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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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308 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Effect of Thermodynamic and Environmental Factors on Crystallization of DNA‐Origami Superlattices

The directed self‐assembly of nanoscale materials into ordered superlattices presents a powerful strategy for creating next‐generation materials with programmable mechanical, optical, and photonic properties. Deoxyribonucleic acid (DNA) origami has emerged as a versatile scaffold for encoding nanoscale geometry and guiding the crystallization of complex 3D architectures. However, a systematic understanding of the parameters that govern the efficiency and quality of superlattice formation remains limited. In this study, we utilize octahedral DNA nanoscale frames as a model system to investigate the relative influence of key factors, including buffer composition, ionic strength, frame concentration, and thermal annealing protocols, on the size, order, and reproducibility of the resulting superlattices. Our findings provide a quantitative framework to rationally optimize DNA‐based assembly pathways. Structural characterization via small‐angle x‐ray scattering (SAXS), scanning electron microscopy (SEM), and optical microscopy validates the quality and fidelity of the assembled lattices. Moreover, by templating these DNA frameworks into inorganic replicas, we establish general design principles that extend beyond biomolecular systems, providing a foundation for the synthesis of programmable materials in broader nanofabrication contexts.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Multi‐Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications

Abstract Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi‐objective search is developed, combined with first‐principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state‐of‐the‐art post‐Cu material, and also semi‐global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Chemistry↗
DOE OSTI2026

Heterotrophic respiration by soil microbes in a changing climate

Soil microbes strongly influence the soil organic carbon (SOC) pool, which globally stores ~2,000 PgC. Specifically, the balance between microbial heterotrophic respiration (R H ), which degrades SOC, and plant–microbe interactions that stabilize SOC determines whether terrestrial ecosystems are a net source or sink of CO 2 to the atmosphere. Here, in this Review, we evaluate how climate change alters these competing processes. R H is approximately half of total soil respiration, at ~50 PgC yr −1 , with 70% occurring in topsoils. Warming accelerates microbial metabolism, with a 10 °C temperature increase estimated to raise R H by ~50%, an effect that is particularly strong in Arctic soils. Warming also reduces soil moisture, further modulating R H , which responds nonlinearly to soil moisture, being limited by saturation and desiccation and meeting a maximum at intermediate levels. Consequently, R H is highly sensitive to future precipitation changes and drought. However, soil management strategies could enhance SOC stocks and persistence under climate change. Bacterial and fungal inoculants can promote SOC production and stabilization, while deep-rooting plants increase SOC inputs to deeper layers that experience lower R H . Agricultural practices and biochar amendments can also enhance SOC and reduce R H . Expanding field trials across regions, climates and soil types would improve empirical understanding of these responses and support better representation of R H in predictive models, enabling more accurate assessments of climate impacts on SOC storage.

Jansson, Janet K. [Pacific Northwest National Labo↗
DOE OSTI2023

Oxidation Chemistry of Bicarbonate and Peroxybicarbonate: Implications for Carbonate Management in Energy Storage

Carbonate formation presents a major challenge to energy storage applications based on low-temperature CO 2 electrolysis and recyclable metal–air batteries. While direct electrochemical oxidation of (bi)carbonate represents a straightforward route for carbonate management, knowledge of the feasibility and mechanisms of direct oxidation is presently lacking. Herein, we report the isolation and characterization of the bis(triphenylphosphine)iminium salts of bicarbonate and peroxybicarbonate, thus enabling the examination of their oxidation chemistry. Infrared spectroelectrochemistry combined with time-resolved infrared spectroscopy reveals that the photoinduced oxidation of HCO 3 – by an Ir(III) photoreagent results in the generation of the short-lived bicarbonate radical in less than 50 ns. The highly acidic bicarbonate radical undergoes proton transfer with HCO 3 – to furnish the carbonate radical anion and H 2 CO 3 , leading to the eventual release of CO 2 and H 2 O, thus accounting for the appearance of H 2 O and CO 2 in both electrochemical and photochemical oxidation experiments. Here, the back reaction of the carbonate radical subsequently oxidizes the Ir(II) photoreagent, leading to carbonate. In the absence of this back reaction, dimerization of the carbonate radical provides entry into peroxybicarbonate, which we show undergoes facile oxidation to O 2 and CO 2 . Together, the results reported identify tangible pathways for the design of catalysts for the management of carbonate in energy storage applications.

