and mortality in hospitals. Prompt detection and administration of the appropriate drug targeting the correct causative agent increases the chance of patient survival. The study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis and urinary changes suggestive of bladder cancer. The method comprises spectra of spectroscopy measurement of patients' urine samples with: urinary tract infection, urosepsis and bladder cancer. To provide reliable classification of results assistance of 27 algorithms were tested. We proved that is possible to obtain up to 95% accuracy of the measurement method with the use of machine learning. The method was validated on urine samples from 93 patients. The advantages of the proposed solution are the simplicity of the sensor, mobility, versatility, and low cost of the test.

This article deals with the issue of fire safety monitoring of wooden buildings. The aim of this paper is to describe the possibilities of monitoring temperature changes during a simulated fire using optical fiber and a distributed temperature measurement system (DTS). The DTS system uses the principle of stimulated Raman scattering, which allows longitudinal temperature measurements with a spatial resolution of 1 m. The increase in spatial resolution was achieved by winding the optical fiber into 5 cm diameter rings, with a fiber length of 3 m in each ring. The measuring rings of optical fiber were attached to fabric arranged in a rectangular grid. This fabric with the rings was then placed at the surveyed locations in the construction panels. During the temperature loading of the test samples with the gas torch, temperature monitoring was carried out both on the reverse side of the samples and in the inner layers. The results showed that this system with conventional multimode optical fibers can measure temperatures ranging from 20 °C to approximately 500 °C.

We report on the realization of novel instruments for absolute vapor pressure measurements: (i) a novel vacuum system in which the saturation vapor pressure of low-volatile substances can be measured directly and accurately, (ii) optomechanical “membrane sandwich” squeeze film pressure sensors with enhanced pressure response and pressure sensitivities on a par with those of the best commercial capacitive diaphragm sensors.

One key challenge in the bioreactor sector is the need for continuously monitoring the products at hand, as the already established techniques are time-consuming and proven to be costly. In this work, we will demonstrate, the development of a Raman library for the monitoring of nutrients in bioreactor samples, based on a portable MarqMetrix All-in-One Raman system, equipped with a 785 nm solid state laser, with spectral resolution averages at 6.5 cm-1 across a spectral band range from 100 to 3200 cm-1. The Raman measurements take place with the use of a Raman Probe equipped with a sapphire spherical lens which contact the surface of the liquid of interest enabling the in situ testing. A complete study will be presented for multiple prototype samples of increasing complexity and real samples provided by bioreactors for the production of Hydrogen and mammalian cells bioreactors. The outcome of this study will be later on tested with Raman on chip sensors developed within an EU project named LIBRA towards the developmen of a benchtop smart multisensing system for the in-line automatable screening of cultivation processes in bioreactors with the use of PIC sensors.

This publication aims to describe the design, implementation, and verification of a fiber-optic wrist sensor based on the Bragg grating (FBG) for monitoring the heart rate (HR) of the human body. One of our primary goals was also to create a sensor suitable for measurements in harsh environments such as magnetic resonance imaging (MRI). For this reason, we used polylactic acid (PLA) material for the encapsulation of Bragg grating itself. This combination results in a sensor solution that is resistant to electromagnetic interference (EMI). Based on their previous agreement, the sensor has been properly and systematically tested based on a group of six volunteers of one sex. The acquired data were then processed and evaluated, using the absolute error (AE), relative error (RE), and its mean. The outcome of this study indicated an RE of 9.07%, implying promising results and the first step of this study.

Here we investigate the improvement of the precision of measurement means to determine if it is possible to have sufficient sensitivity for detecting effects of elementary particles which would be characteristic of dark matter. A particle has been proposed and is called axion. There would be an interaction between the axions and the photons using the Primakoff effect under strong magnetic field. Radio frequencies in the range of 460 – 810 MHz could be assumed to be enough suitable for the mass of the axion, in case it could exist. It is then interesting to focus on the piezoaxionic effect. If the frequency of the axions could match the natural frequency of a normal mode bulk acoustic of a piezoelectric crystal, one would expect the piezoaxionic effect to occur. One could then rely on the piezoelectric effect to observe the variations on the resonant frequency which can be read out electrically using the best piezoelectric materials such as quartz. Through this example of develop-ment and applications in detection, we propose to decrypt this subject and to show how multidisci-plinary skills are necessary to hope that small fluctuations can be detectable.

