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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A device for volatile organic compound (VOC) analysis from skin using heated dynamic headspace sampling

Abstract Human skin is an important source of volatile organic compounds (VOCs) offering noninvasive methods to gain clinical metabolite information. This work was focused on the development of a skin sampling device based on a dynamic headspace sampling method with the addition of temperature to increase VOC metabolite recovery. The device preconcentrates skin VOC emissions onto a sorbent substrate, which can either be preserved for offline analysis or attached to a real time sensor downstream. In this work, skin VOC samples were analyzed offline using thermal desorption-gas chromatography-mass spectrometry. A list of 10 common skin VOCs was pre-selected to optimize parameters of sampling time, sampling temperature, and sorbent selection. Overall, this study highlights an effective skin VOC sampling technology with a heating dimension (40 °C, rather than 30 °C or no heating) with a sampling time of 15 min (rather than 5 or 30 mins) and onto Tenax TA sorbent (rather than PDMS), which collectively increases the recovery of compounds with lower vapor pressure and decreases the observed variability in skin VOC measurements. Finally, a list of 79 skin VOC compounds were detected and identified within a cohort of 20 young, healthy volunteers.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Temperature Control of Crystal Optics for Ultrahigh-Resolution Applications

The temperature control of crystal optics is critical for ultrahigh resolution applications such as those used in meV-resolved Inelastic Scattering. Due to the low count rate and long acquisition time of these experiments, for 1-meV energy resolution, the absolute temperature stability of the crystal optics must be maintained below 4 mK to ensure the required stability of lattice constant, thereby ensuring the energy stability of the optics. Furthermore, the temperature control with sub-mK precision enables setting the absolute temperature of individual crystal, making it possible to align the reflection energy of each crystal’s rocking curve in sub-meV resolution thereby maximizing the combined efficiency of the crystal optics. In this contribution, we report the details of an EPICS control system using PT1000 sensors, Keithley 3706A 7.5 digits sensor scanner, and Wiener MPOD LV power supply for the analyzer crystals of the Inelastic X-ray Scattering (IXS) beamline 10-ID at NSLS-II**. We were able to achieve absolute temperature stability below 1 mK and sub-meV energy alignment for several asymmetrically cut analyzer crystals. The EPICS ePID record was used for the control of the power supplies based on the PT1000 sensor input that was read with 7.5 digits accuracy from the Keithley 3706A scanner. The system enhances the performance of the meV-resolved IXS spectrometer with currently a 1.4 meV total energy resolution and unprecedented spectral sharpness for studies of atomic dynamics in a broad range of materials.

Gofron, Kaz↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Nonradiative Properties of Self–Trapped Holes in Ultra–Wide Bandgap Gallium Oxide Film

The photoluminescence (PL) of self-trapped holes (STH) in ultra-wide bandgap β-Ga 2 O 3 is commonly its most dominant light emission and is an inherent property. Thus, gaining knowledge of the crystal dynamics that impact the PL properties is vital to sensor and other technologies. The PL, Raman-phonons, and their interactions are studied at an extreme temperature range of 77–622 K. The PL is studied up to the bandgap value of ≈5 eV. It is found that the high-energy Raman modes provide a major route to the nonradiative process of the PL via STH–phonon interaction with an activation energy of 72 meV. This dynamic is modeled with the configurational coordinate scheme at the strong phonon coupling limit. The exceptionally broad Gaussian PL linewidth manifests this coupling. The weak temperature response of the PL energy peak position indicates that the STH has characteristics of a deep-level defect. This contrasts with the large redshift of ≈220 meV of the optical gap of the film, ascertained from transmission. Unlike the temperature response of the high-energy phonons, the behavior of the low-energy phonons is found to follow the Bose–Einstein population increase, indicating no strong interaction with the STH.

