A Dual-Gated Structures for Lossless Ion Manipulations-Ion Mobility Orbitrap Mass Spectrometry Platform for Combined Ultra-High-Resolution Molecular Analysis.

High-resolution ion mobility spectrometry-mass spectrometry (HR-IMS-MS) instruments have enormously advanced the ability to characterize complex biological mixtures. Unfortunately, HR-IMS and HR-MS measurements are typically performed independently due to mismatches in analysis time scales. Here, we overcome this limitation by using a dual-gated ion injection approach to couple an 11 m path length structures for lossless ion manipulations (SLIM) module to a Q-Exactive Plus Orbitrap MS platform. The dual-gate setup was implemented by placing one ion gate before the SLIM module and a second ion gate after the module. The dual-gated ion injection approach allowed the new SLIM-Orbitrap platform to simultaneously perform an 11 m SLIM separation, Orbitrap mass analysis using the highest selectable mass resolution setting (up to 140 k), and high-energy collision-induced dissociation (HCD) in ∼25 min over an m/z range of ∼1500 amu. The SLIM-Orbitrap platform was initially characterized using a mixture of standard phosphazene cations and demonstrated an average SLIM CCS resolving power (RpCCS) of ∼218 and an SLIM peak capacity of ∼156, while simultaneously obtaining high mass resolutions. SLIM-Orbitrap analysis with fragmentation was then performed on mixtures of standard peptides and two reverse peptides (SDGRG1+, GRGDS1+, and RpCCS = 305) to demonstrate the utility of combined HR-IMS-MS/MS measurements for peptide identification. Our new HR-IMS-MS/MS capability was further demonstrated by analyzing a complex lipid mixture and showcasing SLIM separations on isobaric lipids. This new SLIM-Orbitrap platform demonstrates a critical new capability for proteomics and lipidomics applications, and the high-resolution multimodal data obtained using this system establish the foundation for reference-free identification of unknown ion structures.

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data.

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input.

Vapor detection and vapor pressure measurements of fentanyl and fentanyl hydrochloride salt at ambient temperatures.

There is a need for non-contact, real-time vapor detection of drugs to combat illicit transportation and help curb the opioid epidemic. The low volatility of drugs, like fentanyl, makes room temperature vapor detection of illicit drugs challenging, but feasible by atmospheric flow tube-mass spectrometry (AFT-MS). AFT-MS is a non-contact vapor detection approach capable of ultra-trace detection of drugs, including fentanyl and its analogs at low parts-per-quadrillion (ppqv) levels. The determination of vapor pressure values of fentanyl is necessary to understand potential vapor concentrations that may be available for detection. In this paper, vapor pressures of fentanyl free base and fentanyl hydrochloride salt (a common form of the illicit drug) were measured as a function of temperature at or near ambient conditions using the transpiration (gas saturation) method and AFT-MS. Based on our measurements, the vapor pressure of fentanyl at 25 °C is 9.0 × 10-14 atm (90 ppqv), and the vapor pressure of fentanyl hydrochloride at 25 °C is 1.8 × 10-17 atm (0.018 ppqv). We also demonstrate non-contact, real-time vapor detection of fentanyl. Preconcentration of vapors can further extend the detection capabilities. The collection, desorption, and detection of fentanyl vapors at ambient conditions was demonstrated for sampling times of seconds to an hour resulting in increased signal. AFT-MS is a viable detection method of fentanyl and other drugs for screening of packages and cargo.

Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.

Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.

Vapor Pressures of RDX and HMX Explosives Measured at and Near Room Temperature: 1,3,5-Trinitro-1,3,5-triazinane and 1,3,5,7-Tetranitro-1,3,5,7-tetrazocane.

