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DOE OSTI2022

Color Sensor Comparison for Solvent Extraction Processes

Idaho National Laboratory is building Beartooth, a test bed to advance research into nuclear fuel cycle reprocessing. This test bed will give researchers the opportunity to study solvent extraction processes. Beartooth extraction operations will include the use of centrifugal contactors designed for the recovery and purification of special nuclear material from spent fuel. This new test bed facility provides researchers with the opportunity to study innovative technologies. As part of that initiative, this project will integrate non-traditional (atypical to a solvent extraction process) measurement sensors into a system of contactors to determine the state-of-health. These non-traditional sensors have the potential to enhance nuclear safeguarding efforts and other process activities. The non-traditional sensors include accelerometers, acoustic microphones, colorimetric, pH, conductivity, viscosity, density, and infrared cameras. This report documents the testing and evaluation of two different colorimetric RGB sensors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2018

Virtual Sensors Determined Through Machine Learning

We propose a method that increases the capability of a conventional sensor, transforming it into an enhanced virtual sensor. This paper focuses on a virtual thermal Infrared Radiation (IR) sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classiﬁcation, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. This paper describes the developed deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree Celsius].

Torresen, Jim↗

NASA NTRS2018

Virtual Sensors Determined Through Machine Learning

We propose a method that increases the capability of a conventional sensor/instrument, transforming it into an enhanced virtual sensor/instrument. This paper focuses on a virtual thermal IR sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classiﬁcation, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual/RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. We developed a deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree].

Kurazume, Ryo↗

DOE OSTI2024

UAS remote sensing (Autel EVO II platform): Red-green-blue (RGB) imagery and derived products (Level 0-2 data), Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using a optical red-green-blue (RGB) sensor installed on an Autel EVO II unoccupied aerial system (UAS). This package includes data from 15 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71, Kougarok MM64, and Teller MM27 sites on the Seward Peninsula, Alaska USA, in July 2022. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors, and Level 1-2 processed products including digital surface models (DSM), photo orthomosaics, canopy height models (CHM) and digital terrain models (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as image (*.jpg, *.tif), point cloud (*.laz), tabular (*.csv) and *.pdf formats. Raw images are compressed as tar.gz. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the data and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

UAS remote sensing (Autel EVO II platform): Red-green-blue (RGB) imagery and derived products (Level 0-2 data), Seward Peninsula, Alaska, 2023

Airborne remote sensing data collected using a optical red-green-blue (RGB) sensor installed on an Autel EVO II unoccupied aerial system (UAS). This package includes data from 4 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71 and Teller MM27 sites on the Seward Peninsula, Alaska USA, in July 2023. This package provides the Level 0 (raw, unprocessed) data collected by the platform and sensors, and Level 1-2 processed products including digital surface models (DSM), photo orthomosaics, canopy height models (CHM) and digital terrain models (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Data and metadata are provided as image (*.jpg, *.tif), point cloud (*.laz), tabular (*.csv) and *.pdf formats. Raw images are compressed as tar.gz. The metadata documents contain flight campaign, platform, sensor, flight and file metadata, along with a description of the data and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Multimodal sensor fusion for real-time standoff estimation in directed energy deposition

In Laser Powder-based Direct Energy Deposition (LP-DED) systems, achieving consistency, precision and quality of produced parts requires tight control over printing parameters. One of the critical parameters is the standoff distance. Maintaining an optimal standoff height is crucial for achieving correct laser power density and powder catchment efficiency, as both laser and powder streams are focused at this distance. Here, this study introduces a novel approach using multimodal sensor fusion to predict standoff height in real-time. The proposed system integrates two low-profile, cost-effective sensors: an RGB coaxial camera and a high frequency and high dynamic range microphone. By utilizing a simple fully connected neural network, trained on a limited dataset, data fusion of these sensors allowed for the real-time prediction of the standoff height. The results demonstrate high resolution and accuracy of the predictions across multiple geometries and a wide range of standoff heights. This approach offers a simple, and cost-effective solution for real-time standoff height monitoring and lays the groundwork for future integration into commercial LP-DED systems.