In this study, we demonstrate a multicore fiber interferometer applied for insulin biosensing. This interferometer was produced by splicing a segment of a multicore fiber (MCF) fused to a standard single-mode fiber (SMF). Our investigation depicted that the interferometer's response to changes in refractive index (RI) was influenced by polarization when a thin gold layer was deposited at the end facet of the multicore fiber (MCF). The most significant sensitivity to RI was achieved when using P-polarized light with a ~10 nm-thick gold-coated sample. This gold-coated interferometer was subsequently functionalized for biosensing purposes and employed in the detection of insulin, successfully identifying insulin at a concentration level of 10−8 g/ml.

In this work, we introduce a new concept for an in-line optical fiber temperature and force sensor, which are based on two cascaded Fabry-Perot cavities and the Vernier effect. The sensing cavity is composed of the UV glue, while the reference cavity is composed of the silica fiber. Due to the high thermo-optic coefficient and low Young's modulus of the UV glue in the sensing cavity, this sensor can be used for temperature and force measurements. The obtained temperature and force sensitivity are 8.0 nm/℃ and ‒5.44e4 nm/N, respectively.

We present numerical modeling results of plasmonic sensor chips employed for highly sensitive bulk sensing and localized sensing, with high tunability of the wavelengths of operation from the visible wavelengths to the infrared wavelengths. We investigate periodic arrays of plasmonic nanostructures present on a continuous thin film such that these plasmonic nanostructures have a combination of surface plasmon polaritons (SPPs) propagating on the surface of the nanostructures and localized surface plasmons (LSPs) in the vicinity of the nanostructures. In order to measure the sensitivity of these plasmonic sensors, we determine the shifts in the reflectivity minima are determined to obtain information on perturbations in localized (local binding analyte layer sensing) and bulk refractive index (bulk medium sensing) on the sensor surface.

Measuring flour quality is essential for achieving consistent and high-quality results in baking and food production. It enables bakers and food manufacturers to make informed decisions about ingredient selection, adjust recipes for optimal performance, and meet the expectations of consumers. The advantage of spectroscopy in flour quality assessment lies in its non-destructive nature, speed, and the ability to analyze multiple components simultaneously without any sample preparation. We examined a collection of 40 commercially available flours of various types, including cereals, legumes, tubers, and others. As a result, their nutraceutical composition varied. For spectroscopic measurements in the 850-1700 nm range, we utilized the SpectraPodTM pocket-size spectral sensor by Mantispectra. Subsequently, we used chemometrics to combine the measured spectra with the nutraceutic data available on the flour packaging. The Partial Least Square method was used to develop predictive models for quantifying carbohydrates and proteins. The calibration and validation results are presented in this paper.

Within the "MiniSpectral" project at Jade University of Applied Sciences in Wilhelmshaven, a polymer-based miniaturized spectrometer for the optical wavelength range is being developed. The design features a dome-shaped optic coupled with a concave diffraction grating, serving as a complete spectrometer without the need for additional lens systems. Zemax simulations determine optimal dome and grating sizes. The polymer dome aims for a 10mm diameter, while the diffraction grating targets up to 1000 lines/mm density for sub-1nm resolution. The basic form as well as the diffraction grating were simulated using Zemax. Here, an initial comparison of simulation results with measurements on initial demonstrators will be presented.

The existing literature has identified distinct volatile organic compound (VOC) profiles in organic samples, correlating them with various disease conditions. 2-propanol has been implicated in different diseases across a range of organic samples, including type 2 diabetes, malignant biliary strictures, and various types of cancers. These findings underscore the potential utility of 2-propanol as a biomarker for disease detection in diverse biological matrices. In this study, we present a sensor that integrates a multimode interference structure with a molecularly imprinted polymer (MIP). Our results demonstrate that the developed MIP exhibits specificity for 2-propanol in the gas phase, contrasting with the corresponding NIP. This outcome highlights the potential of our proposed sensor design for the selective detection of 2-propanol in gas phase analyses.

