This PDF file contains the front matter associated with SPIE Proceedings Volume 11007 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

We studied high-performance ion-selective field-effect transistors (ISFETs) to detect human salivary stress-marker candidates using direct potentiometry. For acute stress monitoring, we investigated various kinds of sensor materials, designed nitrate-sensing materials, designed plasticizers and biocompatible polymers for salivary nitrate ISFETs. We prepared prototype of silicon based FET nitrate checker using specially ordered FET mV checker. The prototype of nitrate ISFETs showed almost the theoretical Nernst response with a response time of less than 10 seconds. As we applied whole human saliva using direct potentiometry, we obtained excellent relationship with conventional ionchromatography. We successfully demonstrated several acute stress tasks for healthy volunteers using wearable heart rate monitor and prototype of salivary nitrate checkers based on NO₃ -ISFETs. We also discussed high-performance organic transistor based FET (OFET) biosensors for stress monitoring using extended-gate type configuration towards stress monitoring.

In this paper, we report a biosensing method to analyze and monitor simply and easily biological phenomena such as DNA recognition events, antigen-antibody reaction, and cell functions in vitro by use of a semiconductor device. As the most important point, we focus on a direct detection of ions or ionized biomolecules with charges, because most biological phenomena are related to ionic behaviors such as sodium or potassium ions through ion channel at cell membrane resulted from cell-cell communications, and biomolecules such as DNA molecules have intrinsic molecular charges. That is, the principle of semiconductor-based biosensing device contributes to directly detect biomolecular charges. Thus, a platform based on the semiconductor-based biosensor is suitable for a label-free and noninvasive biosensing detection system in the field of in vitro diagnosis (IVD).

A nanostructure-based plasmonic biochip with the same size as standard 96-well plates for backside reflection-type biosensing was proposed and validated through analyses of biological interactions. The capped gold nanoslit arrays were fabricated on a polycarbonate plastic film using a rapid hot embossing nanoimprint lithography process. The optical properties of capped gold nanoslits with different structure parameters in backside reflection geometry were studied; their refractive index (bulk) and surface (thickness) sensitivities were verified. By changing the cavity length, the coupling between a broadband cavity resonance and a narrowband surface plasmon resonance mode results in an asymmetric Fano resonance in the reflection spectra. The coupling mode is able to enhance the thickness sensitivity by a factor of 2.4 with wavelength interrogation. The bulk and thickness sensitivities were 454 nm/RIU and 1.14 nm/nm, respectively. The protein-protein interaction experiments verified the sensing capabilities and high sensitivity of the capped nanostructures; a limit of detection (LOD) of 2 ng/mL IgA was achieved. Such a multi-well plate with backside reflection-type geometry, decoupling the optical paths, allows for sensing with opaque, bubbly or highly scattering liquids and benefits multiple sensing applications in the biotechnology and agricultural products.

The ability to monitor gene expression within living plants is of importance in many applications ranging from plant biology research to biofuel development; however, no method currently exists without requiring sample extraction. Herein, we report a multimodal imaging method based on plasmonic nanoprobes for in vivo imaging and biosensing of microRNA biotargets within whole plant leaves. This method integrates three different but complementary techniques: surfaceenhanced Raman scattering (SERS), X-ray fluorescence (XRF), and plasmonics-enhanced two-photon luminescence (TPL). The multimodal method utilizes plasmonic nanostars, which not only provide large Raman signal enhancement, but also allow for localization and quantification by XRF and plasmonics-enhanced TPL, owing to gold content and high two-photon luminescence cross-sections. For the sensing mechanism, inverse molecular sentinel (iMS) nanoprobes are used for SERS bioimaging of microRNA within Arabidopsis thaliana leaves to provide a dynamic SERS map of detected microRNA targets while also quantifying nanoprobe concentrations using XRF and TPL. This report lays the foundation for the use of plasmonic nanoprobes for in vivo functional imaging of nucleic acid biotargets in whole plants, a tool that will allow the study of these biotargets with previously unmet spatial and temporal resolution.

Dugway Proving Ground (DPG) is a U.S. Army test facility that conducts open-air field testing of systems used in defense against chemical and biological threats. The proving ground is geographically remote, covers 3,200 km² , and thus allow for large or energetic test events. The various test ranges are populated with a combination of standoff and point sensors that are used to benchmark the systems under test. Elastic-backscatter lidar systems are the primary active standoff referee system for aerosol releases. They provide detection, quantification, and location of aerosol plumes across the test grids. The majority of the lidar systems operate in the near infra-red at 1 or 1.5 μm with nominal ocular hazard distances (NOHD) that vary between 0 and 5.5 km. Lidar calibrations are conducted using the Active Standoff Chamber (ASC) and Joint Ambient Breeze Tunnel (JABT) test fixtures. The ASC is a large chamber with 3m apertures on both ends to allow standoff measurements coincident with point measurements collected inside. It confines the aerosol release via air curtains and is capable of maintaining a consistent aerosol concentration. The JABT is an openended tunnel with exhaust fans to draw released aerosols down its > 100 m length and allows the plume to actively develop in a manner similar to a field release. This mix of point sensors, lidars, and calibration and test facilities allow DPG to provide calibrated referee data for a variety of aerosol release test events.

