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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dual-Phase Malicious User Detection Scheme for IM-OFDMA Systems Using IQ Imbalance

Physical-layer security techniques have contributed to the achievement of various security objectives in an efficient and lightweight manner. Thus, these techniques have been widely considered for limited-resource networks such as Internet of Things networks. Among the different security objectives, malicious user detection by exploiting physical-layer parameters has demonstrated efficient performance. In this work, malicious user detection in the recently proposed index modulation-based orthogonal frequency division multiple access (IM-OFDMA) is addressed. The proposed malicious user detection scheme exploits the hardware impairments, especially the in-phase and quadrature imbalance parameters, for both legitimate and malicious users to design a dual-phase efficient detection scheme. The proposed scheme accounts for the special characteristics of IM-OFDMA transmission that are different from other multiple-access techniques. The performance of the proposed scheme was evaluated considering detection probability and false alarm probability performance metrics. Moreover, closed-form expressions of these metrics were derived for both phases and were validated by Monte Carlo simulation results under different configurations of IM-OFDMA systems.

Alaca, Ozgur [ORNL] (ORCID:0000000153713758)↗
DOE OSTI2021

Trocylation of 3-quinuclidinol, a key marker for the chemical warfare agent 3-quinuclidinyl benzilate, for its enhanced detection at low levels in complex soil matrices by electron ionization gas chromatography–mass spectrometry

Detection of 3-quinuclidinol (3Q), a marker for the chemical warfare agent 3-quinuclidinyl benzilate, is very difficult by gas chromatography–mass spectrometry (GC/MS), providing low, broad signals even when analyzed in isolated form. Therefore, a method that can convert 3Q into a substrate with enhanced detectability by GC/MS would be an important tool for its analysis. 2,2,2-Trichloroethoxycarbonyl chloride (TrocCl) was used in the derivatization of 3Q in three different soils of varying composition and total organic content (Virginia type A soil, Nebraska EPA standard soil and Ottawa sand) when present at a 10 μg g -1 concentration in each. A direct derivatization protocol and one involving the pre-extraction of the analyte were evaluated for their individual efficiencies and subsequent analysis using electron ionization GC/MS. The practical derivatization of 3Q, when present at low levels (10 μg g -1 ) in three different soil matrices, was found to be rapid (1 h) and to take place smoothly at ambient temperature (and as low as 4°C). The method detection limit was determined to be 30 ng mL -1 for the Virginia type A soil, 49 ng mL -1 for the Nebraska EPA standard soil and 72 ng mL -1 for the Ottawa sand sample. An expedient and practical derivatization method for 3Q, a chemical warfare degradation product difficult to detect by GC/MS, has been realized using trichloroethyl chloroformate. The reaction provides 3Q-Troc, a derivative with better detectability than 3Q by electron ionization GC/MS such as peak sharpness and a unique mass spectrum for its unambiguous identification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2022

Broadband Fourier-Transform-Detected EPR at W-Band

This manuscript reports development of wideband Fourier transform (FT) electron paramagnetic resonance (EPR) detection capability at the uniquely high frequency of 94 GHz. Here, this technique is based on the HiPER spectrometer developed at the University of St. Andrews, into which we have integrated an arbitrary waveform generator (AWG) that is used to modulate the output from a solid-state multiplier chain prior to amplification, generating up to 1 kW microwave power with 1 GHz bandwidth. Benchmark experiments are presented for a standard TEMPOL radical, which comprises a 500 MHz broad EPR spectrum at 94 GHz. Using a single frequency-swept microwave pulse, efficient inversion of this spectrum is demonstrated, enabling frequency-dependent studies of the magnetization relaxation in the time-domain, again via broadband echo detection. From these measurements, an anisotropy in the spin–lattice relaxation time, T 1 , can be determined with ease for TEMPOL. In addition, we implement the FT detection scheme for multi-dimensional (electron–electron double resonance, or ELDOR) experiments, demonstrating the full capabilities of the HiPER spectrometer. As an example, we present FT-detected version of the ELDOR-detected nuclear magnetic resonance technique for the TEMPOL radical. We also describe potential applications of the new instrumentation for studies of gadolinium qubits.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2020

Spectroradiometric detection of competitor diatoms and the grazer Poteriochromonas in algal cultures

