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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Detecting new physics as novelty — Complementarity matters

Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in ref. [1], between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a $t\bar{t}γγ$ final state: $t\bar{t}h$, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, resulting in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for detecting both signals are found to be encouraging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Electropolymerized-molecularly imprinted polymers (E-MIPS) as sensing elements for the detection of dengue infection

Here, a straightforward in situ detection method for dengue infection was demonstrated through the molecular imprinting of a dengue nonstructural protein 1 (NS1) epitope into an electropolymerized molecularly imprinted polyterthiophene (E-MIP) film sensor. The key enabling step in the sensor fabrication is based on an epitope imprinting strategy, in which short peptide sequences derived from the original target molecules were employed as the main template for detection and analysis. The formation of the E-MIP sensor films was facilitated using cyclic voltammetry (CV) and monitored in situ by electrochemical quartz crystal microbalance (EC-QCM). Surface properties were analyzed using different techniques including atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and polarization modulation-infrared reflection-adsorption (PM-IRRAS). The standard calibration curve (R = 0.9830) was generated for the detection of the epitope, Ac-VHTWTEQYKFQ-NH 2 , with a linear range of 0.2 to 30 μg/mL and detection limit of 0.073 μg/mL. A separate calibration curve (R = 0.9786) was obtained using spiked buffered solutions of dengue NS1 protein, which resulted in a linear range of 0.2 to 10 μg/mL and a detection limit of 0.056 μg/mL. The fabricated E-MIP sensor exhibited long-term stability, high sensitivity, and good selectivity towards the targeted molecules. These results indicated that the formation of the exact and stable cavity imprints in terms of size, shape, and functionalities was successful. In our future work, we aim to use our E-MIP sensors for NS1 detection in real-life samples such as serum and blood.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Delayed-Phase Cone-Beam CT Improves Detectability of Intrahepatic Cholangiocarcinoma During Conventional Transarterial Chemoembolization

PurposeTo evaluate the detectability of intrahepatic cholangiocarcinoma (ICC) on dual-phase cone-beam CT (DPCBCT) during conventional transarterial chemoembolization (cTACE) compared to that of digital subtraction angiography (DSA) with respect to pre-procedure contrast-enhanced magnetic resonance imaging (CE-MRI) of the liver.MethodsThis retrospective study included 17 consecutive patients (10 male, mean age 64) with ICC who underwent pre-procedure CE-MRI of the liver, and DSA and DPCBCT (early-arterial phase (EAP) and delayed-arterial phase (DAP)) just before cTACE. The visibility of each ICC lesion was graded by two radiologists on a three-rank scale (complete, partial, and none) on DPCBCT and DSA images, and then compared to pre-procedure CE-MRI.ResultsOf 61 ICC lesions, only 45.9 % were depicted by DSA, whereas EAP- and DAP-CBCT yielded a significantly higher detectability rate of 73.8 % and 93.4 %, respectively (p < 0.01). Out of the 33 lesions missed on DSA, 18 (54.5 %) and 30 (90.9 %) were revealed on EAP- and DAP-CBCT images, respectively. DSA depicted only one lesion that was missed by DPCBCT due to streak artifacts caused by a prosthetic mitral valve. DAP-CBCT identified significantly more lesions than EAP-CBCT (p < 0.01). Conversely, EAP-CBCT did not detect lesions missed by DAP-CBCT. For complete lesion visibility, DAP-CBCT yielded significantly higher detectability (78.7 %) compared to EAP (31.1 %) and DSA (21.3 %) (p < 0.01).ConclusionDPCBCT, and especially the DAP-CBCT, significantly improved the detectability of ICC lesions during cTACE compared to DSA. We recommend the routine use of DAP-CBCT in patients with ICC for per-procedure detectability and treatment planning in the setting of TACE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

The Value of Hybrid Angio-CT in Preoperative Detection and Localization of Insulinomas: A Single-Center Retrospective Study

