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At least 613 records · Page 34

Colossal Magnetoresistance without Mixed Valence in a Layered Phosphide Crystal

Materials with strong magnetoresistive responses are the backbone of spintronic technology, magnetic sensors, and hard drives. Among them, manganese oxides with a mixed valence and a cubic perovskite structure stand out due to their colossal magnetoresistance (CMR). A double exchange interaction underlies the CMR in manganates, whereby charge transport is enhanced when the spins on neighboring Mn 3+ and Mn 4+ ions are parallel. Prior efforts to find different materials or mechanisms for CMR resulted in a much smaller effect. Here an enormous CMR at low temperatures in EuCd 2 P 2 without manganese, oxygen, mixed valence, or cubic perovskite structure is shown. EuCd 2 P 2 has a layered trigonal lattice and exhibits antiferromagnetic ordering at 11 K. The magnitude of CMR (10(4)%) in as-grown crystals of EuCd 2 P 2 rivals the magnitude in optimized thin films of manganates. In this work, the magnetization, transport, and synchrotron X-ray data suggest that strong magnetic fluctuations are responsible for this phenomenon. The realization of CMR at low temperatures without heterovalency leads to a new regime for materials and technologies related to antiferromagnetic spintronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advanced Natural Gas Reciprocating Engine(s)

Energy independence and fuel savings are hallmarks of the nation’s energy strategy. The advancement of natural gas reciprocating engine power generation technology is critical to the nation’s future. A new engine platform that meets the efficiency, emissions, fuel flexibility, cost and reliability/maintainability targets will enable American manufacturers to have highly competitive products that provide substantial environmental and economic benefits in the US and in international markets. Along with Cummins and Waukesha, Caterpillar participated in a multiyear cooperative agreement with the Department of Energy to create a 50% efficiency natural gas powered reciprocating engine system with a 95% reduction in NOx emissions by the year 2013. This platform developed under this agreement will be a significant contributor to the US energy strategy and will enable gas engine technology to remain a highly competitive choice, meeting customer cost of electricity targets, and regulatory environmental standard. Engine development under the Advanced Reciprocating Engine System (ARES) program was divided into phases, with the ultimate goal being approached in a series of incremental steps. This incremental approach would promote the commercialization of ARES technologies as soon as they emerged from development and would provide a technical and commercial foundation of later-developing technologies. Demonstrations of the Phase I and Phase II technology were completed in 2004 and 2008, respectively. Program tasks in Phase III included component and system development and testing from 2009-2012. Two advanced ignition technology evaluations were investigated under the ARES program: laser ignition and distributed ignition (DIGN). In collaboration with Colorado State University (CSU), a laser ignition system was developed to provide ignition at lean burn and high boost conditions. Much work has been performed in Caterpillar’s DIGN program under the ARES program. This work has consisted of both modeling and single cylinder engine experiments to quantify DIGN performance. The air handling systems of natural gas engines dissipate a percentage of available energy as a result of both flow losses and turbomachinery inefficiencies. An analytical study was initiated to increase compressor efficiency by employing a 2-stage inter-cooled compressor. Caterpillar also studied a turbo-compound system that employs a power turbine to recover energy from the exhaust gases for improved engine efficiency. Several other component and system investigations were undertaken during the final phase of the program to reach the ultimate ARES goals. An intake valve actuation system was developed and tested to improve engine efficiency, durability and load acceptance. Analytical modeling and materials testing were performed to evaluate the performance of steel pistons and compacted graphite iron cylinder head. Effort was made to improve the detonation sensing system by studying and comparing the performance of different pressure sensors. To reduce unburned hydrocarbon emissions, different camshafts were designed and built to investigate the effect of exhaust valve opening timing and value overlap. 1-D & 3-D coupled simulation was used to study intake and exhaust manifold dynamics with the goal of reducing load in-balance between cylinders. Selective catalytic reduction with on-board reductant generation to reduce NOx emissions was also engine tested. An effective mean to successfully deploy ARES technologies into the energy markets is to deploy demonstration projects in the field. In 2010, NETL and Caterpillar agreed to include a new “opportunity fuel” deliverable and two field demonstrations in the ARES program. An Organic Rankine Cycle system was designed with production intent incorporating lessons learned from the Phase II demonstration. Unfortunately, business conditions caused Caterpillar to cancel this demonstration in 2011. Nonetheless, Caterpillar partnered with a local dealer to deploy an ARES class engine using syngas from a biomass gasifier as the DE-FC26-01CH11079 primary combustion fuel in Gleason, TN. Upon the successful start-up and commissioning of the demonstration unit, ownership of the system was transferred to the dealer. In order to further our understanding of syngas combustion, a fundamental combustion study on syngas combustion at high pressure and lean condition was conducted through the collaboration with University of Southern California. A Methane program was also developed to rate engine performance for various compositions of syngas using empirical data obtained at CSU. While much work remains in terms of extending and integrating these developments into commercial products, it is evident that engine manufacturers on our own or through private consortium efforts could not have overcome the financial hurdles to drive these improvements into reciprocating engine and system capabilities, helping maintain the natural gas reciprocating engine power generation technology as a strong option for electric power markets, both in the United States and worldwide.