42 ENGINEERING↗

DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Surface and Buried Thermal, and RGB Unexploded Ordnance Data Collection

This document provides a description of a data collection campaign of unexploded ordnance (UXOI) set. The dataset captures a controlled UAV imaging campaign designed to support detection of UXO across varied environmental conditions. Data were collected during three campaigns in Norris and Northeast Knoxville, Tennessee, using RGB, and thermal sensors mounted on Parrot UKR. In total, the dataset contains 9925 images, 26 full-motion video, and approximately 81.99 GB of data, collected across late spring/summer conditions, every hour during sunlight, and multiple surface contexts, including tall grass, short grass, gravel, as well as buried in sand, and other gravel mixtures. The collection was designed to capture thermal and visual variability relevant to UXO detection in agricultural land, bare earth, and subsurface. Review of the imagery showed that ordnance was most detectable during periods of changing solar input, especially approximately 10-60 minutes after sunrise, approximately 20-60 minutes after sunset, and 2-3 min after cloud cover interrupted prolonged solar heating. These conditions increased thermal contrast because many ordnance items retained or released heat differently than the surrounding vegetation and ground surface. This dataset provides a useful resource for developing and evaluating airborne UXO detection methods under realistic field conditions. All ordnance used in the study was inert, and thermal behavior may differ from that of live ordnance. In addition, variation in ordnance type, composition, and placement introduced differences in thermal response that should be considered when interpreting results.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2023

National Center for Airborne Laser Mapping (NCALM) LiDAR, Imagery, and DEM data from five NGEE Arctic Sites, Seward Peninsula, Alaska, August 2021

From August 8 through August 16 of 2021, airborne remote sensing data was collected by the National Center for Airborne Laser Mapping (NCALM) in collaboration with NGEE Arctic scientists. Data was collected around five NGEE Arctic study sites on the Seward Peninsula of Alaska: Teller mm 27, Teller mm 47, Kougarok mm 64, Kougarok mm 86, and Council mm 71. A Robinson R44 II helicopter with a RIEGL VQ-580 II airborne laser scanner was used to collect the LiDAR point cloud data for each study site. A Phase One iXM-RS100F camera was integrated with the Riegl sensor to collect RGB imagery. This data package contains LiDAR point clouds (.las), RGB imagery (tif), 1 m or 50 cm Digital Elevation Models (.tif) generated from the LiDAR data, and shapefiles of the .las tiling system for each site (.shp). Two supplemental documents are also included in the package: 1) a report describing data collection details, GNSS corrections, and processing steps and 2) a document describing the LiDAR Classification used (.pdf). This survey was conducted towards the end of the summer on the Seward Peninsula, and can be paired with data collected in April of 2022 during the snow-on campaign "National Center for Airborne Laser Mapping (NCALM) LiDAR and DEM data from two NGEE Arctic Sites, Seward Peninsula, Alaska, Winter 2022" (Singhania et.al, 2023) (NGA314). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Limb Correction of Individual Infrared Channels Used in RGB Composite Products

This study demonstrates that limb-cooling can be removed from infrared imagery using latitudinally and seasonally dependent limb correction coefficients, which account for an increasing optical path length as scan angle increases. Furthermore, limb-corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situation awareness for operational forecasters, seamless transition between overlaid RGB composites, easy comparison of RGB products from different sensors, and the availability of high quality proxy products for the GOES-R era, as demonstrated by the case examples presented in Section 3. This limb correction methodology can also be applied to additional infrared channels used to create other RGB products, including those created from other satellite sensors, such as Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS).

RGB↗

NASA NTRS2015

Limb Correction of Individual Infrared Channels Used in RGB Composite Products

Elmer, Nicholas J.↗

DOE OSTI2024

NDVI and Multispectral Images, UAS Flights, Council Road Site, Seward Peninsula, Alaska, June 2022