ObjectiveTo investigate the performance of hybrid angio-CT in preoperative detection and localization for insulinomas.MethodsFifty-four postoperative pathology-confirmed patients from April 2015 to March 2017 were retrospectively reviewed; all patients underwent angio-CT with administration of contrast media in SMA, GDA and SA due to negative, inconclusive or controversial findings by several preoperative diagnostic methods including TAUS, CEUS, EUS, ECT, EMRI and DSA. Surgery was used as gold standard for localization of insulinomas; detection sensitivity and localization accuracy of angio-CT were assessed and compared with other preoperative image options.ResultsFifty-six benign insulinomas (mean diameter, 15.8 mm) in 54 patients (18 men and 36 women) were found according to surgical results; the overall detection sensitivity of TAUS, CEUS, EUS, ECT, EMRI and DSA was 21.4, 78.4, 79.2, 70.0, 79.2 and 72.2%, respectively. The overall localization accuracy was 14.3, 58.8, 68.8, 60.0, 75.0 and 44.4%, respectively. A total of 53 tumors were detected by angio-CT, 51 tumors were accurately located, and the overall detection sensitivity and localization accuracy of angio-CT was 94.4 and 90.7%, which was significantly higher than that of all other preoperative diagnostic methods (p < 0.05). No any serious complications occurred during angio-CT.ConclusionOur study indicates the great potential value of angio-CT in the preoperative detection and localization of insulinomas; angio-CT can be used as a preferred invasive diagnostic method.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

A new method for detecting abrupt changes in the dependence among multivariate hydrological series based on moving cut total correlation

Knowledge of how to define and estimate the dependence among multivariate hydrological series is essential for detecting abrupt changes in the dependence. Here, in this paper, a new method (BMCTC) is proposed to detect all possible abrupt change points in the dependence among multivariate hydrological series. The total correlation estimated by the matrix-based Renyi's alpha-order entropy functional is firstly introduced to define and measure the dependence strength among multivariate hydrological series. Then, the moving cut total correlation (MCTC) sequence is built by the moving window technique, which is used to measure changes in the dependence strength among multivariate hydrological series. Finally, the Bernaola-Galvan algorithm is used to detect all change points of the MCTC sequence. Simulations are performed to compare the effectiveness of BMCTC with Pearson correlation (BMCPC) and Spearman correlation (BMCSC), Cramer-von Mises (CvM) and copula-based likelihood-ratio (CLR). The results show that all change points are detected by BMCTC regardless of the samples size, but wrong change points or no change points are detected by other methods in most cases. BMCTC is applied to detect change points in the dependence among annual runoff, precipitation and sediment discharge series in the Xiliugou and the Kuyehe River, China. It is found that the dependence among runoff, precipitation and sediment discharge changed abruptly in 1980 and 1996 in the Kuyehe River and in 1999 in the Xiliugou River. These changes are mainly caused by human activities such as construction of water conservancy projects and coal mining.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spatiotemporal pattern detection, generation, and computation with circuits

Abstract Implementations of neurons, delays, and synapse circuits are presented with simulations. These neural elements are used to create two small spiking neural networks, the Rate-Window and Order-Biased clusters, which are capable of detecting simple two-spike spatiotemporal patterns. A simple pattern detecting network (SPDN) is created by combining the Rate-Window and Order-Biased clusters, where clusters are small spiking neural networks, and its simple pattern detection ability is demonstrated in simulation. The SPDN is used to implement a complex pattern detecting network (CPDN) and its complex pattern detection ability is demonstrated in simulation. Methods for generating arbitrary spatiotemporal patterns are presented. The CPDN and spatiotemporal pattern generation methods are then used to implement a novel spatiotemporal computing paradigm based on detecting and responding to spatiotemporal symbols. A simulation of a spatiotemporal half adder is presented to demonstrate the computing paradigm.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Evaluation of a high-throughput method for processing sponge-stick samples to detect viable, non-spore-forming biothreat agents