Raman↗
DOE OSTI2023

Investigations of room temperature multiferroic and magneto-electric properties of (1-Φ) PZTFT-Φ CZFMO particulate composites

Multiferroic composites consisting of a single-phase multiferroic [0.6(PbZr 0.53 Ti 0.47 O 3 )-0.4(PbFe 0.5 Ta 0.5 )O 3 ] as a matrix and a magnetostrictive phase (Co 0.6 Zn 0.4 Fe 1.7 Mn 0.3 O 4 ) dispersed in the matrix are fabricated via hybrid synthesis technique. The structure and surface morphology studies using x-ray diffraction and field emission scanning electron microscopy techniques indicate the formation of 3-0 type particulate composites. Coexistence of soft-magnetic behavior and ferroelectric characteristics are confirmed for composites from magnetization vs magnetic field (M–H) and polarization vs electric field (P–E) measurements, respectively. Magneto-dielectric (MD) measurement shows significant changes in the dielectric properties with the application of a magnetic field, indicating the existence of strong MD behavior. The biquadratic nature of magneto-electric (ME) coupling is described by the Landau free energy equation arising from the strain transfer at the interfaces between the constituent phases. The direct magneto-electric voltage coefficient measurement also confirms very strong coupling between ferroelectricity and magnetism and supports the strain-mediated magneto-electric effect in composites. The Φ = 0.3 composite exhibits the maximum ME coefficient of 20.72 mV/cm Oe with M S = 24.62 emu/g, H C = 59.66 Oe, and piezoelectric coefficient value d 33 = 19 pC/N. In conclusion, the strong magneto-electric effect along with low dielectric loss at room temperature in these composites suggests their suitability for multifunctional magneto-electric device applications such as magnetic sensors, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Environmental monitoring data from the 2022-2023 field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains sensor data from programmed loggers as well as handheld moisture probes, including weather data, air and soil temperature, and soil volumetric water content. Data files and data dictionary(ies) are uploaded as .csv files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought. This environmental data was collected to provide context for the fungal community data and Pinus ponderosa physiological data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

IMS Rapid Response 2023-2 (RR 2023-RND-2): Magnetic order in layered bismuthide RhMnBi 3 (Final Report)

Microelectronics are technologies (mostly theoretical at this point) to create electronic circuits with components of atomic length scale. One of the key materials innovations enabling this technology is exfoliatable two-dimensional materials. These materials allow thin sheets of only a few atoms thick to be easily created in the laboratory. More and more of these materials are reported every year, but few have been characterized in detail. Thus a major goal in materials research in pursuit of microelectronic devices is characterizing newly discovered materials to evaluate their properties. The goal of this project was the measure and understand the magnetic order in a newly discovered van der Waals magnet RhMnBi 3 . Bulk property experiments indicated very complicated magnetic behavior, with multiple phase transitions between 2K and 300K (room temperature). This potentially indicated highly sensitive and tunable magnetism appropriate for single-layer magnetic switches and sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2021

Applications of Strain-Coupled Magnetoelectric Composites

This article deals with research, development and future directions of magnetoelectric composites for practical devices applications. In the past 20 years there has been a surge of research in the area of multiferroics (MF) and magnetoelectrics (ME) due to their potential to replace existing technologies based only on ferroelectric or ferromagnetic materials. Some of the magnetoelectric composites show exceptionally high potential in the area of magnetic field sensors, however, work remains before commercialization can be realized. The cross coupling among various ferroic parameters in magnetoelectric composites is several orders higher than single phase magetoelectrics, which make its favorable for low detection (nT or pT) magnetic field sensors. Advances in both layered structures or controlled three-dimensional matrix composites for applications as magnetoelectric nonvolatile memory elements are both required. Robust cross-coupling among various parameters with more than four logic states and its compatibility with complementary-symmetry metal–oxide–semiconductor (CMOS) technology are the main requirements for heterostructure magnetoelectric thin films. The major hurdles in the area of magnetoelectric nonvolatile memory elements are poor interfacial properties and weak magnetoelectric coupling for high density fast read and write processes. Another potential area is strained coupled magneto-electric composites where magnetostriction mediated dimensional change in the magnetic layer effectively modulate the change in the dimension of piezoelectric layers via piezostriction, which leads to a strong ME coupling where their coupling magnitude is sufficient for magnetic field sensors. Energy harvesters based on magnetoelectric composites are also intriguing concepts to capture various types of waste energy, in the form mechanical vibration, pressure, wind energy, hydrothermal and waste temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum-cascade-laser-absorption-spectroscopy diagnostic for temperature, pressure, and NO X 2 Π 1/2 at 500 kHz in shock-heated air at elevated pressures