The dataset contains Normalized Difference Vegetation Index (NDVI) and multispectral images collected on June 18, 2022 using an UAS (Unmanned Aerial System) Yuneec H520, Mapir Survey 3, and Parrot Sequoia cameras. This data was created to accompany the following datasets:- NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska (https://doi.org/10.5440/1526749)- Sun-induced chlorophyll fluorescence/eddy covariance measurements at NGEE Arctic Council site, Seward Peninsula, Alaska, 2022 (https://doi.org/10.15485/2466175)- NGEE Arctic Meteorological Data from Instrumented PCDC Stations, Council Site, Seward Peninsula, Alaska, 2018 - 2023 (https://doi.org/10.5440/1529604)Survey 3 is RGN (Red, Greeen, Near Infra Red) camera with 87° Horizontal Field of View (HFOV) (19mm) f/2.8 Aperture, 550nm/660nm/850nm (Red+Green+NIR) filter transmission and produced geo referenced jpg and raw format images. Parrot Sequoia is a camera with 16 megapixel RGB (Red, Green, Blue) sensor and 1.2 megapixel monochrome sensors in discrete spectral bands: Green (550nm - 40Nm Bandwidth), Red (660nm - 40nm Bandwidth), Red Edge (735nm - 10nm Bandwidth) and Near Infrared (790nm - 40nm Bandwidth).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Developing a 3-DOF Compliant Perching Arm for a Free-Flying Robot on the International Space Station

This paper presents the design and control of the 3-DOF compliant perching arm for the free-flying Astrobee robots that will operate inside the International Space Station (ISS). The robots are intended to serve as a flexible platform for future guest scientists to use for zero-gravity robotics research - thus, the arm is designed to support manipulation research. It provides a 1-DOF underactuated tendon-driven gripper capable of enveloping a range of objects of different shapes and sizes. Co-located RGB camera and LIDAR sensors provide perception. The Astrobee robots will be capable of grasping each other in flight, to simulate orbital capture scenarios. The arm's end-effector module is swappable on-orbit, allowing guest scientists to add upgraded grippers, or even additional arm degrees of freedom. The design of the arm balances research capabilities with Astrobee's operational need to perch on ISS handrails to reduce power consumption. Basic arm functioning and grip strength were evaluated using an integrated Astrobee prototype riding on a low-friction air bearing.

Park, In-Won↗

NASA NTRSDate not supplied

Standardized RGB Composite Imagery for Global Applications

NASA’s MODIS instruments on the Terra and Aqua satellites have been providing imaging capabilities from polar orbit for the last 20 years allowing for twice daily sampling of mesoscale atmospheric features with multispectral or Red, Green, Blue (RGB) composite imagery. With the launch of the next generation of geostationary sensors the capability exists for expanded coverage of RGB imagery with the ability to track features over long distances and study the processes that contribute to the development of fast evolving features. However, the use of RGB imagery on a global basis is not without limitations due to the impact of limb-effects at high viewing angles and subtle spectral channel differences between satellite sensors.

Emily Berndt↗

NASA NTRS2016

Limb Correction of Polar-Orbiting Imagery for the Improved Interpretation of RGB Composites

Red-Green-Blue (RGB) composite imagery combines information from several spectral channels into one image to aid in the operational analysis of atmospheric processes. However, infrared channels are adversely affected by the limb effect, the result of an increase in optical path length of the absorbing atmosphere between the satellite and the earth as viewing zenith angle increases. This paper reviews a newly developed technique to quickly correct for limb effects in both clear and cloudy regions using latitudinally and seasonally varying limb correction coefficients for real-time applications. These limb correction coefficients account for the increase in optical path length in order to produce limb-corrected RGB composites. The improved utility of a limb-corrected Air Mass RGB composite from the application of this approach is demonstrated using Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, the limb correction can be applied to any polar-orbiting sensor infrared channels, provided the proper limb correction coefficients are calculated. Corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situational awareness for operational forecasters, and the ability to use RGB composites from multiple sensors jointly to increase the temporal frequency of observations.