20 FOSSIL-FUELED POWER PLANTS↗

CW EC-QCL-based sensor for simultaneous detection of H 2 O, HDO, N 2 O and CH 4 using multi-pass absorption spectroscopy

A sensor system based on a continuous wave, external-cavity quantum-cascade laser (CW EC-QCL) was demonstrated for simultaneous detection of atmospheric H 2 O, HDO, N 2 O and CH 4 using a compact, dense pattern multi-pass gas cell with an effective path-length of 57.6 m. The EC-QCL with a mode-hop-free spectral range of 1225-1285 cm -1 operating at similar to 7.8 mu m was scanned covering four neighboring absorption lines, for H 2 O at 1281.161 cm -1 , HDO at 1281.455 cm -1 , N 2 O at 1281.53 cm -1 and CH 4 at 1281.61 cm -1 . A first-harmonic-normalized wavelength modulation spectroscopy with second-harmonic detection (WMS-2f/1f) strategy was employed for data processing. An Allan-Werle deviation analysis indicated that minimum detection limits of 1.77 ppmv for H 2 O, 3.92 ppbv for HDO, 1.43 ppbv for N 2 O, and 2.2 ppbv for CH 4 were achieved with integration times of 50-s, 50-s, 100-s and 129-s, respectively. In conclusion, experimental measurements of ambient air are also reported.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Verification of a two-dimensional infiltration model for the resin transfer molding process

A two-dimensional finite element model for the infiltration of a dry textile preform by an injected resin was verified. The model, which is based on the finite element/control volume technique, determines the total infiltration time and the pressure increase at the mold inlet associated with the RTM process. Important input data for the model are the compaction and permeability behavior of the preform along with the kinetic and rheological behavior of the resin. The compaction behavior for several textile preforms was determined by experimental methods. A power law regression model was used to relate fiber volume fraction to the applied compaction pressure. Results showed a large increase in fiber volume fraction with the initial application of pressure. However, as the maximum fiber volume fraction was approached, the amount of compaction pressure required to decrease the porosity of the preform rapidly increased. Similarly, a power law regression model was used to relate permeability to the fiber volume fraction of the preform. Two methods were used to measure the permeability of the textile preform. The first, known as the steady state method, measures the permeability of a saturated preform under constant flow rate conditions. The second, denoted the advancing front method, determines the permeability of a dry preform to an infiltrating fluid. Water, corn oil, and an epoxy resin, Epon 815, were used to determine the effect of fluid type and viscosity on the steady state permeability behavior of the preform. Permeability values measured with the different fluids showed that fluid viscosity had no influence on the permeability behavior of 162 E-glass and TTI IM7/8HS preforms. Permeabilities measured from steady state and advancing front experiments for the warp direction of 162 E-glass fabric were similar. This behavior was noticed for tests conducted with corn oil and Epon 815. Comparable behavior was observed for the warp direction of the TTI IM7/8HS preform and corn oil. Mold filling and flow visualization experiments were performed to verify the analytical computer model. Frequency dependent electromagnetic sensors were used to monitor the resin flow front as a function of time. For the flow visualization tests, a video camera and high resolution tape recorder were used to record the experimental flow fronts. Comparisons between experimental and model predicted flow fronts agreed well for all tests. For the mold filling tests conducted at constant flow rate injection, the model was able to accurately predict the pressure increase at the mold inlet during the infiltration process. A kinetics model developed to predict the degree of cure as a function of time for the injected resin accurately calculated the increase in the degree of cure during the subsequent cure cycle.