The design, validation, and application of a quantum-cascade-laser-absorption-spectroscopy diagnostic for measuring gas temperature, pressure, and nitric oxide (NO) in high-temperature air are presented. A distributed-feedback quantum-cascade laser (QCL) centered near 1976 c m − <#comment/> 1 was used to scan across two transitions of NO in its ground electronic state ( X 2 Π <#comment/> 1 / 2 ). A measurement rate of 500 kHz was achieved using a single QCL by: (1) performing current modulation through a bias-tee, and (2) targeting closely spaced transitions with a large difference in lower-state energy. The diagnostic was validated in a mixture of 95% argon and 5% NO, which was shock-heated to ≈ <#comment/> 2000 to 3700 K. The average mean percent differences between laser-absorption-spectroscopy (LAS) measurements and predictions from shock-jump relations for temperature, pressure, and NO mole fraction were 3.1%, 4.1%, and 6.5%, respectively. The diagnostic was then applied to characterize shock-heated air at high temperatures (up to ≈ <#comment/> 5500 K ) and high pressures (up to 12 atm) behind either incident or reflected shocks. The LAS measurements were compared to theoretical predictions from shock-jump relations, pressure sensors mounted in the wall of the shock tube, and equilibrium values of the NO mole fraction. The average mean percent differences between LAS measurements and their aforementioned reference values were 3.2%, 10.8%, and 10.4% for temperature, pressure, and NO mole fraction, respectively. Last, a comparison between a measured NO mole fraction time history and a time-stepped homogeneous reactor simulation performed using two different chemical kinetics mechanisms is presented.

Gilvey, Jonathan J. (ORCID:0000000317664383)↗
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2022

Fault Characterization and Diagnostics Supporting Condition-Based Operation and Maintenance of Gas Turbine Engines

Condition-Based Operation and Maintenance (CBOM) is the state-of-the-art in maintenance approaches for gas turbine engines. CBOM applies engine sensor information to the optimization of future operation and maintenance procedures; this technique reduces costs for engine operators by minimizing unplanned outages and catastrophic engine degradation. However, due to the complexities of gas turbine operation and the lack of available engine sensors, further development is required to fully realize the benefits of CBOM. In the turbine section, rotating components interact with high temperature flows, which creates intense thermal and mechanical stresses. As a result, there are numerous mechanisms of component degradation in the turbine section. Furthermore, turbine components – like stator vanes and rotor blades – are among the most expensive in the engine because they are complex to design and manufacture. For these reasons, this dissertation addresses two main questions: (i) which parameters or faults within the turbine section are most important to monitor, and (ii) how can these parameters or faults be monitored in an engine-relevant environment? Although many turbine parameters and faults have been investigated in the open literature, there are some faults that are still not well understood. Rotor-casing eccentricity, which causes a non-constant blade tip clearance around the annulus, has not been investigated in terms of its effects on turbine efficiency. Therefore, the first study in this dissertation quantifies overall and local turbine efficiency for varying levels of rotor-casing eccentricity. Results showed negligible variations to overall turbine efficiency, meaning rotor-casing eccentricity only becomes relevant to CBOM when its severity causes rotordynamic issues. Purge flow is critical to turbine hardware longevity because it prevents ingestion of hot main gas path (MGP) flow into the under-platform regions. Despite its importance, there are currently no methods for monitoring purge flow performance in an engine environment. Therefore, the second study in this dissertation develops a predictive model for sealing effectiveness using inputs from two fast-response pressure sensors. Results exhibited low prediction errors across a full range of purge flow rates, which supports the viability of the modelling approach for CBOM. The final two studies in this dissertation address blade coolant flow monitoring. This cooling flow is responsible for protecting the turbine blades from the MGP flow, which exits the combustor at temperatures greater than the blade melting point. These studies showed that temperature measurements on the blade surface can be used to accurately predict blade coolant flow rate, and that defining the candidate features relative to the coolant trajectory is important for maintaining accuracy as coolant flow rate degradation occurs. This work enables blade coolant flow monitoring, which is currently not possible through existing condition monitoring techniques.