25 ENERGY STORAGE↗
DOE OSTI2026

PV Durability at Scale: Assessing Bifacial and TOPCon Field Performance

Recent field performance results of TOPCon and PERC Bifacial modules are presented based on small-scale deployments at NLR, and a selection of 3rd-party residential data. Bifacial PERC degradation rates are a median of -1 % / yr with bifacial TOPCon modules at a similar rate of -1.1 %/yr over the first couple of years. Comparable PERC Monofacial modules have a degradation rate of -0.5%/yr. Some TOPCon modules exhibited an indoor dark metastability effect which requires recovery by light soaking or UV exposure prior to IV curve measurement. Further monitoring is needed to confirm the long-term stability of TOPCon modules.

14 SOLAR ENERGY↗
DOE OSTI2023

Atomic Dynamics of Multi‐Interfacial Migration and Transformations

Redox-induced interconversions of metal oxidation states typically result in multiple phase boundaries that separate chemically and structurally distinct oxides and suboxides. Directly probing such multi-interfacial reactions is challenging because of the difficulty in simultaneously resolving the multiple reaction fronts at the atomic scale. Using the example of CuO reduction in H 2 gas, a reaction pathway of CuO → monoclinic m-Cu 4 O 3 → Cu 2 O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu 2 O/m-Cu 4 O 3 interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu 4 O 3 /CuO transformation. Together with atomistic modeling, it is shown that such a multi-interface transformation results from the surface-reaction-induced formation of oxygen vacancies that diffuse into deeper atomic layers, thereby resulting in the formation of the lower oxides of Cu 2 O and m-Cu 4 O 3 , and activate the interfacial transformations. In conclusion, these results demonstrate the lively dynamics at the reaction fronts of the multiple interfaces and have substantial implications for controlling the microstructure and interphase boundaries by coupling the interplay between the surface reaction dynamics and the resulting mass transport and phase evolution in the subsurface and bulk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗
DOE OSTI2026

Carbon Tetrachloride Degradation Results for 200-ZP-1 Operable Unit

Carbon tetrachloride (CT) contamination in the 200-ZP-1 Operable Unit (OU) at the Hanford Site originated from large-volume discharges to the subsurface during plutonium production operations between 1955 and 1973. Contamination migrated through more than 70 meters of unsaturated sediment to reach the underlying unconfined aquifer, where it persists as a large and complex groundwater plume. The 200-ZP-1 OU Record of Decision (ROD) requires that groundwater CT concentrations be reduced to 3.4 µg/L within 125 years. Current groundwater modeling projections estimate that the existing pump-and-treat, even when combined with monitored natural attenuation (specifically hydrolysis), will not achieve this target within the designated timeframe. A fundamental contributor to this shortfall is the extremely slow rate of CT hydrolysis under Hanford aquifer conditions, which has been estimated to have a half-life of 630 years. If faster-acting biotic and abiotic degradation processes are operating within the aquifer, their contribution to CT mass reduction could have a meaningful impact. However, site-specific measurements of these processes and their rates have not previously been performed. This report documents the results of a two-phase laboratory investigation designed to characterize and quantify the capacity of site-specific 200-ZP-1 OU sediments and groundwater to support natural attenuation of CT through biotic and abiotic pathways. In this context, degradation capacity is defined as the intrinsic potential of the subsurface matrix to transform CT under optimized, controlled conditions. System capacity is evaluated in two ways: (1) as rate-limited capacity, which establishes the maximum kinetic velocity of CT transformation and is measured using half-lives and first order rate constants; and (2) as mass limited capacity, which defines the total contaminant mass the batch experimental system can degrade before reactants are exhausted, representing the maximum amount of contaminant the microbial community and reactive mineral phases can transform under the experimental conditions.