Hammond, Vincent H.↗

Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

In this article, we report the growth of Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous mpetus in improving device and sensor performance by compensating for limi tations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets con taining PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. Additionally, the effect of detector resolution on the efficiency of the CNN was also explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. Here, this study focuses on the component of “driving volatility matrix” related to specific normal and safety-critical events, named “event-based volatility.” The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

33 ADVANCED PROPULSION SYSTEMS↗

LumaCam: a novel class of position-sensitive event mode particle detectors using scintillator screens

A new type of position-sensitive detectors is gaining attention in the neutron community. They are scintillator based detectors that detect the scintillation light on an individual photon basis via an image intensifier and a fast image sensor. Their readout operates in event mode i.e. it produces information about individual neutron interactions, reconstructed from the sensor data, thus enabling to achieve superior spatial and temporal resolutions compared to regular detectors. Although the development of current detectors is focused on neutrons, the concept is also applicable to the detection of other particles such as high-energy photons. This document provides a description on how these detectors are built, how they operate, and what their characteristics are. An example of a detector implementation based on a Timepix3 chip is described to illustrate the detector concept. This includes a detailed description of the algorithm that reconstructs the neutron interactions from the sensor data, one of the core components that sets it apart from established scintillator-based imaging detectors. Energy-resolved epithermal neutron radiography was performed at the ISIS EMMA beamline with this detector, illustrating some of the fundamental differences in the data that can be produced with the new type of detector compared to more established types of scintillator based neutron detectors. The term LumaCam is proposed to refer to this new class of position-sensitive event-mode detectors.

47 OTHER INSTRUMENTATION↗

Fueling the Future: The Emergence of Self-Powered Enzymatic Biofuel Cell Biosensors

Self-powered biosensors are innovative devices that can detect and analyze biological or chemical substances without the need for an external power source. These biosensors can convert energy from the surrounding environment or the analyte itself into electrical signals for sensing and data transmission. The self-powered nature of these biosensors offers several advantages, such as portability, autonomy, and reduced waste generation from disposable batteries. They find applications in various fields, including healthcare, environmental monitoring, food safety, and wearable devices. While self-powered biosensors are a promising technology, there are still challenges to address, such as improving energy efficiency, sensitivity, and stability to make them more practical and widely adopted. This review article focuses on exploring the evolving trends in self-powered biosensor design, outlining potential advantages and limitations. With a focal point on enzymatic biofuel cell power generation, this article describes various sensing mechanisms that employ the analyte as substrate or fuel for the biocatalyst’s ability to generate current. Technical aspects of biofuel cells are also examined. Research and development in the field of self-powered biosensors is ongoing, and this review describes promising areas for further exploration within the field, identifying underexplored areas that could benefit from further investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

University of Colorado and Black Swift Technologies RPAS-based measurements of the lower atmosphere during LAPSE-RATE

Abstract. Between 14 and 20 July 2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30 h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914 m a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27 May 2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ammonia sensing properties of V-doped ZnO:Ca nanopowders prepared by sol–gel synthesis