After a bioterrorism incident, surface sampling is often used to determine the extent of contamination and exposure, guiding decontamination efforts and decisions for re-occupancy of affected sites. The sponge-stick (SS) is a preferred and commonly used device for sample collection to detect both spore-forming and non-spore-forming biothreat agents from non-porous surfaces. Here, in this study, a recently developed high-throughput method (HTM) for processing SS samples to detect viable Bacillus anthracis spores was adapted for detection of non-spore-forming biothreat agents, Yersinia pestis and Francisella tularensis. The scalable HTM was used to process up to 20 SS samples simultaneously, compared to the current stomacher-based method which processes one SS at a time. Comparisons of the HTM and the stomacher-based method were statistically indistinguishable for most experiments (P > 0.05) with HTM recoveries of 37–60 % for Y. pestis inoculated at 102–103 cells/SS and held 48 h at 4 °C to mimic sample transport/storage. The HTM was integrated with Rapid Viability-Polymerase Chain Reaction (RV-PCR) analysis to detect viable Y. pestis in the presence of particulate contamination (Arizona Test Dust, ATD). This approach detected Y. pestis inoculated at 20 cells/SS and ATD did not impact detection (P > 0.05). F. tularensis showed significantly lower recoveries between no-hold time and 48-h hold time (4 °C, P < 0.05) using the HTM, which further testing showed could be due to toxicity of the neutralizing buffer used for SS pre-wetting. With modifications, this method could enhance throughput capacity while maintaining similar recovery efficiencies to current methods for other non-spore-forming bacterial pathogens.

Biological and medical sciences↗
DOE OSTI2014

Confocal Raman Microscopy for in Situ Detection of Solid-Phase Extraction of Pyrene into Single C 18 –Silica Particles

Solid-phase extraction (SPE) is a well known method for separating and pre concentrating analytes prior to ex-situ detection and quantification. Despite advances in miniaturization of solid-phase extraction, the technique has not evolved to meet the needs for detection in small scale microfluidic devices. While successful efforts have led to smaller scale extractors that allow preconcentration from small volumes, detection within correspondingly small volumes has remained out of reach because of analyte dilution during a post extraction transfer or “wash off” step prior to detection. In this work, successful extraction into sub-picoliter collection volumes is accomplished by using a single chromatographic particle as an extractor; wash off dilution is totally avoided by using confocal Raman microscopy for in-situ detection within the single-particle collection phase. Specifically, micromolar concentrations of pyrene in methanol water solution were equilibrated with an individual C 18 -functionalized silica particle, and Raman spectra were acquired from a small confocal sampling volume (~1 fL) within the particle interior. By comparing the Raman scattering intensity of the pyrene with that of the C 18 -chains in the stationary phase, it was possible to quantify the equilibrium coverage of pyrene relative to the C 18 chains. Spectroscopic isotherms were measured to determine the saturation surface coverage of pyrene relative to C 18 -chains and to study how the preconcentration equilibrium can be controlled by the source-phase solution composition. For extraction from aqueous solutions having the lowest methanol concentration (5% by volume), the preconcentration factor for pyrene into the particle was found to be 4.8 (±0.8) × 10 4 , which allowed detection of pyrene extracted from a 10 nM solution into a 0.52-pL particle volume.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Enhancing the Sensitivity of Solid-State NMR Experiments with Very Low Gyromagnetic Ratio Nuclei with Fast Magic Angle Spinning and Proton Detection

Many transition metals commonly encountered in inorganic materials and organometallic compounds possess NMR-active nuclei with very low gyromagnetic ratios (γ) such as 89 Y, 103 Rh, 109 Ag, and 183 W. A low-γ leads to poor NMR sensitivity and other experimental challenges. Consequently, nuclei with low-γ are often impossible to study with conventional solid-state NMR methods. In this paper, we combine fast magic angle spinning (MAS) and proton detection to enhance the sensitivity of solid-state NMR experiments with very low-γ nuclei by 1–2 orders of magnitude. Coherence transfer between 1 H and low-γ nuclei was performed with low-power double quantum (DQ) or zero quantum (ZQ) cross-polarization (CP) or dipolar refocused insensitive nuclei enhanced by polarization transfer (D-RINEPT). Comparison of the absolute sensitivity of CP NMR experiments performed with proton detection with 1.3 mm rotors and direct detection with 4 mm rotors shows that proton detection with a 1.3 mm rotor provides a significant boost in absolute sensitivity, while requiring approximately 1/40 th of the material required to fill a 4 mm rotor. Fast MAS and proton detection were applied to obtain 89 Y and 103 Rh solid-state NMR spectra of organometallic complexes. Finally, these results demonstrate that proton detection and fast MAS represents a general approach to enable and accelerate solid-state NMR experiments with very low-γ nuclei.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Selective Detection of Intermediate-Amplitude Motion by Solid-State NMR