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are a major water quality and public health issue in inland waters as they hamper recreational activities, degrade aquatic habitats, and potentially affect human health via toxic contamination. Despite the risks posed to the environment, human and animal health, currently, there is a lack of rapid monitoring program to periodically evaluate the spatial distribution of cyanobacteria in inland waters. This study integrated multiple clouds including community cloud (via social media data), sensor cloud (CyanoSense- wireless hyperspectral sensor) and computational cloud to design and implement techniques for early detection of CyanoHABs in inland waters. Social cloud data helped to identify the geographical locations frequently affected by CyanoHABs and CyanoSense helped in verifying those locations and retrieve concentrations. This integrated monitoring system would be very useful for lake resource management and state agencies by reducing their budget cost for rapid detection and frequent monitoring of CyanoHABs across inland waters.

A microsphere-fiber probe used for spectroscopic analysis of material samples is presented. The monolithic structure is formed by melting the end of a section of optical fiber forming a bead at the distal end of the fiber. Light guided through the fiber and emerges from the sphere focusing to a point beyond the surface. Raman scattering is used to demonstrate the efficacy of the probe, which operates in a bidirectional manner efficiently collecting the scattered light, re-imaging it back into the waveguide, and transmitting it to a spectrometer for dispersion. The probe demonstrates an order of magnitude improvement over the spatial resolution conventional fiber probes. This improvement in spatial resolution and corresponding collection efficiency will aid in critical analyses such as cancer margin detection and material characterization.

Gas-phase biosensors (Bio-sniffers, Sniff-cam) have been investigated not only for human volatiles (acetone, methyl mercaptan, trimethylamine, ethanol, isopropanol, etc.) but also for residential harmful VOCs (formaldehyde, toluene, nicotine) causing sick-house syndrome, etc. The biofluorometric bio-sniffers constructed with UV-LED and PMT shows good sensitivity and selectivity for continuous monitoring of target VOCs (formaldehyde, ethanol, acetaldehyde, acetone, isopropanol, etc.). The sniff-cam with enzyme immobilized mesh demonstrates a spatiotemporal gas-imaging for human volatiles (i.e. ethanol, acetaldehyde, etc. after drinking). As novel non-invasive biosensing approaches, the gas-phase biosensors for human and environmental VOCs will be introduced in this contribution. The bio-sniffer for acetone vapor was developed using S-ADH (secondary alcohol dehydrogenase) reverse reaction by detecting NADH fluorometric system. The S-ADH was possible to continuous measure gaseous acetone from less than 1 ppb to 20ppm with a good selectivity based on the enzyme specificity. The device allows to use the evaluation of the acetone concentration in exhaled air from healthy subjects and diabetes patients (type I and II). The novel biofluorometric sniff-cam for ethanol was also fabricated with ADH (alcohol dehydrogenase) immobilized mesh and the NADH visualization unit (UV-LED sheet array and high sensitive CCD), thus imaging human ethanol vapor not only exhaled air but also skin gas after drinking. The sniffer-device would be useful for conventional detecting the volatile biomarkers.

The modern farm is a technological marvel, from smart tractors to genetically modified organisms (GMOs), along with chemical pesticides and fertilizer. Farms today have continuously increased production by utilizing these various techniques. Many farms on the east coast of North America are growing dent or field corn while also rotating crops between soybeans of various types and winter wheat. These crops have become symbiotic in nature due to the need for specific soil nutrients of the crops and the practice of no till farming. More recently, schools with farm programs have started researching the use of drone technologies and multispectral analysis as a means to reduce chemical usage thereby saving farmers annual chemical costs. This paper investigates the use of drones in capstone projects for undergraduate engineering and computer science programs. Undergraduate capstone projects usually require a design and build element to satisfy ABET accreditation requirements. Therefore, the students needed to design and build an airframe capable of surveying farms with a multispectral camera. In the course of the aircraft design process it was discovered that the students needed to have a broader understanding of federal regulations, experimentation, and a robust understanding of how the drones and data would be used to benefit a typical farm. In addition, we look at the results obtained and discuss the problems associated with making the data and analysis accessible to the farmers who participated in our study. In the process we also discovered other potential uses for the images we created.