V-doped ZnO:Ca nanopowders with different V loading were prepared by sol–gel synthesis and successive drying in ethanol under supercritical conditions. Characterization data of nanopowders annealed at 700 °C in air, revealed that they have the wurtzite structure. Raman features of V-doped ZnO:Ca samples were found to be substantially modified with respect to pure ZnO or binary ZnO:Ca samples, which indicate the substitution of vanadium ions in the ZnO lattice. The ammonia sensing properties of V-doped ZnO:Ca thick films were also investigated. The results obtained demonstrate the possibility of a fine tuning of the sensing characteristics of ZnO-based sensors by Ca and V doping. In particular, their combined effect has brought to an enhanced response towards NH{sub 3} compared to bare ZnO and binary V-ZnO and Ca-ZnO samples. Raman investigation suggested that the presence of Ca play a key role in enhancing the sensor response in these ternary composite nanomaterials. - Graphical abstract: V-doped ZnO:Ca nanopowders prepared by sol–gel synthesis possess enhanced sensing characteristics towards NH{sub 3} compared to bare ZnO. - Highlights: • V-doped ZnO:Ca nanopowders with different V loading were prepared by sol–gel synthesis. • Raman features of V-doped ZnO:Ca samples indicate the substitution of V ions in the ZnO lattice. • Combined effects of dopants have brought to an enhanced response to NH{sub 3} compared to ZnO. • Ca play a key role in enhancing the sensor response of ternary V-doped ZnO:Ca composites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗

Determination of interstellar He parameters using five years of data from the IBEX: Beyond closed form approximations

Interstellar He represents a key sample of interstellar matter that, due to its high first ionization potential, survives the journey from beyond our solar system’s heliospheric boundaries to Earth. Ongoing analysis of interstellar neutral (ISN) He atoms by the Interstellar Boundary Explorer (IBEX) has resulted in a growing sophistication in our understanding of the local interstellar flow. A key feature of the IBEX observations near perihelion of the ISN trajectories is a narrow “tube” of approximately degenerate interstellar parameters. These degenerate solutions provide a tightly coupled relationship between the interstellar flow longitude and latitude, speed, and temperature. However, the IBEX analysis resulting in a specific solution for the inflow longitude, inflow speed, temperature, and inflow latitude was accompanied by a sizeable uncertainty along the parameter tube. Here, we use the three-step method to find the interstellar parameters: (1) the ISN He peak rate in ecliptic longitude uniquely determines a relation (as part of the tube in parameter space) between the longitude λ{sub ISN∞} and the speed V{sub ISN∞} of the He ISN flow at infinity; (2) the ISN He peak latitude (on the great circle swept out in each spin) is compared to simulations to derive unique values for λ{sub ISN∞} and V{sub ISN∞} along the parameter tube; and (3) the angular width of the He flow distributions as a function of latitude is used to derive the interstellar He temperature. For simulated peak latitudes, we use a relatively new analytical tool that traces He atoms from beyond the termination shock into the position of IBEX and incorporates the detailed response function of IBEX-Lo. By varying the interstellar parameters along the IBEX parameter tube, we find the specific parameters that minimize the chi-square difference between observations and simulations. The new computational tool for simulating neutral atoms through the integrated IBEX-Lo response function makes no assumptions or expansions with respect to the spin-axis pointing or frame of reference. Thus, we are able to move beyond closed-form approximations and utilize observations of interstellar He during the complete five year period from 2009 to 2013 when the primary component of interstellar He is most prominent. Chi-square minimization of simulations compared to observations results in a He ISN flow longitude of 75.°6 ± 1.°4, latitude of −5.°12 ± 0.°27, speed of 25.4 ± 1.1 km s{sup −1}, and temperature of 8000 ± 1300 K, where the uncertainties are related and apply along the IBEX parameter tube. This paper also provides documentation for a new release of ISN data and associated model runs.