Annealings have been performed on Ge0.84Sn0.16 microstructures in order to improve their optical properties by reducing the number of defects at the origin of Sn segregation. An enhancement of the photoluminescence intensity by a factor of 2.4 for annealed microstructures compared to ones without annealing was for instance achieved. Different annealing temperatures were tested to limit Sn segregation above the epitaxy temperature.

In this paper, we present our initial attempts to determine concentrations of nitrates, nitrites, and phosphates using spectroscopic methods as an alternative to currently employed laboratory techniques. The developed measurement techniques, based on photonic technologies, will be applicable in monitoring systems for assessing the quality of water resources in rivers, lakes, and reservoirs and in monitoring the quality of wastewater in municipal and industrial treatment plants. Current monitoring methods utilize various mechanisms for detecting and measuring the concentration of hazardous substances, such as chemical reactions involving the substances under investigation or methods that permanently alter the samples subjected to analysis (flame atomic absorption spectroscopy). The spectroscopic methods will solely rely on the non-destructive interaction of electromagnetic radiation with the substances under investigation, allowing for the maximum reduction of the method's impact on the analyzed sample. This work has received support from the National Centre for Research and Development through project FoSMoWater ( HYDROSTRATEG1/000E/2022).

We introduce a novel approach to sensing using coupled fiber cavities within the non-linear broken PT-symmetric region near the exceptional point. Employing a unique sensing metric, full-width-half-maximum (FWHM) change, the research explores the impact of induced loss on one subcavity due to refractive index changes. Unlike previous proposals, this design maintains constant coupling strength while introducing perturbations through cavity loss. The fiber Fabry-Perot sensor exhibits a square-root dependence of FWHM on varying refractive index-induced cavity loss. Comparative analyses showcase superior sensitivity in the proposed PT-symmetric sensor, promising breakthroughs in ultrasensitive sensing for applications in diverse sectors.

We propose a method that allows to perform a road markings vectorization from dense Mobile Laser Scanning (MLS) point clouds in an urban road context. The objectives are to automatically detect, locate and synthetize road markings using exclusively a raw dense MLS point cloud. Preliminary results are very promising, on test datasets. This work is part of a global approach aiming to automatically vectorize urban road point clouds to automate the production of topographic maps generation and furthermore to carry out road assets inventory.

Hollow-core fibers (HCF) are increasingly being studied and evaluated for telecommunication, as well as sensing application. They seem to have many advantages compare to standard single-mode fibers (SMF), especially in the area of the cyber-security of optical fiber infrastructures and also information they carrying. In our research we mainly focus on cyber-security issues, especially acoustic sensing. In the paper we evaluate sensitivity of negative curve HCF using a Mach--Zehnder interferometry (MZI). Results are compared with SMF (G.657 in this case). Both fibers were in primary coating with similar length. Sensitivity measurements were conducted within the controlled environment of an anechoic chamber. Results show that both fibers are sensitive to acoustic vibration and with post-processing method based on de-noising of the measured signal, the sensitivity can be improved. The clarity of the captured speech signal was assessed using the Speech Transmission Index for Public Address (STIPA).

Bearings are omnipresent in mechatronic systems such as motors and drive trains, as they are the interface between rolling and stationary components. Bearings in critical machinery are continuously monitored to detect faults in an early stage and avoid system damage and downtime. This condition monitoring is typically done with electrical accelerometers or microphone arrays. Optical fiber Bragg grating-based (FBG) sensors are ideal candidates to overcome the shortcomings of their electronic counterparts as they are thin and lightweight, immune to EMI and crosstalk, and have multiplexing capabilities. This implies that a multitude of sensors can be implemented directly on the bearing. Here, we report on the quantitative investigation of the repeatability and reliability of FBG-based bearing fault detection. We document the influence of the installation of the fiber on the bearing on the acquired strain signals, and the impact thereof on the selection of an adequate damage indicator. To the best of our knowledge, this is the first time that such an extensive study has been carried out in view of quantifying the repeatability and reliability of FBG-based fault detection in bearings.