The coexistence of rigid and mobile molecules or molecular segments abounds in biomolecular assemblies. Examples include the carbohydrate-rich cell walls of plants and intrinsically disordered proteins that contain rigid β-sheet cores. In solid-state nuclear magnetic resonance (NMR) spectroscopy, dipolar polarization transfer experiments are well suited for detecting rigid components, whereas scalar-coupling experiments are well suited for detecting highly mobile components. However, few NMR methods are available to detect the segments that undergo intermediate-amplitude fast motion. Here, we introduce two NMR experiments, a two-dimensional T 2H -filtered CP-hCH correlation and a three-dimensional J-INADEQUATE CCH correlation, to observe this intermediate-amplitude motion. Both experiments involve 1 H detection under fast magic-angle spinning (MAS). By combining 1 H transverse relaxation (T 2H ) filters with dipolar polarization transfer, we suppress the signals of both highly rigid and highly mobile species, thus revealing the signals of intermediate mobile species. 1 H detection under fast MAS is crucial for distinguishing the different motional amplitudes. Here, we demonstrate these techniques on several plant cell wall samples and show that they allow the selective detection and resolution of certain hemicellulose and pectin signals, which are usually masked by the signals of the rigid cellulose and the highly dynamic pectins in purely dipolar and scalar NMR spectra.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Redox-Based Electrochemical Affinity Sensor for Detection of Aqueous Pertechnetate Anion

Rapid, selective, in-situ detection of TcO4- in multicomponent matrices consisting of interfering anions such as the ubiquitous NO3- and Cl- or the isostructural and isoelectronic CrO42- is challenging. Present sensors mostly lack the selectivity to exclude these interferences, or the sensitivity to meet the detection limits that are lower than the drinking water standards across the globe. This work presents an affinity based electrochemical sensor for TcO4- detection that relies on selective reductive precipitation of aqueous TcO4- induced by a capture probe immobilized on an electrode platform. This results in a direct decrease of the electron transfer current, the magnitude of the decrease being proportional to the amount of TcO4- added. Using this approach, we were able to achieve a detection limit of 1x10-10 M, which is lower than the drinking water standard of 5.2x10-10 M set by United States Environmental Protection Agency. Our proposed approach also allowed us to detect TcO4- from a multicomponent groundwater sample obtained from a well at the Hanford site in Washington (well 299-W19-36) that also contained NO3- , Cl- and CrO42-, without discernably affecting the detection limits.

Chatterjee, Sayandev↗
DOE OSTI2022

An End-To-End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. Here, in this study, we introduce a new end-to-end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi-station processing of seismic waveforms recorded over a seismic network. This end-to-end architecture consists of three sub-networks: a backbone network that extracts features from raw waveforms, a phase picking sub-network that picks P- and S-wave arrivals based on these features, and an event detection sub-network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub-networks together with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end-to-end approach can effectively pick P- and S-wave arrivals and achieve earthquake detection accuracy rivaling that of other state-of-the-art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.

58 GEOSCIENCES↗
DOE OSTI2022

An Overview of ARTMIP's Tier 2 Reanalysis Intercomparison: Uncertainty in the Detection of Atmospheric Rivers and Their Associated Precipitation

Atmospheric rivers, or long but narrow regions of enhanced water vapor transport, are an important component of the hydrologic cycle as they are responsible for much of the poleward transport of water vapor and result in precipitation, sometimes extreme in intensity. Despite their importance, much uncertainty remains in the detection of atmospheric rivers in large datasets such as reanalyses and century scale climate simulations. To understand this uncertainty, the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) developed tiered experiments, including the Tier 2 Reanalysis Intercomparison that is presented here. Eleven detection algorithms submitted hourly tags--binary fields indicating the presence or absence of atmospheric rivers--of detected atmospheric rivers in the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts' Reanalysis Version 5 (ERA5) as well as six-hourly tags in the Japanese 55-year Reanalysis (JRA-55). Due to a higher climatological mean for integrated water vapor transport in MERRA-2, atmospheric rivers were detected more frequently relative to the other two reanalyses, particularly in algorithms that use a fixed threshold for water vapor transport. The finer horizontal resolution of ERA5 resulted in narrower atmospheric rivers and an ability to detect atmospheric rivers along resolved coastlines. We found that the fraction of hemispheric area covered by ARs varies throughout the year in all three reanalyses, with different atmospheric river detection tools having different seasonal cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