satellite imagery↗

NASA NTRS2008

Chromatic Modulator for a High-Resolution CCD or APS

A chromatic modulator has been proposed to enable the separate detection of the red, green, and blue (RGB) color components of the same scene by a single charge-coupled device (CCD), active-pixel sensor (APS), or similar electronic image detector. Traditionally, the RGB color-separation problem in an electronic camera has been solved by use of either (1) fixed color filters over three separate image detectors; (2) a filter wheel that repeatedly imposes a red, then a green, then a blue filter over a single image detector; or (3) different fixed color filters over adjacent pixels. The use of separate image detectors necessitates precise registration of the detectors and the use of complicated optics; filter wheels are expensive and add considerably to the bulk of the camera; and fixed pixelated color filters reduce spatial resolution and introduce color-aliasing effects. The proposed chromatic modulator would not exhibit any of these shortcomings. The proposed chromatic modulator would be an electromechanical device fabricated by micromachining. It would include a filter having a spatially periodic pattern of RGB strips at a pitch equal to that of the pixels of the image detector. The filter would be placed in front of the image detector, supported at its periphery by a spring suspension and electrostatic comb drive. The spring suspension would bias the filter toward a middle position in which each filter strip would be registered with a row of pixels of the image detector. Hard stops would limit the excursion of the spring suspension to precisely one pixel row above and one pixel row below the middle position. In operation, the electrostatic comb drive would be actuated to repeatedly snap the filter to the upper extreme, middle, and lower extreme positions. This action would repeatedly place a succession of the differently colored filter strips in front of each pixel of the image detector. To simplify the processing, it would be desirable to encode information on the color of the filter strip over each row (or at least over some representative rows) of pixels at a given instant of time in synchronism with the pixel output at that instant.

Hartley, Frank↗

NASA NTRS2016

Limb Correction of Infrared Imagery in Cloudy Regions for the Improved Interpretation of RGB Composites

Red-Green-Blue (RGB) composites (EUMETSAT User Services 2009) combine information from several channels into a single composite image. RGB composites contain the same information as the original channels, but presents the information in a more efficient manner. However, RGB composites derived from infrared imagery of both polar-orbiting and geostationary sensors are adversely affected by the limb effect, which interferes with the qualitative interpretation of RGB composites at large viewing zenith angles. The limb effect, or limb-cooling, is a result of an increase in optical path length of the absorbing atmosphere as viewing zenith angle increases (Goldberg et al. 2001; Joyce et al. 2001; Liu and Weng 2007). As a result, greater atmospheric absorption occurs at the limb, causing the sensor to observe anomalously cooler brightness temperatures. Figure 1 illustrates this effect. In general, limb-cooling results in a 4-11 K decrease in measured brightness temperature (Liu and Weng 2007) depending on the infrared band. For example, water vapor and ozone absorption channels display much larger limb-cooling than infrared window channels. Consequently, RGB composites created from infrared imagery not corrected for limb effects can only be reliably interpreted close to nadir, which reduces the spatial coverage of the available imagery. Elmer (2015) developed a reliable, operational limb correction technique for clear regions. However, many RGB composites are intended to be used and interpreted in cloudy regions, so a limb correction methodology valid for both clear and cloudy regions is needed. This paper presents a limb correction technique valid for both clear and cloudy regions, which is described in Section 2. Section 3 presents several RGB case studies demonstrating the improved functionality of limb-corrected RGBs in both clear and cloudy regions, and Section 4 summarizes and presents the key conclusions of this work.

RGB↗

NASA NTRS2022

Reversible Colorimetric Sensing of Volatile Analytes By Wicking in Close Proximity to A Photonic Film

Isolation of volatile analytes from environmental or biological fluids is a rate-determining step that can delay the response time for continuous sensing. In this paper, we demonstrate a colorimetric sensing system that enables the rapid detection of gas-phase analytes released from a flowing micro-volume fluid sample. The sensor platform is an analyte-responsive metal-insulator-metal (MIM) thin-film structure integrated with a large area quartz micropillar array. This allows precise planar alignment and microscale separation (310 μm) of the optical and fluidic structures. This configuration offers rapid and homogeneous color changes over large areas that permits detection by low-resolution optics or eye, which is well-suited to portable/wearable devices. For our proof-of-principle demonstration, we utilized a poly(methyl methacrylate) (PMMA) spacer and evaluated the sensor's response (color change) to ethanol vapor. We show that the RGB color value is quantitatively linked to the spacer swelling, which is reversible and repeatable. The optofluidic platform reduces the sensor response time from minutes to seconds compared with experiments using a conventional chamber. The sensor's concentration-dependent response was examined, confirming the potential of the reported sensing platform for continuous, compact, and quantitative colorimetric analysis of volatile analytes in low-volume samples, such as biofluids.

Timothy J. Palinski↗