To address challenges in early detection of pond pests, we have extended a spectroradiometric monitoring method, initially demonstrated for measurement of pigment optical activity and biomass, to the detection of algal competitors and grazers. The method relies upon measurement and interpretation of pond reflectance spectra spanning from the visible into the near-infrared. Reflectance spectra are acquired every 5 min with a multi-channel, fiber-coupled spectroradiometer, providing monitoring of algal pond conditions with high temporal frequency. The spectra are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed in terms of the absorption and backscatter coefficients of the cultured species, with additional terms accounting for the pigment fluorescence features and for the water-surface reflection of sunlight and skylight. With this method we demonstrate detection of diatoms and the predator Poteriochromonas in outdoor cultures of Nannochloropsis oceanica and Chlorella vulgaris, respectively. Further, the relative strength of these signatures is compared to microscopy and sequencing analysis. Spectroradiometric detection of diatoms is then further assessed on beaker-contained mixtures of Microchloropsis salina with Phaeodactylum tricornutum, Thalassiosira weissflogii, and Thalassiosira pseudonana, respectively, providing an initial evaluation of the sensitivity and specificity of detecting pond competitors.

09 BIOMASS FUELS↗
DOE OSTI2021

Striking luminescence phenomena of carbon dots and their applications as a double ratiometric fluorescence probes for H2S detection

Here, we report some new observations on the luminescence of carbon dots (CDs) that are passivated with polyethylene glycol, and explored them, for the first time, as highly sensitive H2S detection probe. The as-prepared CDs have an ultraviolet (UV) emission at 350 nm and a green emission at 540 nm when excited at 270 nm, but they have only a green emission at 523 nm when excited at 365 nm. As Na2S is added to the CDs, the UV emission decreases gradually, while the green emission increases slightly, and interestingly a new emission appears at 455 nm is increased linearly in intensity with the increase of Na2S concentration. The blue emission at 455 nm is from the intrinsic core of CDs, and the green emissions at 540 or 523 nm are from their surface states, while the UV emission at 350 nm is from the anchored PEG. Both the emission intensity ratios of F-450/F-350 and F-455/F-523 show an excellent linear relationship with the Na2S concentration in the range of 0-800 mu M, and the detection limit estimated from both of them is approximately 7.0 nM, indicating our method is the most sensitive H2S CDs based detection method reported thus far. Furthermore, the detection was tested successfully for H2S imaging in live cells. We propose that the interactions of H2S with the CDs and the anchored ligands, as well as the energy transfer between the ligands and the CDs, are responsible for the luminescence responses for H2S detection. Our studies enrich the world of carbon dots with plenty of potential applications. (C) 2020 Elsevier Ltd. All rights reserved.

Bioimaging↗
DOE OSTI2021

Deployment of a double scatter system for directional detection of background neutron radiation

The detection of special nuclear materials (SNM) requires the understanding of nuclear signatures that allow the discrimination against background. In particular, understanding neutron background characteristics such as count rates and energies and their correlations with environmental conditions and surroundings of measurement locations is important in enhancing SNM detection capabilities. The Mobile Imager of Neutrons for Emergency Responders (MINER) was deployed for 8 weeks in downtown San Francisco (CA) to study such neutron background characteristics in an urban environment. Of specific interest was the investigation of the impact of surrounding buildings on the neutron background count rates and to answer the question whether buildings act as absorber of neutrons or as sources via the so-called ship effect. MINER consists of 16 liquid scintillator detector elements and can be operated as a neutron spectrometer, as a neutron imager, or simply as a counter of fast neutrons. As expected, the neutron background rate was found to be inversely proportional to the atmospheric pressure. In the energy range where MINER is most sensitive, approximately 1–10 MeV, it was found that the shape of the detected background spectrum is similar to that of a detected fission spectrum, indicating the limited discrimination power of the neutron energy. The similarities between the detected background neutron spectrum and fission sources makes it difficult to discriminate SNM from background based solely on the energies observed. The images produced using maximum likelihood expectation maximization revealed that neutrons preferentially are coming from areas in the environment that have open sky, indicating that the surrounding buildings act as absorbers of neutrons rather than sources as expected by the ship effect. Here, the inherent properties of a neutron scatter camera limit the achievable image quality and the effective deployment to systematically map neutron background signatures due to the low count rate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Clear-sky detection for PV degradation analysis using multiple regression