Knowing accurate saturated vapor pressures of explosives at ambient conditions is imperative to provide realistic boundaries on available vapor for ultra-trace detection. In quantifying vapor content emanating from low-volatility explosives, we observed discrepancies between the quantity of explosive expected based on literature vapor pressure values and the amount detected near ambient temperatures. Most vapor pressure measurements for low-volatility explosives, such as RDX (1,3,5-trinitro-1,3,5-triazinane) and HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane), have been made at temperatures far exceeding 25 °C and linear extrapolation of these higher temperature trends appears to underestimate vapor pressures near room temperature. Our goal was to measure vapor pressures as a function of temperature closer to ambient conditions. We used saturated RDX and HMX vapor sources at controlled temperatures to produce vapors that were then collected and analyzed via atmospheric flow tube-mass spectrometry (AFT-MS). The parts-per-quadrillion (ppqv) sensitivity of AFT-MS enabled measurement of RDX vapor pressures at temperatures as low as 7 °C and HMX vapor pressures at temperatures as low as 40 °C for the first time. Furthermore, these vapor pressures were corroborated with analysis of vapor generated by nebulizing low concentration solutions of RDX and HMX. We report updated vapor pressure values for both RDX and HMX. Based on our measurements, the vapor pressure of RDX at 25 °C is 3 ± 1 × 10-11 atm (i.e., 30 parts per trillion by volume, pptv), the vapor pressure of HMX is 1.0 ± 0.6 × 10-14 atm (10 ppqv) at 40 °C and, with extrapolation, HMX has a vapor pressure of 1.0 ± 0.6 × 10-15 atm (1.0 ppqv) at 25 °C.

Non-contact vapor detection of illicit drugs via atmospheric flow tube-mass spectrometry.

Real-time, non-contact detection of illicit drugs is a desirable goal for the interdiction of these controlled substances, but the relatively low vapor pressures of such species present a challenge for trace vapor detection technologies. The introduction of atmospheric flow tube-mass spectrometry (AFT-MS), which has previously been demonstrated to detect gas-phase analytes at low parts-per-quadrillion levels for explosives and organophosphorus compounds, also enables the potential for non-contact drug detection. With AFT-MS, direct vapor detection of cocaine and methamphetamine from ∼5 µg residues at room temperature is demonstrated herein. Furthermore, thermal desorption of low- to sub-picogram levels of cocaine, methamphetamine, fentanyl, and heroin is observed via AFT-MS using a carrier flow rate of several L min-1 of air. These low levels can permit non-contact sampling through collection of vapor, effectively preconcentrating the analyte before desorption and analysis. Quantitative evaluation of the thermal desorption approach has yielded limits of detection (LODs) on the order of 10 fg for cocaine and fentanyl, 100 fg for methamphetamine, and 1.6 pg for heroin. The LOD for heroin was lowered to 300 fg by using tributyl phosphate as a dopant to form a proton-bound heterodimer with heroin. When used with AFT-MS, the intentional formation of specific drug-dopant adducts has the potential to enhance detection limits and selectivity of additional drug species. Species that are prone to form adducts present a challenge to analysis, but that difficulty can be overcome by the intentional addition of a dopant. Molecules unlikely to form adducts will remain essentially unimpacted, but the adduct-forming species will interact with the dopant to compress the analyte signal into a single peak. This approach would be valuable in the application of non-contact screening for illicit substances via vapor collection followed by thermal desorption for analysis.

Human Occupancy Detection via Passive Cognitive Radio.

Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects-not only in residential rooms-but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption.

Selective Reagent Ions for the Direct Vapor Detection of Organophosphorus Compounds Below Parts-per-Trillion Levels.

Real-time low to sub parts-per-trillion (pptv) vapor detection of some organophosphorous compounds (OPCs) is demonstrated with an atmospheric flow tube-mass spectrometer. The chemical species investigated included dimethyl methylphosphonate, triethyl phosphate, and tributylphosphate. The atmospheric flow tube provides ambient chemical ionization with up to several seconds of ionization time. With sensitivities in the parts-per-quadrillion (ppqv) range, there are many background contaminants competing for charge with the target analytes. Initially, the OPCs were not observable in direct room air analysis, presumably due to other trace components possessing higher proton affinities. However, the addition of a trialkylamine as a dopant chemical served to provide a single reagent ion that also formed a proton-bound heterodimer with the OPCs. These asymmetric proton-bound dimers had sufficiently high hydrogen bond energy to allow the cluster to remain intact during the analysis time of several seconds. Changes in stability were observed for some of these asymmetric proton-bound dimers with a shorter half-life for adducts with a larger proton affinity differences between the amine and the OPC. Detection levels approaching low pptv to high ppqv were correlated by three different methods, including use of a permeation tube, direct injection of a fixed mass into the sample air flow, and calculations based upon signal intensity ratios, reaction time, and an estimated reaction rate constant. A practical demonstration showed real-time monitoring of a laboratory environment initially with low pptv levels of vapor observed to decay exponentially over about an hour while returning to baseline levels.

