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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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DOE OSTI2021

Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

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

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Vacant niches help predict invasion risk by birds

Abstract Aim If communities have a ceiling determining the number of species that can coexist, then the ability of alien species to establish at any given location should be related to the quantity of vacant niches available. We developed a new approach to estimate the extent to which niches are vacant and then explored the relationship between vacant niches and alien species. Location Global with focal tests in Europe and North America. Taxon Birds. Methods Drawing on a global classification of trophic structures for birds, we calculated a ‘vacant niche ratio’ metric to quantify the expected level of saturation (i.e. number of vacant niches) for each 1° × 1° grid cell globally, based on the difference between expected and observed numbers of bird species for all trophic guilds. Next, we used random forests to examine if the presence of plant‐invertivore (whose food source represents plants, seeds, fruits and invertebrates) and granivore alien bird species was associated with the vacant niche ratio across well‐sampled regions. Results In Europe, we found a significant relationship between alien species and vacant niches, with greater numbers of alien species being found in communities that offered greater numbers of vacant niches overall, and across habitat types. In North America, we found no significant relationship between plant‐invertivores and vacant niches, while for granivores, we found that areas with fewer vacant niches had greater numbers of alien species, especially in forests. However, vacant niches alone correctly predict 69% of the presence of alien bird species when combining both regions and trophic guilds. Main Conclusions Most regions of the world have unsaturated bird communities, with the level of saturation in communities varying within regions and trophic guilds. We found that although often‐neglected, vacant niches are likely to be, at least partially, related with the successful establishment of alien bird species.

González‐del‐Pliego, Pamela↗
DOE OSTI2021

Molecular phylogenetic analysis of the Paguristes tortugae Schmitt, 1933 complex and selected other Paguroidea (Crustacea: Decapoda: Anomura)

Morphological characters, as presently applied to describe members of the Paguristes tortugae Schmitt, 1933 species complex, appear to be of limited value in inferring phylogenetic relationships within the genus, and may have similarly misinformed understanding of relationships between members of this complex and those presently assigned to the related genera Areopaguristes Rahayu & McLaughlin, 2010 and Pseudopaguristes McLaughlin, 2002. Previously undocumented observations of similarities and differences in color patterns among populations additionally suggest genetic divergences within some species, or alternatively seem to support phylogenetic groupings of some species. In the present study, a Maximum Likelihood (ML) phylogenetic analysis was undertaken based on the H3, 12S mtDNA, and 16S mtDNA sequences of 148 individuals, primarily representatives of paguroid species from the western Atlantic. This molecular analysis supported a polyphyletic Diogenidae Ortmann, 1892, although incomplete taxonomic sampling among the genera of Diogenidae limits the utility of this finding for resolving family level relationships. Several hypotheses regarding the evolutionary relationships among hermit crab genera were refuted by the Kishino-Hasegawa (KH). ShimodairaHasegawa (SH) and Approximately Unbiased (AU) tree topology tests, among them the hypothesis that Areopaguristes is monophyletic. A lack of support for the monophyly of Areopaguristes calls into question the phylogenetic validity of gill number for the differentiation of Paguristes, Areopaguristes, and Pseudopaguristes. The study was inconclusive with regard to the relationships among these three genera, but previously unknown diversity within both Paguristes and Areopaguristes was demonstrated. Existence of an undescribed species confounded under the name Paguristes tortugae Schmitt, 1933 was supported by genetics, morphology, and coloration. A second undescribed species with remarkable similarity to Areopaguristes hummi Wass, 1955 was discovered based on genetics and coloration.