abiotic degradation↗
DOE OSTI2026

Atomic- and Molecular-Scale Interphase Engineering for High-Performance Solid-State Batteries

Solid-state batteries (SSBs) promise a decisive advance beyond conventional Li-ion systems, yet their development remains constrained by persistent solid–solid interfacial instabilities that degrade performance and durability. Interfaces between solid electrolytes and both cathodes and Li metal often exhibit poor wettability, limited physical contact, and high charge–transfer resistance, leading to chemical decomposition, mechanical failure, and impedance growth. Overcoming these limitations requires interphase engineering with atomic-scale precision—capabilities that conventional coating methods cannot reliably deliver. Atomic layer deposition (ALD) and molecular layer deposition (MLD) uniquely meet this need by enabling ultrathin, conformal, and composition-tunable films that stabilize reactive surfaces, suppress parasitic reactions, and regulate Li-metal morphology. Importantly, this Perspective highlights ALD/MLD systems that have already demonstrated effectiveness in liquid-electrolyte cells and discusses how these validated strategies can be deliberately translated to solid-state architectures. By grounding future directions in experimentally proven concepts rather than speculative hypotheses, we outline how atomic- and molecular-scale design principles can accelerate the development of robust, high-performance SSB technologies.

atomic and molecular layer deposition↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
DOE OSTI2026

Aliovalent gallium dopants remove Ti 3+ defects and improve photocatalytic and photoelectrochemical water oxidation properties of LaTiO 2 N

LaTiO 2 N is a promising semiconductor for the water splitting reaction due to its 2.1 eV band gap and stability against corrosion. However, its solar energy conversion is limited by Ti 3+ recombination defects introduced during ammonolysis. Here we show for the first time that Ti 3+ defects in LaTiO 2 N can be suppressed with incorporation of 2, 5, and 10% aliovalent gallium (Ga 3+ ) dopants during synthesis via the layered La 2 Ti 2 O 7 intermediate. Electron paramagnetic resonance (EPR) spectroscopy on the solid powders confirms a reduction in the Ti 3+ donor density from 2.97 × 10 17 cm −3 for the non-doped material to ∼6.24 × 10 16 cm −3 for 5% Ga-doped LaTiO 2 N. The remaining Ti 3+ defects are concentrated near the LaTiO 2 N surface, according to X-ray photoelectron spectroscopy. The defect reduction shifts the optical absorption edge from 2.02 to 2.09 eV and eliminates a broad absorption band at 1050 nm from the optical absorption spectra. It also removes a 1.0–1.7 eV sub-band gap photovoltage signal from surface photovoltage spectra. This suggests that empty Ti 3+ d-orbitals are located 1.0–1.7 eV above the LaTiO 2 N valence band edge. Removing these recombination states with increasing Ga 3+ content enhances the photoconversion efficiency of LaTiO 2 N during water oxidation. The optimized 2 wt% CoO x -loaded 5% Ga-doped LaTi O2 N material has a 16% AQE (400 nm) for O 2 production from aqueous silver nitrate solution and a ∼2.1 mA cm −2 water oxidation photocurrent at 1.23 V under 100 mW cm −2 Xe arc lamp illumination. The water oxidation photocurrent is stable during a 50 min test, and the Faraday efficiency for O 2 generation is 97%, confirming short-term corrosion stability of LaTiO 2 N. Altogether, these results provide a better understanding of the distribution, concentration, and impact of Ti 3+ defects on the optical, photovoltage, and photoelectrochemical properties of LaTiO 2 N. In combination with other defect control strategies, aliovalent Ga 3+ doping can help bring the solar energy conversion efficiency of LaTiO 2 N closer to the theoretical limit.