Detection of Inorganic Salt-Based Homemade Explosives (HME) by Atmospheric Flow Tube-Mass Spectrometry.

Using a commercial mass spectrometer interfaced with an atmospheric flow tube (AFT) allowed for the detection of a variety of inorganic compounds used as oxidizers in homemade explosives (HMEs) at picogram levels. The AFT provides reaction times of between 3 and 5 s with flows of 6 L/min, enabling detection levels, after thermal desorption, similar to those previously demonstrated for RDX vapor in the low parts per quadrillion range. The thermal desorption of chlorate and perchlorate salts resulted in the production of the corresponding anions which have higher electron affinities than that of the nitrate reactant ions. A dielectric barrier discharge, used as the ionization source, produced the nitrate reactant ions. In some instances, the molecular salt formed adducts with the nitrate, chlorate, and/or perchlorate anions, giving insight into the original identity of the salt cation. Urea nitrate, guanidine nitrate, and potassium nitrate were also detected as adducts with the nitrate reactant ion. The direct room-temperature vapor detection of urea nitrate and hydrogen peroxide, which have relatively high vapor pressures compared to the other salts in this study, is also demonstrated. Room-temperature vapor detection of chlorate and perchlorate salts is possible by the addition of a dilute acid which converts the salt into a more volatile acidic form. A discussion of the instrumentation, methods used, and the ionization chemistry is provided.

Bayesian Integration and Classification of Composition C-4 Plastic Explosives Based on Time-of-Flight-Secondary Ion Mass Spectrometry and Laser Ablation-Inductively Coupled Plasma Mass Spectrometry.

Time-of-flight-secondary ion mass spectrometry (TOF-SIMS) and laser ablation-inductively coupled plasma mass spectrometry (LA-ICPMS) were used for characterization and identification of unique signatures from a series of 18 Composition C-4 plastic explosives. The samples were obtained from various commercial and military sources around the country. Positive and negative ion TOF-SIMS data were acquired directly from the C-4 residue on Si surfaces, where the positive ion mass spectra obtained were consistent with the major composition of organic additives, and the negative ion mass spectra were more consistent with explosive content in the C-4 samples. Each series of mass spectra was subjected to partial least squares-discriminant analysis (PLS-DA), a multivariate statistical analysis approach which serves to first find the areas of maximum variance within different classes of C-4 and subsequently to classify unknown samples based on correlations between the unknown data set and the original data set (often referred to as a training data set). This method was able to successfully classify test samples of C-4, though with a limited degree of certainty. The classification accuracy of the method was further improved by integrating the positive and negative ion data using a Bayesian approach. The TOF-SIMS data was combined with a second analytical method, LA-ICPMS, which was used to analyze elemental signatures in the C-4. The integrated data were able to classify test samples with a high degree of certainty. Results indicate that this Bayesian integrated approach constitutes a robust classification method that should be employable even in dirty samples collected in the field.

How efficiently do corn- and soybean-based cropping systems use water? A systems modeling analysis.