79 ASTRONOMY AND ASTROPHYSICS↗

EXAMINING THE ROLE AND RESEARCH CHALLENGES OF SOCIAL MEDIA AS A TOOL FOR NONPROLIFERATION AND ARMS CONTROL TREATY VERIFICATION

Traditional arms control treaty verification activities typically involve a combination of technical measurements via physical and chemical sensors, state declarations, political agreements, and on-site inspections involving international subject matter experts. However, the ubiquity of the internet, and the electronic sharing of data that it enables, has made available a wealth of open source information with the potential to benefit verification efforts. Open source information is already being used by organizations such as the International Atomic Energy Agency to support the verification of state-declared information, prepare inspectors for in-field activities, and to maintain situational awareness . The recent explosion in social media use has opened new doors to exploring the attitudes, moods, and activities around a given topic. Social media platforms, such as Twitter, Facebook, and YouTube, offer an opportunity for individuals, as well as institutions, to participate in a global conversation at minimal cost. Social media data can also provide a more data-rich environment, with text data being augmented with images, videos, and location data. The research described in this paper investigates the utility of applying social media signatures as potential arms control and nonproliferation treaty verification tools and technologies, as determined through a series of case studies. The treaty relevant events that these case studies touch upon include detection of undeclared facilities or activities, determination of unknown events recorded by the International Monitoring System (IMS), and the global media response to the occurrence of an Indian missile launch. The case studies examine how social media can be used to fill an information gap and provide additional confidence to a verification activity. The case studies represent, either directly or through a proxy, instances where social media information may be available that could potentially augment the evaluation of an event. The goal of this paper is to instigate a discussion within the verification community as to where and how social media can be effectively utilized to complement and enhance traditional treaty verification efforts. In addition, this paper seeks to identify areas of future research and development necessary to adapt social media analytic tools and techniques, and to form the seed for social media analytics to aid and inform arms control and nonproliferation policymakers and analysts. While social media analysis (as well as open source analysis as a whole) will not ever be able to replace traditional arms control verification measures, they do supply unique signatures that can augment existing analysis.

nonproliferation, arms control, social media↗

Development and flight-testing of modular autonomous cultivation systems for biological plastics upcycling aboard the ISS

Cultivation of microorganisms in space has enormous potential to enable in-situ resource utilization (ISRU) Here, we develop an autonomous payload with fully programmable serial passaging and sample preservation, termed the Modular Open Biological Platform (MOBP), and flight-test the MOBP aboard the International Space Station (ISS) by conducting enzymatic and microbial plastics upcycling experiments. The MOBP is a compact, modular bioreactor system that allows for sustained microbial growth via automated media transfers, such as those for sample collection and storage for terrestrial analyses, and precise data monitoring from integrated sensors. The MOBP was flight-tested with two experiments designed to evaluate biological upcycling of the plastic poly(ethylene terephthalate) (PET). The bioproduct βKA can be polymerized into a nylon-6,6 analog with improved properties for use in the production of a variety of materials. We posit the MOBP will aid in democratizing the execution of synthetic biology in spaceflight towards enabling ISRU.

09 BIOMASS FUELS↗

Measurements from mobile surface vehicles during the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE)

Abstract. Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

How much information on precipitation is contained in satellite infrared imagery?

Satellite infrared (IR) brightness temperature providing information on the characteristics of cloud tops is one of the primary input data used for estimating precipitation from satellites. Thus, the natural questions to ask are: Does IR imagery contain more information about precipitation in some regions more than others? What are the factors that lead to such differences? Should we expect the amount of information provided by IR imagery to change with respect to temporal or spatial aggregation? Comprehensive analysis of the accuracy of satellite-based precipitation estimates obtained from any given algorithm may provide qualitative answers to the above questions. However, such an approach only offers conclusions within the limited context of the algorithm used to obtain precipitation estimates; that is, patterns caused by the inherent information content of IR imagery are inextricable from those induced by assumptions embedded in the precipitation estimation algorithm. Here, we address the abovementioned questions from a different perspective using an information-theoretic measure, free of structural assumptions and general to a wide range of relationships, to characterize the average and seasonal dependence between IR imagery provided by satellite sensors in the spectral range (10.7–11.5 μm) and precipitation rates in the contiguous United States (CONUS) across distinct temporal and spatial scales. We analyze a total of more than 1.3 billion pairs of IR and precipitation observations over CONUS, and we observe interesting patterns. First, we show that there is a strong inverse relationship (ρ = - 0.73) between the information content of IR data and the number of no-rain observations; however, its robustness varies regionally and seasonally with less significant correlation during the warm season. Second, we demonstrate that the intuitive relationship of increasing dependence between IR and precipitation as a result of temporal or spatial aggregation exhibits a diminishing returns behavior. For instance, temporal aggregation from 1 to 3 h increases the dependence approximately 7 times as much as temporal aggregation from 12 to 24 h. Furthermore, we quantitatively examine and re-confirm several statements previously reported in the literature such as the strong association between IR and precipitation in convective storms and the low association in orographic rainfall. Finally, we conclude by illustrating the potential of the analysis in diagnosis of operational algorithms for estimating precipitation from IR brightness temperature. The findings of this study pinpoint spatial domains and time scales at which IR is not an adequate proxy for estimating precipitation. Thus, they can potentially guide the development of operational algorithms that utilize satellite Infrared imagery for estimating, downscaling and data fusion of precipitation. Finally, the analysis presented here opens up the possibility of developing new methodologies for diagnosis of satellite-based precipitation estimation algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