The Army is interested in sensors capable of characterizing/monitoring the environment (battlefield or military training ranges) at proximal distances. Recently, we evaluated laser induced breakdown spectroscopy (LIBS) systems (hand-held, proximal, and bench top) for the characterization of metals (antimony, copper, lead, tungsten, and zinc) in soils obtained from military training ranges. We then compared the results to findings obtained with standard field and laboratory instrumentation for metals analysis - X-ray Fluorescence (XRF) and Inductively Couple Plasma- Optical Emission Spectroscopy (ICP-OES).

Rapid and sensitive detection of hazardous chemicals is essential for worker safety in industrial environments and for soldier safety in the field. In this work, we propose a physical coloration platform to passively amplify existing colorimetric sensing mechanisms. We outline a design process which can be targeted to an arbitrary colorimetric indicator and target and fabricate as a demonstration a sensor which produces noticeable color change on reaction with ammonia in the vapor phase.

Vendor-supplied calibration curves for fixed-grating, fixed-detector-array portable spectrometers typically provide wavelength accuracies of about ±1 pixel on the array. Independent calibration using atomic lamp spectra is hampered by the sparsity of available lines: interpolation between atomic lines typically leads, again, to pixel-width errors or greater. We provide a technique that combines information from sparse atomic line spectra with densely populated peaks from the transmission spectrum of an air-spaced etalon to generate calibration curves capable of ~1/10th pixel accuracies across entire detector arrays. Subsequent transmission spectra through solid etalons of well-characterized glass samples validate the calibration procedure.

Early detection of molecular targets can greatly impact the clinical diagnosis and outcome of many diseases such as cancer. Methods utilizing optical detection systems and Surface-Enhanced Raman Scattering (SERS)-labeled nanoparticles provide a way of selectively targeting and obtaining signals unique to the target diseases as well as in-vivo applications for biomass and biofuel research with plants. However, these modalities are often limited to surface level detection due to attenuation from layers of highly scattering and absorbing tissue. In this work, we utilize surface-enhanced spatially offset Raman spectroscopy (SESORS) to probe through thick tissue to overcome this limitation. This modality combines high SERS signals generated by nanoparticles with a depth resolved detection technique called spatially offset Raman spectroscopy (SORS). We show the detection and recovery of SERS signal in layered systems comprising of optically mimicking gel as well as bone material.

Precise and functional phenotyping is a limiting factor for crop genetic improvement. However, because of its ease of application, imagery-based phenomics represents the next breakthrough for improving the rates of genetic gains in field crops. Currently, crop breeders lack the know-how and computational tools to include such traits in breeding pipelines. A fully automatic user-friendly data management together with a more powerful and accurate interpretation of results should increase the use of field high throughput phenotyping platforms (HTPPs) and, therefore, increasing the efficiency of crop genetic improvement to meet the needs of future generations. The aim of this study is to generate a methodology to high throughput phenotyping based on temporal multispectral imagery (MSI) collected from Unmanned Aerial Systems (UAS) in soybean crops. In this context, ‘Triple S’ (Statistical computing of Segmented Soybean multispectral imagery) is developed as an open-source software tool to statistically analyze the pixel values of soybean end-member and to compute canopy cover area, number and length of soybean rows from georeferenced multispectral images. During the growing season of 2017, a soybean experiment was carried out at the Agronomy Center for Research and Education (ACRE) in West-Lafayette (Indiana, USA). Periodic images were acquired by Parrot Sequoia Multispectral sensor on board senseFly eBee. The results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis of crop extension and affording a constant operational improvement and proactive management.

The QuiC SLED light emitting diode and its photodiode counterpart, the PDQ, use InAs-GaSb p-i-n superlattices for emission and detection, cascaded with tunnel junctions. Variable-period superlattices form the carrier injectors and separate-confinement heterostructures. The superlattices mitigate Auger recombination losses; weakly-coupled dielectric buttes enhance light extraction, and composite metal-mesh electrodes aid current spreading. The first example of the platform, emitting at 4.25 µm and aligned with carbon dioxide absorption in the MWIR band, is rated at 2 mW in pulse mode at room temperature. Emitting superlattices can cover approximately 3 µm to 12 µm wavelengths. Prominent sensing applications for this class of devices rely on short-pulse, low-duty cycle measurements to reduce system power requirements for long battery life. An important target is for the LEDs typically to consume less than a few microjoules per measurement pulse. Within this context, the optimal QuiC SLED-PDQ properties are defined by the needs of the product stack, which starts with these mid-IR devices and extends through the analog front end chips and signal processing chips, to the application algorithms and user interface.