Agricultural systems are being challenged to decrease water use and increase production while climate becomes more variable and the world's population grows. Low water use efficiency is traditionally characterized by high water use relative to low grain production and usually occurs under dry conditions. However, when a cropping system fails to take advantage of available water during wet conditions, this is also an inefficiency and is often detrimental to the environment. Here, we provide a systems-level definition of water use efficiency (sWUE) that addresses both production and environmental quality goals through incorporating all major system water losses (evapotranspiration, drainage, and runoff). We extensively calibrated and tested the Agricultural Production Systems sIMulator (APSIM) using 6 years of continuous crop and soil measurements in corn- and soybean-based cropping systems in central Iowa, USA. We then used the model to determine water use, loss, and grain production in each system and calculated sWUE in years that experienced drought, flood, or historically average precipitation. Systems water use efficiency was found to be greatest during years with average precipitation. Simulation analysis using 28 years of historical precipitation data, plus the same dataset with ± 15% variation in daily precipitation, showed that in this region, 430 mm of seasonal (planting to harvesting) rainfall resulted in the optimum sWUE for corn, and 317 mm for soybean. Above these precipitation levels, the corn and soybean yields did not increase further, but the water loss from the system via runoff and drainage increased substantially, leading to a high likelihood of soil, nutrient, and pesticide movement from the field to waterways. As the Midwestern United States is predicted to experience more frequent drought and flood, inefficiency of cropping systems water use will also increase. This work provides a framework to concurrently evaluate production and environmental performance of cropping systems.

Internal Domains of Natural Porous Media Revealed: Critical Locations for Transport, Storage, and Chemical Reaction.

Internal pore domains exist within rocks, lithic fragments, subsurface sediments, and soil aggregates. These domains, termed internal domains in porous media (IDPM), represent a subset of a material's porosity, contain a significant fraction of their porosity as nanopores, dominate the reactive surface area of diverse media types, and are important locations for chemical reactivity and fluid storage. IDPM are key features controlling hydrocarbon release from shales in hydraulic fracture systems, organic matter decomposition in soil, weathering and soil formation, and contaminant behavior in the vadose zone and groundwater. Traditionally difficult to interrogate, advances in instrumentation and imaging methods are providing new insights on the physical structures and chemical attributes of IDPM, and their contributions to system behaviors. Here we discuss analytical methods to characterize IDPM, evaluate information on their size distributions, connectivity, and extended structures; determine whether they exhibit unique chemical reactivity; and assess the potential for their inclusion in reactive transport models. Ongoing developments in measurement technologies and sensitivity, and computer-assisted interpretation will improve understanding of these critical features in the future. Impactful research opportunities exist to advance understanding of IDPM, and to incorporate their effects in reactive transport models for improved environmental simulation and prediction.

From image pair to a computer generated hologram for a real-world scene.

We propose an approach to produce computer generated holograms (CGHs) from image pairs of a real-world scene. The ratio of the three-dimensional (3D) physical size of the object is computed from the image pair to provide the correct depth cue. A multilayer wavefront recording plane method completed with a two-stage occlusion culling process is carried out for wave propagation. Multiple holograms can be generated by propagating the wave toward the desired angles, to cover the circular views that are wider than the viewing angle restricted by the wavelength and pitch size of a single hologram. The impact of the imperfect depth information extracted from the image pair on CGH is examined. The approach is evaluated extensively on image pairs of real-world 3D scenes, and the results demonstrate that the circular-view CGH can be produced from a pair of stereo images using the proposed approach.

Optimizing detection of RDX vapors using designed experiments for remote sensing.

This paper presents results of designed experiments performed to study the effect of four factors on the detection of RDX vapors from desorption into an atmospheric flow tube mass spectrometer (AFT-MS). The experiments initially included four independent factors: gas flow rate, desorption current, solvent evaporation time and RDX mass. The values of three detection responses, peak height, peak width, and peak area were recorded but only the peak height response was analyzed. Results from the first block of experiments indicated that solvent evaporation time was not statistically significant at the 95% confidence level. A second round of experiments was designed and executed using flow rate, current, and RDX mass as factors and the results were used to create a model to predict conditions resulting in maximum peak height. Those conditions were confirmed experimentally and used to obtain data for a calibration model. The calibration model represented RDX amounts ranging from 1 to 25 pg desorbed into an air flow of 7 L min(-1). Air samples from a shipping container that held 2 closed explosive storage magazines were collected on metal filaments for varying amounts for time ranging from 5 to 90 minutes. RDX was detected from all of the filaments sampled by desorption into the AFT-MS. From the calibration model, RDX vapor concentrations within the shipping container were calculated to be in the range of 1 to 50 parts-per-quadrillion (ppqv) from data collected on 2 separate days.

Conceptualization, design, and construction of a novel insect mass trapping device: the USDA Biomass Harvest Trap (USDA-BHT).