In this work, we propose an optical fiber sensor based on a silica capillary section spliced between two sections of single mode fiber. One of the fusion splices is done with a transversal offset, to enable the creation of a Mach-Zehnder interferometer (MZI). Furthermore, the structure, monitored in transmission, also exhibits anti-resonant behavior. This effect is inhibited by submerging the sensor in liquid media, and the MZI becomes dominant. The sensor with a length of 3.75 mm presented a linear response to temperature with a sensitivity of 26.0 pm/ºC. Combining this sensor with another with a length of 3.82 mm, in a parallel configuration where both sensors are placed under water, an interferometric pattern with a higher frequency modulated by a lower one is attained, which is produced by the Vernier effect. The sensor, although insensitive to refractive index variations, showed a maximum temperature sensitivity of 2.193 nm/ºC, showcasing a magnification factor of 84.3. This sensor can find be employed in different fields where an accurate measurement of temperature in liquid media is required, such as chemistry, pharmaceutical, and biological applications.

A growing human population and changing climatic conditions have made food safety a pressing issue to our society. While conventional methods for their analysis are accurate, sensitive, and selective, they are costly, time-consuming and require skilled technicians. Optical fibre sensors have emerged as a versatile alternative for the detection of these compounds with advantages over traditional methods. In this work, we propose a high-sensitivity multimode fibre sensor for RI measurement. Our sensor consists of a section of capillary tube spliced between two sections of SMF, and was characterised in regard to its response to variations in RI in a range between 1.3329 and 1.3459 RIU. The resulting spectrum presented several frequencies and the measurements performed at 1558 nm provided a sensitivity of 423.6 nm/RIU, with a resolution of 9.71 x 10‑5 RIU. Further analysis of the spectrum using a low pass filter with a cut off frequency of 0.06 nm-1 provided a maximum sensitivity of 1459.4 nm/RIU, with a resolution as low as 3.33 x 10-4 RIU.

As advanced technologies and automated systems become increasingly prevalent, the demand for precise target classification within high-throughput processes is growing. These methods must swiftly and accurately analyze a large volume of objects. This study introduces a high-throughput screening platform designed to identify moving targets through optical line scanning. A single-pixel detector is employed to capture the object's distinctive attributes, while reservoir computing is leveraged for target classification. The experimental results affirm the efficacy of the proposed method in accurately classifying objects. This approach holds promise for high-throughput detection and has the potential for applications in the detection of cell morphological features.

The production of Particleboard (PB) uses recycled wood, addressing the challenge of potential contaminants in wood wastes, which can jeopardize safety standards. The research explores the application of Laser-induced breakdown spectroscopy (LIBS) for rapid detection of heavy metals in wood samples. Ten wood samples were analyzed, revealing the presence of contaminants such as Cr and Ti, in 30% of the sample set. The LIBS technique proved to be a powerful methodogy for decision-making purposes.

To meet the increasingly miniaturized and precise monitoring demands, this paper proposed a compact Fiber Bragg Grating (FBG) accelerometer based on a flexible spring. The designed FBG accelerometer retains the compact structure and good lateral anti-interference capability observed in diaphragm type FBG accelerometers, while significantly enhancing the sensitivity. Firstly, the 3D model and the dynamic model of the accelerometer was designed and constructed. Subsequently, theoretical values for accelerometer sensitivity and resonant frequency were derived. Then, finite element modeling (FEM) was employed to investigate the measurement performance of the accelerometer. Finally, a vibration calibration platform was established to determine the actual operational performance of the accelerometer through a series of acceleration excitation and frequency sweep experiments.

Coffee beans exhibit a diverse range of imperfections, including shell beans, cracked beans, fully or partially black beans, withered beans, and those affected by mold damage. Currently, most coffee farmers rely on human visual inspection to sort these beans, but this method becomes less effective over time due to visual fatigue. To address this issue, this study employs a snapshot hyperspectral camera to capture hyperspectral images, which are then used for classification through deep learning techniques. Specifically, 1D-CNN, 2D-CNN, and 3D-CNN models are employed and compared using confusion matrices and kappa values against existing literature. Ultimately, this approach is integrated with a defect detection system to real-time hyperspectral roasted coffee beans defect detection machine that offers both timeliness and accuracy.