A method is presented to detect clear-sky periods for plane-of-array irradiance time-averaged data that is based on the algorithm originally described by Reno and Hansen. Here we show this new method improves the state-of-the-art by providing accurate detection at longer data averaging intervals. Moreover, our new method detects clear periods in plane-of-array data, which is novel. The new method is developed by applying a Design of Experiment approach to optimize the parameters used in the Reno method, and Monte Carlo simulations are used to understand the robustness of the found parameters. Clear-sky detection accuracy is compared among four methods: the Reno method, the default clear-sky filter in RdTools, the Ellis method, and the method outlined in this work, using a hand-labeled two-year data set of 1-min plane-of-array irradiance for a fixed tilt system. The RdTools clear-sky filter is marred by excessive false positives. The other methods all perform well at 1-min data intervals; the method developed here provides more accurate detection at longer data averaging intervals. We show that the parameters are directly linked to the data frequency in the hope that these input variables may not have to be optimized for every data frequency and location. However, only a single fixed system in one location was carefully examined. Finally, we illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

14 SOLAR ENERGY↗
DOE OSTI2023

Automatic detection of cataclysmic variables from SDSS images

Abstract Investigating rare and new objects have always been an important direction in astronomy. Cataclysmic variables (CVs) are ideal and natural celestial bodies for studying the accretion process of semi-detached binaries with accretion processes. However, the sample size of CVs must increase because a lager gap exists between the observational and the theoretical expanding CVs. Astronomy has entered the big data era and can provide massive images containing CV candidates. CVs as a type of faint celestial objects, are highly challenging to be identified directly from images using automatic manners. Deep learning has rapidly developed in intelligent image processing and has been widely applied in some astronomical fields with excellent detection results. YOLOX, as the latest YOLO framework, is advantageous in detecting small and dark targets. This work proposes an improved YOLOX-based framework according to the characteristics of CVs and Sloan Digital Sky Survey (SDSS) photometric images to train and verify the model to realise CV detection. We use the Convolutional Block Attention Module to increase the number of output features with the feature extraction network and adjust the feature fusion network to obtain fused features. Accordingly, the loss function is modified. Experimental results demonstrate that the improved model produces satisfactory results, with average accuracy (mean average Precision at 0.5) of 92.0%, Precision of 92.9%, Recall of 94.3%, and $F1-score$ of 93.6% on the test set. The proposed method can efficiently achieve the identification of CVs in test samples and search for CV candidates in unlabeled images. The image data vastly outnumber the spectra in the SDSS-released data. With supplementary follow-up observations or spectra, the proposed model can help astronomers in seeking and detecting CVs in a new manner to ensure that a more extensive CV catalog can be built. The proposed model may also be applied to the detection of other kinds of celestial objects.

Astronomy & Astrophysics↗
DOE OSTI2021

Smartphone-based Dual-channel Immunochromatographic Test Strip with Polymer Quantum Dot Labels for Simultaneous Detection of Cypermethrin and 3-phenoxybenzoic acid

Currently, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are the primary methods used to detect pesticides and their metabolites for biomonitoring of exposure. Although GC-MS and LC-MS can provide accurate and sensitive measurements, these techniques are not suitable for point-of-care or in-field biomonitoring applications. The objective of this work is to develop a smartphone-based dual-channel immunochromatographic test strip (ICTS) for on-site biomonitoring of exposure to cypermethrin by simultaneous detection of cypermethrin and its metabolite, 3-phenoxybenzoic acid (3-PBA). Polymer carbon dots (PCDs) with ultra-high fluorescent brightness were synthesized and used as signal amplifier in ICTS assay. Cypermethrin (a representative pyrethroid pesticide) and its major metabolite 3-PBA were simultaneously detected to provide more comprehensive analysis of cypermethrin exposure. After competitive immunoreactions between the target sample and the coating antigens preloaded on the test line, the tracer antibody (PCDs conjugated antibody) was quantitatively captured on the test lines. The captured PCDs were inversely proportional to the amount of the target compound in sample. The red fluorescence on the test line then was recorded by a smartphone-based device capable of conducting image analysis and recording. Under optimal conditions, the sensor showed excellent linear responses for detecting cypermethrin and 3-PBA ranging from 1 ng/mL to 100 ng/mL and from 0.1 ng/mL to 100 ng/mL, the limits of detection were calculated to be ~0.35 ng/mL for cypermethrin and ~0.04 ng/mL for 3-PBA, respectively. The results demonstrate that the ICTS device is promising for accurate point-of-care biomonitoring of pesticide exposure.