NIAC Phase 1 Final Study Report on Titan Aerial Daughtercraft

Saturns giant moon Titan has become one of the most fascinating bodies in the Solar System. Even though it is a billion miles from Earth, data from the Cassini mission reveals that Titan has a very diverse, Earth-like surface, with mountains, fluvial channels, lakes, evaporite basins, plains, dunes, and seas [Lopes 2010] (Figure 1). But unlike Earth, Titans surface likely is composed of organic chemistry products derived from complex atmospheric photochemistry [Lorenz 2008]. In addition, Titan has an active meteorological system with observed storms and precipitation-induced surface darkening suggesting a hydrocarbon cycle analogous to Earths water cycle [Turtle 2011].Titan is the richest laboratory in the solar system for studying prebiotic chemistry, which makes studying its chemistry from the surface and in the atmosphere one of the most important objectives in planetary science [Decadal 2011]. The diversity of surface features on Titan related to organic solids and liquids makes long-range mobility with surface access important [Decadal 2011]. This has not been possible to date, because mission concepts have had either no mobility (landers), no surface access (balloons and airplanes), or low maturity, high risk, and/or high development costs for this environment (e,g. large, self-sufficient, long-duration helicopters). Enabling in situ mobility could revolutionize Titan exploration, similarly to the way rovers revolutionized Mars exploration. Recent progress on several fronts has suggested that small-scale rotorcraft deployed as daughtercraft from a lander or balloon mothercraft may be an effective, affordable approach to expanding Titan surface access. This includes rapid progress on autonomous navigation capabilities of such aircraft for terrestrial applications and on miniaturization, driven by the consumer mobile electronics market, of high performance of sensors, processors, and other avionics components needed for such aircraft. Chemical analysis, for example with a mass spectrometer, will be important to any Titan surface mission. Anticipating that it may be more practical to host chemical analysis instruments on a mothership than a daughtercraft, we defined system and mission concepts that deploy a small rotorcraft, termed a Titan Aerial Daughtercraft (TAD), from a lander or balloon to perform high-resolution imaging and mapping, potentially land to acquire microscopic images or other in situ measurements, and acquire samples to return to analytical instruments on the mothership. In principle, the ability to recharge batteries in TAD from a radioisotope or other long-lived power source on the mothership could enable multiple sorties. For a lander-based mission, a variety of landing sites is conceivable, including near lake margins, in dry lake beds, or in regions of plains, dunes, or putative cryovolanic or impact melt features. Such missions may require landing with greater precision than in previous missions (Huygens) and mission studies; this could also enhance the ability of TAD to reach interesting terrain from the landing site. Precision descent may also benefit balloon missions, with or without a daughtercraft, by increasing the probability that the balloon will drift over desired terrain early in its mission. Given these potential benefits, the overall concept studied here includes brief consideration of precision descent for landing or balloon deployment, followed by one or more sorties by a rotorcraft deployed from the mothership, with the ability to return to the mothership.

Saturn↗