Two-dimensional metal organic frameworks (MOFs) demonstrate the advantages of organo-inorganic nature such as operation efficiency, and stability for optical sensing. For achieving high selectivity and operational performance optical properties of MOFs nanosheets can be varied by choosing the metal centers and the linking ligands. According to 2D nature, the material could be exfoliated up to monolayer height, which drastically increases the effective surface area of the sensor. The existing challenge is to preserve the lateral size of the membrane while reducing the height of the material. In our work, we have overcome the challenge and obtained high-quality 2D MOF layers with a record aspect ratio (21333:1) and demonstrated unique optical sensitivity to solvents of varied polarity. Our method of membrane fabrication opens the way to produce scalable and freestanding 2D MOF-based atomically thin chemo-optical sensors by an industry-oriented approach.

At present, early diagnosis and treatment of cancers through biomarker detection in body fluids represents one of the greatest challenges in biomedical research given its health, social and economic impact. The project “Fiber optics sensors as a platform for cancer diagnosis and in vitro model testing” (FOCAL) aims at developing a reliable, compact and operando monitoring biophotonic platform based on fiber optical sensors for non-invasive early diagnosis of cancer biomarkers and to monitor live cancer cell activity. First, we want to develop a multiparameter fiber-based biophotonic platform with optimized optical thin film made of metal oxide to reach improved sensitivity, sub-fM limit of detection, clinically-relevant concentration and working range for the detection of growth factor cancer-related biomarkers. Second, we want to combine the biophotonic platform with novel functional nanomaterials, such as electrospun 3D scaffolds, and then detect cancer biomarkers released into biological fluids by specific cell lines.

In this paper we investigate the use of photopolymer material as the sensor medium. This research focuses on the creation of self-written waveguides with the photopolymer and the interaction with the outside environment. A master SWW is created within the PVA/AA material, which is permanent. Under external environmental forces where movement is recorded new SWWs are created measured and recorded within the photopolymer on a 3D plane. These newly created SWWs, from numerical modelling, represent the interaction with the surrounding environment. The changes in SWW propagation are used to measure force and direction of movement.

Single Walled Carbon Nanotubes (SWCNTs) enable sensing of biomolecules such as histamine, dopamine and adrenaline. Their interaction with the analyte changes the samples fluorescence, which allows their microscopic and spectroscopic detection. We present different approaches from our latest work in this area, including multiplexed NIR imaging and lifetime measurement by time-correlated single photon counting in order to determine analyte concentrations as well as single molecule detection.

There has been a growing interest in the MEMS sensors manufactured by microelectronic technology because of their small size, low power consumption, and low cost. MEMS resonance sensors have attracted much attention due to their superior performance, whose sample current almost has no thermal effect to sensing structure, signal-to-noise ratio and sensitivity are higher, and frequency output signals are readily adapted for digital processing. The performance of a MEMS sensor is not only related to its sensing material and structure and its manufacturing processes, but also associated with the measurement method. In this paper, the research on frequency measurement for a designed bi-material resonator which is used to detect infrared (IR) radiation by means of tracking the change in resonance frequency of the resonator with temperature attributed to the IR radiation from targets is presented. The research provides the basis for the integrated design of the uncooled infrared MEMS resonance structure and its measuring circuits.

Landslides may occur due to geomorphological processes, which can lead to changes in the physical, hydrogeological, and mechanical properties of the materials involved over time. Several alternative non-invasive techniques are available for detecting changes in these attributes, which complicates the selection of the most accurate, applicable, extensive, logistically feasible, timely, cost-effective, and integrable technology. Researchers conducted a study on landslides in four different locations utilizing TLS (Terrestrial Laser Scanning), UAV (Unmanned Aerial Vehicle), and SAR (Synthetic Aperture Radar)-based photogrammetry to provide more evidence for this argument. In order to make progress in this field, drones will need the installation of more multispectral and sophisticated sensors. The data from these sensors will be combined with geophysical and meteorological data to enable AI-driven landslip prediction. In order to identify and track the changing patterns of surface deformations in the Himalaya, it is necessary to explore the capabilities of two-pass differential InSAR and advanced DInSAR methods such as Small Baseline Subset (SBAS) and Persistent Scatterers interferome

3D laser nanoprinting based on multi-photon absorption (or multi-step absorption) has become an established commercially available and widespread technology. Here, we focus on recent progress concerning increasing print speed, improving the accessible spatial resolution beyond the diffraction limit, increasing the palette of available materials, and reducing instrument cost.