The use of insects as animal feed has the potential to be a green revolution for animal agriculture as insects are a rich source of high-quality protein. Insect farming must overcome challenges such as product affordability and scalability before it can be widely incorporated as animal feed. An alternative is to harvest insect pests from the environment using mass trapping devices and use them as animal feed. For example, intensive agricultural environments generate large quantities of pestiferous insects and with the right harvest technologies, these insects can be used as a protein supplement in traditional animal daily rations. Most insect trapping devices are limited by the biomass they can collect. In that context, and with the goal of using wild collected insects as animal feed, the United States Department of Agriculture-Biomass Harvest Trap (USDA-BHT) was designed and built. The USDA-BHT is a valuable mass trapping device developed to efficiently attract, harvest, and store flying insects from naturally abundant agricultural settings. The trap offers a modular design with adjustable capabilities, and it is an inexpensive device that can easily be built with commonly available parts and tools. The USDA-BHT is also user-friendly and has customizable attractants to target various pest species.

Standoff trace explosives vapor detection at meter distances.

Vapor detection is a noncontact sampling method, which is a less invasive means of explosives screening than physical swiping. Explosive vapor detection is a challenge due to the low levels of vapors available for detection. This study demonstrates that the parts-per-quadrillion sensitivity of atmospheric flow tube-mass spectrometry (AFT-MS) combined with a high-volume air sampler enables standoff detection of trace explosives vapor at distances of centimeters to meters. Standoff detection of explosives vapor was possible both upstream and downstream of the vapor source relative to room air currents. RDX vapor from a saturated source was detected at up to 2.5 m. Vapors from RDX residue and nitroglycerin residue were detected at distances up to 0.5 m. The sampling can be optimized by accounting for air movement in the room or environment, which could further extend standoff detection distances. Using AFT-MS with a high-volume sampler could also be effective for standoff vapor detection of drugs and additional chemical threats and could be useful for security screening applications such as at mail facilities, border crossings, and security checkpoints.

Elucidating the Gas-Phase Behavior of Nitazene Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry.

2-Benzylbenzimidazoles, or "nitazenes", are a class of novel synthetic opioids (NSOs) that are increasingly being detected alongside fentanyl analogs and other opioids in drug overdose cases. Nitazenes can be 20× more potent than fentanyl but are not routinely tested for during postmortem or clinical toxicology drug screens; thus, their prevalence in drug overdose cases may be under-reported. Traditional analytical workflows utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) often require additional confirmation with authentic reference standards to identify a novel nitazene. However, additional analytical measurements with ion mobility spectrometry (IMS) may provide a path toward reference-free identification, which would greatly accelerate NSO identification rates in toxicology laboratories. Presented here are the first IMS and collision cross section (CCS) measurements on a set of fourteen nitazene analogs using a structures for lossless ion manipulations (SLIM)-orbitrap MS. All nitazenes exhibited two high intensity baseline-separated IMS distributions, which fentanyls and other drug and druglike compounds also exhibit. Incorporating water into the electrospray ionization (ESI) solution caused the intensities of the higher mobility IMS distributions to increase and the intensities of the lower mobility IMS distributions to decrease. Nitazenes lacking a nitro group at the R1 position exhibited the greatest shifts in signal intensities due to water. Furthermore, IMS-MS/MS experiments showed that the higher mobility IMS distributions of all nitazenes possessing a triethylamine group produced fragment ions with m/z 72, 100, and other low intensity fragments while the lower mobility IMS distributions only produced fragment ions with m/z 72 and 100. The IMS, solvent, and fragmentation studies provide experimental evidence that nitazenes potentially exhibit three gas-phase protomers. The cyclic IMS capability of SLIM was also employed to partially resolve four sets of structurally similar nitazene isomers (e.g., protonitazene/isotonitazene, butonitazene/isobutonitazene/secbutonitazene), showcasing the potential of using high-resolution IMS separations in MS-based workflows for reference-free identification of emerging nitazenes and other NSOs.

Harvesting insect pests for animal feed: potential to capture an unexploited resource.