12S↗
DOE OSTI2026

PACT Center: Perovskite PV Accelerator for Commercializing Technologies (Final Technical Report)

The Perovskite PV Accelerator for Commercializing Technologies (PACT) center was established in July 2021 as a national resource to accelerate the commercialization of perovskite photovoltaic (PV) technology in the United States. Since its inception, PACT has been led by Sandia National Laboratories (Sandia) in partnership with the National Laboratory of the Rockies (NLR), formerly known as NREL. From FY20-FY23, Los Alamos National Laboratory (LANL), CFV Labs, Black & Veatch (B&V), and the Electric Power Research Institute (EPRI) were part of the project team. LANL brought expertise in perovskite PV device designs and processing, CFV Labs (now GroundWork Renewables) provided initial indoor and outdoor measurement hardware technology, B&V led the initial effort on perovskite PV bankability, and EPRI worked on reviewing testing standards, identifying commercialization gaps, and helping to run PACT’s Industry Advisory Board, a group including representatives from commercial testing labs, independent engineering firms, insurance companies, state regulators, and electric utilities. To source perovskite PV module samples, PACT contracted with the University of North Carolina (UNC), the University of Toledo, the University of Washington, and SLAC/Stanford University to provide a steady stream of research-grade perovskite mini modules, enabling protocol development in advance of commercial module availability. The project period ran from July 1, 2021, through December 31, 2025, including a No Cost Extension. Starting in FY25, the project was continued as a Core Capability in the Lab Call portfolio and continues at a reduced budget with only Sandia and NLR as funded recipients. Notably, starting in FY25 PACT expanded its scope beyond MHP modules to accept all emerging PV mini module technologies for testing, including organic PV (OPV) and all-thin-film tandems, with the aim of supporting commercialization across the broader emerging PV ecosystem. With this change in scope the program was renamed the PV Accelerator for Commercializing Technologies, dropping perovskite from the name.

14 SOLAR ENERGY↗
DOE OSTI2025

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado

Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Size and Time-resolved Aerosol Collector↗
DOE OSTI2022

SERPENT 2 and TRIPOLI-4 neutronics benchmarking of the Jules Horowitz reactor

Jules Horowitz Reactor (JHR) is a future center for nuclear material testing in Europe, currently under construction at CEA Cadarache in southern France. It has been modelled extensively with CEA's HORUS3D/N neutron calculation package, that includes a Monte-Carlo neutron transport code TRIPOLI-4. In order to support the future computational analyses on the reactor core physics, VTT's Monte Carlo neutronics code SERPENT 2 has been used to create an alternative model of the JHR, taking advantage of the pre-existing Constructive Solid Geometry (CSG) structures on which the TRIPOLI-4 model was built. This work describes a benchmark, in which two JHR core configurations were analyzed using both of these models. The first configuration is a fresh fuel core at the beginning of life, in which aluminum based dummy-devices are installed in the reactor test positions. The second configuration represents the end-of-cycle equilibrium core with a heterogeneous burnup distribution, in which CALIPSO devices containing cladding material samples are installed at the in-core experimental positions, while the reflector positions are occupied by DEN-REP devices containing UO{sub 2} fuel. Neutron flux spectra and heat deposition are the parameters of interest in this study, due to their high importance in creating the desired experimental irradiation environment. The impact of delayed gamma energy deposition will be studied in detail, since the two models account for it with different methods. This work aims to establish a SERPENT 2 model of JHR, understanding its differences and agreements with TRIPOLI-4, and discussing its potential future applications. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