Biological discovery is a driving force of biomedical progress. With rapidly advancing technology to collect and analyze information from cells and tissues, we generate biomedical knowledge at rates never before attainable to science. Nevertheless, conversion of this knowledge to patient benefits remains a slow process. To accelerate the process of reaching solutions for healthcare, it would be important to complement this culture of discovery with a culture of problem-solving in healthcare. The talk focuses on recent progress with optical and optoacoustic technologies, as well as computational methods, which open new paths for solutions in biology and medicine. Particular attention is given on the use of these technologies for early detection and monitoring of disease evolution. The talk further shows new classes of imaging systems and sensors for assessing biochemical and pathophysiological parameters of systemic diseases, complement knowledge from –omic analytics and drive integrated solutions for improving healthcare.

This paper presents a method for supporting wayfinding in crowded buildings using Visible Light Communication (VLC). Luminaires are repurposed to transmit encoded messages, providing location-based information to users. Tetra chromatic LEDs and OOK modulation efficiently transmit data, while error detection techniques ensure reliable transmission. Users carry receivers that interpret the light signals and perform localization calculations. Wayfinding algorithms guide users with turn-by-turn directions, landmarks, and alerts. The system integrates VLC into an edge/fog architecture, utilizing existing lighting infrastructure for efficient data processing and communication. It enables indoor navigation without GPS, demonstrating self-localization and optimizing routes. This method enhances accessibility and convenience in unfamiliar buildings

This paper analyses the mobility of Autonomous Guided Vehicles (AGV) in dense industrial environments. VLC technology is exploited to ensure data transmission among the infrastructures and the vehicles, providing X2X links. The VLC technology uses tetrachromatic qhite LEDs for simultaneous lighting and data transmission and dedicated pinpin photodiodes as receivers. Indoors positioning based on VLC methodology provides necessary information to enable guidance services. Coding schemes are designed for each VLC link. The regulation of the AGVs flow along the lanes inside the warehouse is analyzed as the urban traffic flow using a SUMO urban mobility simulator. This tool is used to generate data related to the AGV movement, and a reinforcement learning scheme, combining agent-based modeling and VLC queuing/request/response behaviors, effectively schedules routes. This provides efficient travel and avoids crowded regions. The demonstration of this proof-of-concept is supported on the evaluation of travel time and traffic flows.

Mapping the environment is the basis for measurement tasks, planning processes, monitoring over time, and decision-making. Moreover, the inspection of engineering structures, roads and railways, water and wastewater, and energy infrastructure is essential to ensure safety and sustainability. In this study, we present various concepts of autonomous measurement robotics for mapping and inspecting challenging environments and damage types. After presenting the state-of-the-art methods, we present ways for an autonomous operation of a robotic system equipped with specific sensors for LiDAR-based subsurface damage detection of cavities or delaminations, underwater laser scanning, high-resolution bathymetry, and multispectral LiDAR for moisture detection. In conclusion, this work gives an overview of existing methods and systems and their limitations for mapping and inspection tasks. Moreover, we show examples of various scenarios of how advanced sensor systems can be used as part of autonomous robotic systems to overcome current limitations for high-quality mapping and inspection tasks.

Robots typically face a common problem when trying to work hand in hand: in many applications they are not accurate enough in combination. Therefore we want to highlight our approach to introduce precise adjust procedures in a robotics cell. They are based on tailored optimization algorithms to provide an accurate and absolute coordinate system. Because the final accuracy is limited by surrounding conditions, we additionally present a detailed analysis. As an intermediate result of our ongoing research we are confident to achieve a repeatable accuracy of less than 300 microns.

To create a digital twin of a city, all relevant objects, within the city, must be mapped and labelled accordingly. In order to cover the entire city, vehicles and aircraft are used for the mapping task. While airborne mapping can cover areas that are impossible for a vehicle to reach, terrestrial mapping provides a different perspective, containing further necessary information about the city. The two datasets are registered using reference points, fused together, and processed so that semantic segmentation can be performed by a neural network. The segmented point cloud can then be exported to create a 3D digital twin of the city. This work presents a process for the construction of a 3D digital city model from airborne and terrestrial mapping data.