condition-based, gas turbines, diagnostics,↗
DOE OSTI2020

Electronic Probing and Manipulation of Low-Dimensional Magnets (Final Report)

We investigate the effects of reduced dimensionality of nanomagnets on the dynamical properties at low temperatures where quantum effects such as confinement, coherence, decoherence, and environmental coupling may be important. The experimental focus is on low-temperature transport measurements of such nanomagnets and cryogenic preamplification, followed by theoretical modeling of magnetic dynamics. In addition, we explore quantum effects in magnetic dynamics for their potential as quantum sensors of spin. We are particularly interested in the regime of strong environmental coupling, where novel phases of magnetic matter such as spin-boson may develop. The key new results are improved signal-to-noise ratio of magnetic tunneling junctions through low temperature preamplification that enables us to find the spin-dependent component of the current noise; a new powerful technique to simulate magnetic dynamics in the quantum coherent regime using the master equation formalism; and, finally, experimental hint of a novel state of matter in rare-earth doped permalloy ferromagnetic samples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

The Sensitivity of PBX 9502 to Drilling Operations

Polymer-bonded explosive (PBX) 9502 (95% TATB, 5% Kel-F 800 by weight) is dry-drilled on a CNC milling machine and its thermomechanical response to varying feed rates, drilling speeds, and peck depths with 4 mm and 5 mm diameter drill bits is investigated. The tested samples are affixed to a force sensor that enables recording temporally resolved cutting forces and torques, and a drill-embedded thermocouple yields local temperature data. From the data, an empirical relationship between temperature changes and feed per revolution is developed, which reveals reduced temperatures in higher feed per revolution regimes for PBX 9502. The observed relationship allows extrapolating to temperatures for other hole diameters, indicating increased temperature for smaller diameter drilling across the board. Additional testing was performed with PBX 9501 (95% HMX, 2.5% Estane®, 2.5% BDNPA/BDNPF by weight), albeit over a reduced parameter space, which revealed the opposite behavior for the feed per revolution temperature dependence. It is concluded that both PBX 9502 and PBX 9501 can be dry-drilled efficiently beyond the limits of presently applicable US-DOE standards, where cutting interface temperatures remain far below material critical temperatures. Finally, data reveals that coolant usage in the drilling process for these materials provides a wide safety margin.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Manipulating electron redistribution to achieve electronic pyroelectricity in molecular [FeCo] crystals

Pyroelectricity plays a crucial role in modern sensors and energy conversion devices. However, obtaining materials with large and nearly constant pyroelectric coefficients over a wide temperature range for practical uses remains a formidable challenge. Attempting to discover a solution to this obstacle, we combined molecular design of labile electronic structure with the crystal engineering of the molecular orientation in lattice. This combination results in electronic pyroelectricity of purely molecular origin. Here, we report a polar crystal of an [FeCo] dinuclear complex exhibiting a peculiar pyroelectric behavior (a substantial sharp pyroelectric current peak and an unusual continuous pyroelectric current at higher temperatures) which is caused by a combination of Fe spin crossover (SCO) and electron transfer between the high-spin Fe ion and redox-active ligand, namely valence tautomerism (VT). As a result, temperature dependence of the pyroelectric behavior reported here is opposite from conventional ferroelectrics and originates from a transition between three distinct electronic structures. The obtained pyroelectric coefficient is comparable to that of polyvinylidene difluoride at room temperature.

36 MATERIALS SCIENCE↗