Advancements in efficient unmanned aerial platforms and affordable sensors has led to renewed interest in remote sensing by agricultural producers and land managers for use as an efficient and convenient method of evaluating crop status and pest issues in their fields. For remote sensing to be employed as a viable and widespread tool for weed management, the accurate detection of distinct weed species must be possible through the use of analytical procedures on the resultant imagery. Additionally, the remote sensing platform and subsequent analysis must be capable of identifying these species across a wide range of heights. In 2017, a field study was performed to identify any weed height thresholds on the accurate detection and subsequent classification of three common broadleaf weed species in the southeastern United States: Palmer amaranth (Amaranthus palmeri), common ragweed (Ambrosia artemisiifolia) and sicklepod Senna obtusifolia) as well as the classification accuracy of image classifications performed on the species scale. Pots of the three species at heights of 5, 10, 15, and 30 cm were randomly arranged in a grid and 5-band multispectral imagery was collected at 15 m. Image analysis was used to identify the spectral reflectance behavior of the weed species and height combinations and to evaluate the accuracy of species based supervised classifications involving the three species. Supervised classification was able to discriminate between the three weed species with between 24-100% accuracy depending on height and species. Palmer amaranth classification accuracy was consistently 100%. Increased height of sicklepod and common ragweed plants did not reliably confer improved accuracy but the species were correctly identified with at least 24% and 60% accuracy, respectively.

Three-dimensional (3D) cultured cells are widely used for tissue engineering and drug screening. Therefore, a method for evaluating them is desired. Several kinds of tools have been developed. Among them, electrochemical approaches are receiving considerable attention because of their great features, such as simplicity and highly sensitive detection. Especially, electrode array devices have been developed for electrochemical imaging of cell activity. In he electrochemical imaging, cell activity is converted to current values at electrochemical sensors, and we obtain 2D images consisting of the currents, which is called as electrochemical images. The electrochemical images are useful for chemical mapping. We previously reported a CMOS-based amperometric device containing 400 sensors for electrochemical imaging. The device was applied for electrochemical imaging of several kinds of cell activity. In this study, the device was applied for evaluating intracellular activity using redox mediators. To access intracellular compounds, hydrophobic mediators were used because the mediators can penetrate cell membranes. First, ferrocenemethanol (FMA) was used as a hydrophobic mediator and oxidation currents of FMA were measured. However, the detection was unsuitable for evaluating intracellular enzymes and/or redox compounds. Next, a double-mediator system of [Fe(CN)₆]^3- and menadione was adapted for measuring intracellular sensing. The detection is deeply discussed in the oral presentation. Briefly, quinone oxidoreductase 1 (NQO1) activity was successfully detected. In the future, the detection system will be used for drug screening.

Asbestos recognition, inside different matrices (i.e. Asbestos Containing Materials: ACMs), is of great importance both “in situ” and in the further analysis at lab scale. Among the industrial sectors utilizing asbestos, the building and construction sector is the most important, especially with reference to all the constructions built before the ‘90s. The large utilization of asbestos is mainly linked to its technical properties (i.e. resistance to abrasion, heat and chemicals). Despite its properties, asbestos is recognized as a hazardous material to human health and starting from the ‘80s its use was banned in many countries. Asbestos, in fact, is potentially dangerous due to the potential release in air of fibers that can be inhaled or ingested as a consequence of degradation/alteration phenomena and manipulation/handling activities. Fast and reliable recognition of ACMs, as well as ACMs degradation characteristics, represent two important targets to be reached. ACMs sample collection and their proper preparation and handling are two fundamental aspects in order to correctly perform the analyses, taking into account at the same time operators’ safety. In this paper these latter aspects, specifically investigated with reference to the utilization of an emerging and powerful analytical technique (i.e. hyperspectral imaging: HSI), were analyzed and discussed. Different preparation and sample handling procedures were set up and tested in order to reach the optimal conditions to perform all the analyses in safety, but at the same time not altering the optically acquired information at the base of the ACMs recognition/classification.

With the growing use of the Vegetation Index in many remote sensing applications, it was imperative to examine the Brightness Temperature (BT) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to decrease the long-term inaccuracy of the BT data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instability of data is a consequence of orbit degradation, and from the circuit drifts over the life of a satellite. Degradation of BT over time and shifts of BT between the satellites were estimated using the China data set, because it includes a wide variety of different ecosystems represented globally. It was found that the data for six particular years, four of which were consecutive, are not stable compared to other years because of satellite orbit drift, AVHRR sensor degradation, and satellite technical problems, including satellite electronic and mechanical satellite systems deterioration. The data for paired years for the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 were assumed to be standard because the crossing time of the satellite over the equator maximized the value of the coefficients. These years were considered the standard years, while in other years the quality of satellite observations significantly deviated from the standard. The deficiency of data for the affected years were normalized or corrected by using the EDF method and compared with the standard years. These normalized values were then utilized to estimate new BT time series that show significant improvement of BT data for the affected years so that the dataset is useful for environment monitoring.