DEVELOPMENT OF INEXPENSIVE HIGH TEMPERATURE NITI-BASED SHAPE MEMORY ALLOYS FOR POWDER BED ADDITIVE MANUFACTURING

NiTi and NiTi-based Shape Memory Alloys (SMA) exhibit a reversible solid-state phase transformation from martensite to austenite driven by thermal energy. High temperature (Mf>100°C) SMAs are martensite at room temperature and can be fabricated into solid-state actuators that return to a pre-programmed shape against a designed load after heating to transformation threshold. Reactive as-fabricated additively manufactured parts (4-D printing) is the current state of the art in manufacturing of SMAs but requires compositions compliant to rapid solidification. Existing actuator designs are developed from commercially available, highly investigated material compositions. However, existing high temperature high performance (high actuation strain, low thermal hysteresis) shape memory alloys contain significant (>10% at.) portions of high-cost Platinum Group Metals (PGMs). It is of significant scientific interest to investigate material compositions that are peer performing or superior to PGMs whose constituent elements represent a significant cost savings. Shape memory alloy properties vary significantly with small (0.1% at.) compositional changes making robust investigative sample sets very large. Computational material design can be deployed to shrink the compositional space of possible alloy combinations and reduce the experimental load in material discovery. Investigating shape memory effect (SME) and validating process additive process parameters for a single novel composition is cost intensive in both time and consumed materials. Additionally, sub-optimal processing, oxygen, or solidification rate sensitivity could render additively manufacturing specimens without micro, macro cracks, or significant chemical variance impossible. Unfortunately, such failure susceptibility cannot be simulated. Therefore, a research pathway to validate novel shape memory alloy compositions for powder bed fusion additive manufacturing without the need for powdered feedstock is also proposed. This research investigates novel high temperature shape memory alloys for actuators without platinum group alloying elements to discover one that could be commercially viable as an additive manufacturing feedstock.

Sundermann, Tayler↗
DOE OSTI2025

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

In this work, we present two neural network approaches that approximate the solutions of static and dynamic conditional optimal transport (COT) problems. Both approaches enable conditional sampling and conditional density estimation, which are core tasks in Bayesian inference—particularly in the simulation-based (“likelihood-free”) setting. Our methods represent the target conditional distribution as a transformation of a tractable reference distribution. Obtaining such a transformation, chosen here to be an approximation of the COT map, is computationally challenging even in moderate dimensions. To improve scalability, our numerical algorithms use neural networks to parameterize candidate maps and further exploit the structure of the COT problem. Our static approach approximates the map as the gradient of a partially input convex neural network. It uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives. Our dynamic approach approximates the conditional optimal transport via the flow map of a regularized neural ODE; compared to the static approach, it is slower to train but offers more modeling choices and can lead to faster sampling. We demonstrate both algorithms numerically, comparing them with competing state-of-the-art approaches, using benchmark datasets and simulation-based Bayesian inverse problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

OPFLearnData: Dataset for Learning AC Optimal Power Flow

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The influence of tillage and fertilizer on the flux and source of nitrous oxide with reference to atmospheric variation using laser spectroscopy

Nitrous oxide (N 2 O) is the third most important long-lived greenhouse gas and agriculture is the largest source of N 2 O emissions. Curbing N 2 O emissions requires understanding influences on the flux and sources of N 2 O. We measured flux and evaluated microbial sources of N 2 O using site preference ( S P ; the intramolecular distribution of 15 N in N 2 O) in flux chambers from a grassland tilling and agricultural fertilization experiments and atmosphere. We identified values greater than that of the average atmosphere to reflect nitrification and/or fungal denitrification and those lower than atmosphere as increased denitrification. Our spectroscopic approach was based on an extensive calibration with 18 standards that yielded S P accuracy and reproducibility of 0.7 ‰ and 1.0 ‰, respectively, without preconcentration. Chamber samples from the tilling experiment taken ~ monthly over a year showed a wide range in N 2 O flux (0–1.9 g N 2 O-N ha -1 d -1 ) and S P (- 1.8 to 25.1 ‰). Flux and S P were not influenced by tilling but responded to sampling date. Large fluxes occurred in October and May in no-till when soils were warm and moist and during a spring thaw, an event likely representing release of N 2 O accumulated under snow cover. These high fluxes could not be ascribed to a single microbial process as S P differed among chambers. However, the year-long S P and flux data for no-till showed a slight direct relationship suggesting that nitrification increased with flux. The comparative data in till showed an inverse relationship indicating that high flux events are driven by denitrification. Corn ( Zea mays ) showed high fluxes and S P values indicative of nitrification ~ 4 wk after fertilization with subsequent declines in S P indicating denitrification. Although there was no effect of fertilizer treatment on flux or S P in switchgrass ( Panicum virgatum) , high fluxes occurred ~1 month after fertilization. In both treatments, S P was indicative of denitrification in many instances, but evidence of nitrification/fungal denitrification also prevailed. At 2 m atmospheric N 2 O S P had a range of 31.1 ‰ and 14.6 ‰ in the grassland tilling and agricultural fertilization experiments, respectively. These data suggest the influence of soil microbial processes on atmospheric N 2 O and argue against the use of the global average atmospheric S P in isotopic modeling approaches.