Abstract Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian et al., 2021, https://doi.org/10.3390/rs13122309 , 2022, https://doi.org/10.1029/2021gl097070 ). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECECAN to build a plasma proteome library from DIA data and to query known sequence variants.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Air, surface, and wastewater surveillance of SARS-CoV-2; a multimodal evaluation of COVID-19 detection in a built environment

Environmental surveillance of infectious organisms holds tremendous promise to reduce human-to-human transmission in indoor spaces through early detection. In this study we determined the applicability and limitations of wastewater, indoor high-touch surfaces, in-room air, and rooftop exhaust air sampling methods for detecting SARS-CoV-2 in a real world building occupied by residents recently diagnosed with COVID-19. We concurrently examined the results of three 24-hour environmental surveillance techniques, indoor surface sampling, exhaust air sampling and wastewater surveillance, to the known daily census fluctuations in a COVID-19 isolation dormitory. Additionally, we assessed the ability of aerosol samplers placed in the large volume lobby to detect SARS-CoV-2 multiple times per day. Our research reveals an increase in the number of individuals confirmed positive with COVID-19 as well as their estimated human viral load to be associated with statistically significant increases in viral loads detected in rooftop exhaust aerosol samples (p = 0.0413), wastewater samples (p = 0.0323,), and indoor high-touch surfaces (p < 0.001)). We also report that the viral load detected in lobby aerosol samples was statistically higher in samples collected during presence of occupants whose COVID-19 diagnostic tests were confirmed positive via qPCR compared to periods when the lobby was occupied by either contact-traced (suspected positive) individuals or during unoccupied periods (p = 0.0314 and <2e–16). We conclude that each daily (24h) surveillance method, rooftop exhaust air, indoor high-touch surfaces, and wastewater, provide useful detection signals for building owner/operator(s). Furthermore, we demonstrate that exhaust air sampling can provide spatially resolved signals based upon ventilation exhaust zones. Additionally, we find that indoor lobby air sampling can provide temporally resolved signals useful during short duration sampling periods (e.g., 2-4 hours) even with intermittent occupancy by occupants diagnosed with COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Detection and imaging of chemicals and hidden explosives using terahertz time-domain spectroscopy and deep learning

Detecting concealed chemicals and explosives remains a critical challenge in global security. Terahertz time-domain spectroscopy (THz-TDS) offers a promising non-invasive and stand-off detection technique owing to its ability to penetrate optically opaque materials without causing ionization damage. While many chemicals exhibit distinct spectral features in the terahertz range, conventional terahertz-based detection methods often struggle in real-world environments, where variations in sample geometry, thickness, and packaging can lead to inconsistent spectral responses. In this study, we present a chemical imaging system that integrates THz-TDS with deep learning to enable accurate pixel-level identification and classification of different explosives. Operating in reflection mode and enhanced with plasmonic nanoantenna arrays, our THz-TDS system achieves a peak dynamic range of 96 dB and a detection bandwidth of 4.5 THz, supporting practical, stand-off operation. By analyzing individual time-domain pulses with deep neural networks, the system exhibits strong resilience to environmental variations and sample inconsistencies. Blind testing across eight chemicals—including pharmaceutical excipients and explosive compounds—resulted in an average classification accuracy of 99.42% at the pixel level. Notably, the system maintained an average accuracy of 88.83% when detecting explosives concealed under opaque paper coverings, demonstrating its robust generalization capability. These results highlight the potential of combining advanced terahertz spectroscopy with neural networks for highly sensitive and specific chemical and explosive detection in diverse and operationally relevant scenarios.

Imaging and sensing↗