The demand for animal protein grows as the human population increases. Technological and genetic advances in traditional animal agriculture will not produce enough protein to meet future needs without significant innovations such as the use of insects as protein sources. Insect farming is growing insects, whereas insect harvesting is collecting insects from their natural habitats to produce high-quality protein for animal feed or human food. Intensive agricultural environments produce tremendous quantities of pestiferous insects and with the right harvest technologies these insects can be used as a protein supplement in traditional animal daily rations. An avenue to exploit these insects is to use traps such as the United States Department of Agriculture-Biomass Harvest Trap (USDA-BHT) to efficiently attract, harvest, and store insects from naturally abundant agricultural settings. The modular design allows for a low cost, easy to build and fix device that is user friendly and has customizable attractants to target various pest species. Although insect harvesting faces substantial challenges, including insect biomass quantity, seasonal abundance and preservation, food safety, and economic and nutritional evaluation, the potential for utilizing these pests for protein shows tremendous promise. In this forum, insect harvesting is discussed, including its potential, limitations, challenges, and research needs. In addition, the use of a mass trapping device is discussed as a tool to increase the biomass of insects collected from the environment.

Assessment of the USDA Biomass Harvest Trap (USDA-BHT) device as an insect harvest and mosquito surveillance tool.

Insects are a promising source of high-quality protein, and the insect farming industry will lead to higher sustainability when it overcomes scaling up, cost effectiveness, and automation. In contrast to insect farming (raising and breeding insects as livestock), wild insect harvesting (collecting agricultural insect pests), may constitute a simple sustainable animal protein supplementation strategy. For wild harvest to be successful sufficient insect biomass needs to be collected while simultaneously avoiding the collection of nontarget insects. We assessed the performance of the USDA Biomass Harvest Trap (USDA-BHT) device to collect flying insect biomass and as a mosquito surveillance tool. The USDA-BHT device was compared to other suction traps commonly used for mosquito surveillance (Centers for Disease Control and Prevention (CDC) light traps, Encephalitis virus surveillance traps, and Biogents Sentinel traps). The insect biomass harvested in the USDA-BHT was statistically higher than the one harvested in the other traps, however the mosquito collections between traps were not statistically significantly different. The USDA-BHT collected some beneficial insects, although it was observed that their collection was minimized at night. These findings coupled with the fact that sorting time to separate the mosquitoes from the other collected insects was significantly longer for the USDA-BHT, indicate that the use of this device for insect biomass collection conflicts with its use as an efficient mosquito surveillance tool. Nevertheless, the device efficiently collected insect biomass, and thus can be used to generate an alternative protein source for animal feed.

Identification of Unique Fragmentation Patterns of Fentanyl Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry.

The opioid crisis in the United States is being fueled by the rapid emergence of new fentanyl analogs and precursors that can elude traditional library-based screening methods, which require data from known reference compounds. Since reference compounds are unavailable for new fentanyl analogs, we examined if fentanyls (fentanyl + fentanyl analogs) could be identified in a reference-free manner using a combination of electrospray ionization (ESI), high-resolution ion mobility (IM) spectrometry, high-resolution mass spectrometry (MS), and higher-energy collision-induced dissociation (MS/MS). We analyzed a mixture containing nine fentanyls and W-15 (a structurally similar molecule) and found that the protonated forms of all fentanyls exhibited two baseline-separated IM distributions that produced different MS/MS patterns. Upon fragmentation, both IM distributions of all fentanyls produced two high intensity fragments, resulting from amine site cleavages. The higher mobility distributions of all fentanyls also produced several low intensity fragments, but surprisingly, these same fragments exhibited much greater intensities in the lower mobility distributions. This observation demonstrates that many fragments of fentanyls predominantly originate from one of two different gas-phase structures (suggestive of protomers). Furthermore, increasing the water concentration in the ESI solution increased the intensity of the lower mobility distribution relative to the higher mobility distribution, which further supports that fentanyls exist as two gas-phase protomers. Our observations on the IM and MS/MS properties of fentanyls can be exploited to positively differentiate fentanyls from other compounds without requiring reference libraries and will hopefully assist first responders and law enforcement in combating new and emerging fentanyls.