In this study, we present an examination of the processes of heat propagation and the determination of noise in a miniature, three-layer thermoelectric detection pixel. The investigation included the absorption of single photons with energies ranging from 0.8 to 7.1 eV in absorbers of varying thicknesses to ensure high absorption efficiency. We examined temporal temperature dependencies in various regions of the detection pixel and evaluated the gradient of the average temperature on the sensor boundaries. The analysis also involved determining the signal power resulting from photon absorption, the equivalent noise power, and the signal-to-noise ratio. The findings demonstrated that a detection element equipped with either a W or Mo absorber, a Mo heat sink, with surface dimensions of 1 μm × 1 μm, and nanometer-scale layer thicknesses, can reliably detect single photons with energies ranging from 1.65 eV to 7.1 eV.

Ultrafast X-ray spectroscopy has emerged as a revolutionary tool in the field of chemical dynamics, offering immense potential for further exploration. Despite significant progress in this area, the complete realization of its capabilities remains a challenge. One of the key hurdles in X-ray spectroscopy is the precise measurement of minute changes in absorption near the K-edges of the atomic elements, demanding high signal-to-noise ratios. To harness the potential of such experiments with low photon-flux tabletop sources, it is essential to develop schemes with high detection efficiency in the soft X-ray spectral range and rapid readout for "pump-on" and "pump-off" measurements. In this context, greateyes GmbH is actively involved in the development of X-ray cameras tailored for time-resolved X-ray spectroscopy, incorporating innovative CMOS-based detectors. The project's primary objective is to enhance an existing CMOS camera platform to create an EUV/soft X-ray-sensitive sCMOS camera suitable for high-repetition-rate imaging, spectroscopy, single photon detection, and its application in the study of molecules in solutions via absorption spectroscopy in the soft X-ray range.

High gain, low excess noise, and high responsivity planar InAs electron avalanche photodiodes are reported. Operating at low temperatures and combined with a low noise current amplifier, the detectors are demonstrated to detect very low optical powers corresponding to less than 70 photons per 1550nm laser pulse.

There is an increased demand for low noise avalanche photodiodes (APDs) for infrared wavelengths at 1550nm for long range Light Detection and Ranging applications. Here we present two class of APDs that produce high avalanche gain but with extremely low excess noise factors ~ 2. InAs APDs show F < 2 and offer detection wavelength up to 3500nm, although this drops to ~3000nm when cooled. For reducing effects of scattering in atmosphere, InAs could be an attractive option. In addition InAs APDs are based on a simple homojunction design, which is relatively easy to grow epitaxially. AlGaAsSb when combined with InGaAs, provides a direct replacement for the traditional InGaAs/InP APDs. It is therefore capable of room temperature performance with excess noise performance similar to Si APDs but operates at 1550nm. We will present results that show noise equivalent power as low as 69fW/(Hz)^0.5.

Single-photon avalanche diode (SPAD) sensors are versatile candidates for applications in low-light imaging and scenarios where high temporal resolution is crucial, like quantum imaging, fluorescence lifetime imaging, and (direct) time-of-flight methods. We demonstrate the improvement in light sensitivity by a factor of 7× for LiDAR (Light Detection and Ranging) by molding application-specific filling factor enhancing microlenses directly onto backside-illuminated SPADs. An 8”-wafer-level process is presented utilizing a mask aligner device for selective UV-curing of highly transparent polymer lenslets only in areas where SPADs are located and rinse uncured material from areas being compatible with postprocessing steps like chip dicing and electrical bonding. In addition to the optical benefits of chip-integrated lenslets, advantages arise from less system integration efforts of separately realized microlenses, especially with respect to tolerance conditions.

This paper presents a novel approach to 3D mapping of transparent biological tissues, overcoming the limitations of traditional optical metrology. We introduce a depth-from-focus method enhanced by two key innovations: a ring LED illumination system for improved image clarity and a photographic lens with a larger sensor to expand the field of view. These advancements, coupled with a new focus estimation algorithm, enable precise, high-resolution 3D imaging of biological specimens, surpassing the constraints of existing techniques.