Zhao, Yuting↗
DOE OSTI2023

Luminescent Metal–Organic Framework for the Selective Detection of Aldehydes

The detection of toxic, hazardous chemical species is an important task because they pose serious risks to either the environment or human health. Luminescent metal–organic frameworks (LMOFs) as alternative sensors offer rapid and sensitive detection of chemical species. Interactions between chemical species and LMOFs result in changes in the photoluminescence (PL) profile of the LMOFs which can be readily detected using a simple fluorometer. Herein, we report the use of a robust, Zn-based LMOF, [Zn 5 (μ 3 -OH) 2 (adtb) 2 (H 2 O) 5 ·5 DMA] (Zn-adtb, LMOF-341), for the selective detection of benzaldehyde. Upon exposure to benzaldehyde, Zn-adtb experiences significant luminescent quenching, as characterized through PL experiments. Photoluminescent titration experiments reveal that LMOF-341 has a detection limit of 64 ppm and a Ksv value of 179 M –1 for benzaldehyde. Furthermore, we study the guest–host interactions that occur between LMOF-341 and benzaldehyde through in situ Fourier transform infrared and computational modeling employing density functional theory. The results show that benzaldehyde interacts more strongly with LMOF-341 compared to formaldehyde and propionaldehyde. Furthermore, our combined studies also reveal that the mechanism of luminescence quenching originates from an electron-transfer process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exceptional NH 3 Detection by Cu(cyhdc) MOF Sensor Due to H 2 O Coadsorption

Metal–organic framework (MOF)-based electrical impedance sensors are a growing class of sensors that show utility in the detection of environmentally toxic gases. Detection of trace NH 3 has been particularly difficult to design due to the low electrical response of NH 3 . However, this has been circumvented by judiciously selecting a MOF that has enhanced electrical response to NH 3 due to coadsorption with predominant atmospheric gases such as water. Herein, an MOF Cu(cyhdc) based sensor has been successfully demonstrated for the enhanced detection of environmentally toxic NH 3 gas. The sensor shows a change in impedance when it is exposed to 5 ppm of NH 3 . At 20 °C, >30% relative humidity (RH) is necessary to elicit a change, with the response increasing with RH and reaching 3170× at 92% RH, producing the largest published response to NH 3 for a MOF direct electrical sensor. In the absence of water, no change is observed toward 5 ppm of NH 3 over 20–50 °C. Here, this electrical response is largely driven by huge decreases in the imaginary component of the impedance, attributed to increased capacitance at the surface of the MOF crystallites upon NH 3 and H 2 O coadsorption. Complementary structural and microstructural characterization proves that the Cu(cyhdc) crystalline structure and morphology remain intact under trace NH 3 and/or H 2 O adsorption. Despite extremely low NH 3 , loadings seen in TGA, XPS, and FT-IR confirm NH 3 and H 2 O adsorption, and changes to the metal-carboxylate IR peak positions are observed upon NH 3 adsorption. Concentrated primarily at the outer surfaces of the MOF crystallites, this NH 3 and H 2 O coadsorption effectively increases the surface capacitance across the Cu(cyhdc) powder and enables direct electrical detection of trace NH 3 . Together, these results demonstrate how coadsorption of specific molecules (H 2 O) can be used to enable the electrical detection of trace toxic gases that would otherwise not have produced a MOF sensor response.

ammonia↗
DOE OSTI2025

Copper-Based Two-Dimensional Conductive Metal–Organic Framework Thin Films for Ultrasensitive Detection of Perfluoroalkyls in Drinking Water

Perfluoroalkyls (PFAS) continue to emerge as a global health threat making their effective detection and capture extremely important. Though metal–organic frameworks (MOFs) have stood out as a promising class of porous materials for sensing PFAS, detection limits remain insufficient and a fundamental understanding of detection mechanisms warrants further investigation. Here, in this study, we show the use of a 2D conductive MOF film based on copper hexahydroxy triphenylene (Cu-HHTP) to fabricate chemiresistive sensing devices for detecting PFAS in drinking water. We further show ultrasensitive detection using electrochemical impedance spectroscopy. Owing to excellent electrostatic attractions and electrochemical interactions between the copper-based MOF and PFAS, confirmed by high-resolution spectroscopy and theoretical simulations, the MOF-based sensor reported herein exhibits excellent affinity and sensitivity toward perfluorinated acids at concentrations as low as 0.002 ng/L.