Environmental Sciences & Ecology↗
DOE OSTI2022

Towards a neutron multiplicity measurement with the Accelerator Neutrino Neutron Interaction Experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26 ton Gadolinium (Gd)-loaded water Cherenkov detector located on the Booster Neutrino Beam line at Fermilab. Its main goals are the measurement of the neutron multiplicity in neutrino-nucleus interactions as well as the cross-section of Charged Current Quasi-Elastic (CCQE) neutrino interactions on water. Besides the physics goals, the experiment also aims to be a testbed for new technologies such as Large Area Picosecond Photodetectors (LAPPDs) and Water-based Liquid Scintillators (WbLS). This thesis presents a preliminary measurement of the neutron multiplicity with {ANNIE}, using an analysis conducted on a fraction of the 2021 beam year. As preparatory measures, the efficiency of ANNIE's Front Muon Veto (FMV) was determined to be {$\bar{\varepsilon}_{\mathrm{FMV}} = (95.6 \pm 1.6)\%$} while the average efficiency for active scintillator paddles in the Muon Range Detector (MRD) was found to be {$\bar{\varepsilon}_ {\mathrm{MRD}} = (92.1 \pm 7.9)\%$}. Furthermore, the simulation framework used for ANNIE was validated and adapted to reproduce the experimental data by comparing the detector response for samples of Michel electrons, Americium Beryllium neutrons, and through-going muons. The analysis finds average neutron yields of {$\bar{n}_{\mathrm{data}} (\mathrm{beam}) = (0.272 \pm 0.010_{\mathrm{stat}})$} for an inclusive set of all identified muon neutrino candidates and {$\bar{n}_{\mathrm{data}} (\mathrm{beam,FV}) = (0.287 \pm 0.044_{\mathrm{stat}})$ for interactions which happened inside of the Fiducial Volume of ANNIE, which was optimized to increase the neutron detection acceptance. The presented neutron multiplicity values represent the number of detected neutrons after all event selection cuts and are not yet corrected for the neutron detection efficiency. An equivalent analysis on a simulated beam sample predicts neutron yields of $\bar{n}_{\mathrm{MC}}(\mathrm{beam}) = (0.515 \pm 0.0 07_{\mathrm{stat}})$ and $\bar{n}_{\mathrm{MC}}(\mathrm{beam,FV}) = (0.627 \pm 0.031_{\mathrm{stat}})$, indicating that the models tend to overpredict the number of neutrons produced in such interactions. Systematic errors have been briefly considered to contribute {$\sigma_{\mathrm{sys,FMV}} \sim 0.01\,$neutrons/$\nu$-interaction} due to the slight FMV inefficiency and {$\sigma_{\mathrm{sys,n}} \sim 0.05\,$neutrons/$\nu$-interaction} due to the neutron detection efficiency. Simulation studies further highlighted the importance of neutron detection in Diffuse Supernova Background (DSNB) searches. A combination of neutron tagging and Convolutional Neural Networks was found to reduce the most relevant Neutral Current Quasi-Elastic (NCQE) interaction background below the signal level, achieving a Signal-to-Background ratio of 4:1. In a further study, we investigated the positive impact of a deployment of a WbLS target on the energy reconstruction in ANNIE. WbLS provides a scintillation signal from hadronic recoils in addition to the charged lepton that can be included in neutrino energy reconstruction. It was found that a deployed WbLS volume in ANNIE improves the neutrino energy reconstruction from 14\% to 12\%, with the potential of going beyond this if more sophisticated reconstruction algorithms are developed in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A compact dication source for Ba 2+ tagging and heavy metal ion sensor development