In the presented work, the effects of IR radiations on a thermal imager are experimentally investigated. The IR radiations originate from a nanosecond time scale, high energy, 10 Hz OPO at the wavelength of 3550 nm. The assessment of dazzled IR sensor images is performed using a code that computes the overexposed pixels whether for one single frame or for a whole dazzling sequence. The structural similarity index function (SSIM) is also used together with the Canny edge detection technique. At fixed integration time, we observed that the dazzling effect is more pronounced at low OPO radiant power and the disturbance flattens and spreads more over the whole array (column) as the OPO radiant power increases. This clearly demonstrates that the camera enters a nonlinear regime when submitted to IR laser radiations. Increasing the camera’s integration time leads to results that are contradictory to the expectation.

After laser transmission through the atmosphere, the measurement of far-field parameters of facula plays an important role in accurately evaluating the atmospheric transmission characteristics of laser and the comprehensive performance of laser system. The facula size of the spot formed by the laser after a longer distance transmission can reach the meter level, which can not be measured by the traditional camera due to the limitations of the field of view and resolution. This article studies a far-field laser facula measurement system based on detector arrays. Research is conducted from three aspects: designing the acquisition circuit of the detection array, FPGA for data acquisition and transmission, and upper computer software data processing. The experiment shows that the designed system meets the expected requirements.

With the continuous development of space technology, human beings are increasingly competing for space resources. Especially in the military field, how to prevent the infiltration of enemy reconnaissance satellites is one of the core issues of space defense. In addition, space debris created by human exploration and development of space also seriously threatens the safety of satellite operation. To solve the above problems, this paper analyzes the infrared feature modeling of long-distance space targets, proposes a weak feature small target detection algorithm based on spatial filtering, and carries out experimental verification. Experimental results show that the proposed method has high reliability.

Indoor organic photovoltaic (OPV) cells, known for their thin, lightweight, and flexible attributes, hold great promise for powering smart devices, particularly wearables and implantable. Their cost-effective production methods make them ideal for large-scale manufacturing. OPV cells can revolutionize the medical field by offering a reliable and sustainable energy source for these devices. Achieving impressive power conversion efficiencies exceeding 27.8%, they outperform commercial silicon cells. Notably, they seamlessly integrate into wearable and implantable devices, ensuring patient comfort and safety. The use of organic materials in the active layer enables highly efficient, flexible, lightweight, biocompatible, and durable devices. This research uses PBDB-TCl:AITC:BTP-eC9 in the active layer for high indoor OPV efficiency and optimal voltage control for monitoring smart IoT health sensors. These OPV cells embedded with smart sensors facilitate the monitoring of various patient health parameters like blood pressure and body temperature.

Raster masks (binary, object-by-object, or energy fields) of areas of interest are formed by identifying certain informative features in images. To ensure transparency in presenting these features, raster data is vectorized to obtain information in a readable form. Vectorization involves primary and secondary transformations. For two-dimensional images, the immediate change consists of getting points, sections, and polygons from pixels and raster areas of the “substrate”, and the secondary transformation consists of a series of morphological operations on it. A mathematical model of labyrinths (two-dimensional rectilinear), mathematical characteristics of labyrinths and a model of generation of labyrinths with given characteristics are presented. The types of labyrinths from the point of view of satellite monitoring and some examples of artificial and natural origin are given. The “labyrinth effect” for pedestrians and transport is noted, which arises during the restructuring of the territory using remote sensing.

The approach to solving the problem of positioning unmanned transport objects, in particular, railway transport, in conditions of measurement interference uncertainty is considered. Today, a large number of urban (as well as railway) infrastructure facilities generate an unpredictable nature of disturbances acting on navigation sensors of unmanned objects. In this case, the use of both satellite measurements and various sensors located on the site (in particular, a video surveillance system) often becomes impossible. A stable high-precision solution to the problem of positioning unmanned transport objects becomes necessary. To solve this problem, the article proposes to use autonomous linear motion parameter meters as sensitive elements of the navigation complex. It is proposed to use a Kalman filter and a robust filtration method to process noisy measurements, including those with uncertain probability characteristics.