2D metal−organic frameworks↗
DOE OSTI2026

Exploring Approaches for Accelerated Mass Spectrometric Detection of Fluorochemicals from Aqueous Solutions Using Indirect, Adsorbent-Facilitated Desorption Ionization

There is a growing need for rapid, low-cost analytical tools to screen water supplies for persistent contaminants. Due to their ubiquitous presence in the environment and their detrimental effects on human health, there is a public interest in widespread testing for perfluoroalkyl substances (PFAS). However, the most common methods for PFAS analysis can be time-intensive, requiring a preconcentration step followed by liquid chromatography–mass spectrometry (LC–MS), and are cost-prohibitive for most members of the public. To address these challenges, this study explores a novel method for rapid analysis of aqueous analytes by concentrating analytes on solid adsorbents and detecting them using mass spectrometric analysis via desorption ionization directly from the adsorbent surface. We demonstrate this general approach of indirect solution analysis (ISA) with desorption electrospray ionization (ISA-DESI) and laser desorption ionization (ISA-LDI). Various adsorbent materials were screened, including commercial mesoporous carbons, alumina, and metal–organic frameworks (MOFs). Using ISA-DESI, we consistently detect concentrations as low as 5–100 parts per trillion (ppt) for perfluorooctanoic acid (PFOA) and 10–100 ppt for perfluoroundecanoic acid (PFUnDA). This approach enables rapid detection in under 5 min, making it suitable for applications requiring trace detection with fast response times. We further demonstrate the use of ISA-DESI on a range of environmentally relevant contaminants. Advantages of this indirect solution analysis approach include simplified sample preparation, accelerated analysis, and low cost with readily available materials, making it a promising tool to accelerate mass spectrometric PFAS detection for identifying contaminated sites, performing laboratory studies, and monitoring industrial waste streams.

Bays, Nathan Robert [Sandia National Laboratories ↗
DOE OSTI2023

Highly Stable Fe/Co-TPY-MIL-88(NH 2 ) Metal–Organic Framework (MOF) in Enzymatic Cascade Reactions for Chemiluminescence-Based Detection of Extracellular Vesicles

Metal-Organic Frameworks (MOFs) can deliver many advantages when acting as enzyme mimics to assist with signal amplification in molecular detection: they have abundant active catalytic sites per unit volume of the material; their structures and elemental compositions are highly tunable, and their high specific surface area and porous property can assist with target separation and enrichment. In the present work, we have demonstrated that, by adding the pore partition agent, 2,4,6-tris(4-pyridyl)pyridine (TPY) during synthesis of the bimetallic Fe/Co-MIL-88(NH 2 ) MOF to block the open metal sites, a highly porous MOF of Fe/Co-TPY-MIL-88(NH 2 ) can be produced. This material also exhibits high stability in basic solutions and biofluids and possesses high peroxidase-mimicking activity, which can be utilized to produce long-lasting chemiluminescence (CL) from luminol and H 2 O 2 . Moreover, acting as the peroxidase-mimic, the Fe/Co-TPY-MIL-88(NH 2 ) MOF can form the enzymatic cascade with glucose oxidase (GOx) for biomarker detection. Further, when applied to detect extracellular vesicles (EVs), the MOF material and GOx are brought to the proximity on the EVs through two surface proteins, which triggers the enzyme cascade to produce high CL from glucose and luminol. EVs within the concentration range of 5 × 10 5 - 4 ×10 7 particles/mL can be detected, and the method can be used to analyze EV contents in human serum without sample preparation and EV purification. Overall, our work demonstrates that the high versatility and tunability of the MOF structures could bring in significant benefits to biosensing and enable ultrasensitive detection of biomarkers with judicious material designs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Listening for the Landing: Seismic Detections of Perseverance's Arrival at Mars With InSight

The entry, descent, and landing (EDL) sequence of NASA's Mars 2020 Perseverance Rover will act as a seismic source of known temporal and spatial localization. We evaluate whether the signals produced by this event will be detectable by the InSight lander (3,452 km away), comparing expected signal amplitudes to noise levels at the instrument. Modeling is undertaken to predict the propagation of the acoustic signal (purely in the atmosphere), the seismoacoustic signal (atmosphere-to-ground coupled), and the elastodynamic seismic signal (in the ground only). Our results suggest that the acoustic and seismoacoustic signals, produced by the atmospheric shock wave from the EDL, are unlikely to be detectable due to the pattern of winds in the martian atmosphere and the weak air-to-ground coupling, respectively. However, the elastodynamic seismic signal produced by the impact of the spacecraft's cruise balance masses on the surface may be detected by InSight. The upper and lower bounds on predicted ground velocity at InSight are 2.0 × 10 -14 and 1.3 × 10 -10 m s -1 . The upper value is above the noise floor at the time of landing 40% of the time on average. The large range of possible values reflects uncertainties in the current understanding of impact-generated seismic waves and their subsequent propagation and attenuation through Mars. Uncertainty in the detectability also stems from the indeterminate instrument noise level at the time of this future event. A positive detection would be of enormous value in constraining the seismic properties of Mars, and in improving our understanding of impact-generated seismic waves.

58 GEOSCIENCES↗