Here, we present a tunable metal ion beam that delivers controllable ion currents in the picoamp range for testing of dry-phase ion sensors. Ion beams are formed by sequential atomic evaporation and single or multiple electron impact ionization, followed by acceleration into a sensing region. Controllability of the ionic charge state is achieved through tuning of electrode potentials that influence the retention time in the ionization region. Barium, lead, and cadmium samples have been used to test the system, with ion currents identified and quantified using a quadrupole mass analyzer. Realization of a clean Ba 2+ ion beam within a bench-top system represents an important technical advance toward the development and characterization of barium tagging systems for neutrinoless double beta decay searches in xenon gas. This system also provides a testbed for investigation of novel ion sensing methodologies for environmental assay applications, with dication beams of Pb 2+ and Cd 2+ also demonstrated for this purpose.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Single‐Crystal LiNi x Mn y Co 1− x − y O 2 Cathodes for Extreme Fast Charging

Abstract Ni‐rich layered LiNi x Mn y Co 1− x − y O 2 (NMCs, x ≥ 0.8) are poised to be the dominating cathode materials for lithium‐ion batteries for the foreseeable future. Conventional polycrystalline NMCs, however, suffer from severe cracking along the grain boundaries of primary particles and capacity loss under high charge and/or discharge rates, hindering their implementation in fast‐charging electric vehicular (EV) batteries. Single‐crystal (SC) NMCs are attractive alternatives as they eliminate intergranular cracking and allow for grain‐level surface optimization for fast Li transport. In the present study, the authors report synthetic approaches to produce SC LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NMC811) samples with different morphologies: Oct‐SC811 with predominating (012)‐family surface and Poly‐SC811 with predominating (104)‐family surface. Poly‐SC811, representing the first experimentally synthesized NMC811 single crystals with (104) surface, delivers superior performance even at the ultra‐high rate of 6 C. Through detailed X‐ray analysis and electron microscopy characterization, it is shown that the enhanced performance originates from better chemical and structural stabilities, faster Li + diffusion kinetics, suppressed side reactions with electrolyte, and excellent cracking resistance. These insights provide important design guidelines in the future development of fast‐charging NMC‐type cathode materials.

Lu, Yanying↗
DOE OSTI2022

Layered supramolecular hydrogels from thioglycosides

Low molecular weight hydrogels are made of small molecules that aggregate via noncovalent interactions. Here, comprehensive characterization of the physical and chemical properties of hydrogels made from thioglycolipids of the disaccharides lactose and cellobiose with simple alkyl chains is reported. While thiolactoside hydrogels are robust, thiocellobioside gels are metastable, precipitating over time into fibrous crystals that can be entangled to create pseudo-hydrogels. Rheology confirms the viscoelastic solid nature of these hydrogels with storage moduli ranging from 10–600 kPa. Additionally, thiolactoside hydrogels are thixotropic which is a desirable property for many potential applications. Freeze-fracture electron microscopy of xerogels shows layers of stacked sheets that are entangled into networks. These structures are unique compared to the fibers or ribbons typically reported for hydrogels. Differential scanning calorimetry provides gel-to-liquid phase transition temperatures ranging from 30 to 80 °C. Prodan fluorescence spectroscopy allows assignment of phase transitions in the gels and other lyotropic phases of high concentration samples. Phase diagrams are estimated for all hydrogels at 1–10 wt% from 5 to ≥ 80 °C. In conclusion, these hydrogels represent a series of interesting materials with unique properties that make them attractive for numerous potential applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.

99 GENERAL AND MISCELLANEOUS↗