Wang, Li [University of California, Davis, CA (Uni↗
DOE OSTI2025

H 2 O and CO 2 Sorption in Ion-Exchange Sorbents: Distinct Interactions in Amine Versus Quaternary Ammonium Materials

This article examines how water (H₂O) and carbon dioxide (CO₂) interact with two classes of ion-exchange sorbents — a primary amine sorbent and a quaternary ammonium (QA⁺) sorbent — using calorimetry, thermal gravimetric analysis, gas analysis, and molecular modeling. Here, the QA⁺ sorbent exhibits stronger binding to both H₂O and CO₂ but also shows thermal stability limitations. Mixed-gas experiments reveal that humidity strongly influences CO₂ uptake and that moisture-driven sorbent regeneration enables cyclic moisture swing CO₂ capture, with implications for low-energy CO₂ separation from dilute gas streams.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
DOE OSTI2024

Block copolymer self-assembly derived mesoporous magnetic materials with three-dimensionally (3D) co-continuous gyroid nanostructure

Magnetic nanomaterials are gaining interest for their many applications in technological areas from information science and computing to next-generation quantum energy materials. While magnetic materials have historically been nanostructured through techniques such as lithography and molecular beam epitaxy, there has recently been growing interest in using soft matter self-assembly. In this work, a triblock terpolymer, poly(isoprene-block-styrene-block-ethylene oxide) (ISO), is used as a structure directing agent for aluminosilicate sol nanoparticles and magnetic material precursors to generate organic–inorganic bulk hybrid films with co-continuous morphology. After thermal processing into mesoporous materials, results from a combination of small angle X-ray scattering (SAXS) and scanning electron microscopy (SEM) are consistent with the double gyroid morphology. Nitrogen sorption measurements reveal a type IV isotherm with H1 hysteresis, and yield a specific surface area of around 200 m 2 g −1 and an average pore size of 23 nm. The magnetization of the mesostructured material as a function of applied field shows magnetic hysteresis and coercivity at 300 K and 10 K. Comparison of magnetic measurements between the mesoporous gyroid and an unstructured bulk magnetic material, derived from the identical inorganic precursors, reveals the structured material exhibits a coercivity of 250 Oe, opposed to 148 Oe for the unstructured at 10 K, and presence of remnant magnetic moment not conventionally found in bulk hematite; both of these properties are attributed to the mesostructure. This scalable route to mesoporous magnetic materials with co-continuous morphologies from block copolymer self-assembly may provide a pathway to advanced magnetic nanomaterials with a range of potential applications.

Chemistry↗
DOE OSTI2026

Smart Charging of Fleet and Personal Electric Vehicles through Joint Vehicle-to-Grid Optimization

As electric vehicle (EV) adoption accelerates, vehicle-to-grid (V2G) strategies offer advantages over unmanaged charging (V0G) by enhancing grid stability, reducing fleet operation costs, and supporting integration of variable generation resources. This research develops a day-ahead optimization framework linked with agent-based simulations to evaluate coordinated V2G participation by fleet and personal EVs under 5 energy-pricing settings in Austin, Texas. Three scenarios (V0G, fleet-only V2G, and joint-V2G) are examined, considering real-time price and grid profiles, health-damage costs, and operational constraints for both fleet and personal EVs. Results show how V2G scenarios shift fleet EV charging to mid-day while enabling strategic battery-discharge during evening peaks, mitigating grid stress and lowering EV energy costs. V2G delivers close to 80% energy-cost savings for a 2000-EV fleet in Austin on grid-stressed days, with 55% lower charging pollutant outputs. Joint-V2G amplifies system-level benefits by complementing fleet discharge, but smart-charging equipment costs can offset those benefits.

Electric vehicle↗
DOE OSTI2026

Heterobimetallic Iridium-Niobia Catalyst for Efficient and Selective Methane Ammonia Reforming

A Surface OrganoMetallic Chemistry (SOMC) approach, leveraging a molecularly defined heterobimetallic niobium–iridium complex, was used to prepare a mesoporous SBA-15 silica-supported Ir-NbOx catalyst. The resulting Ir-NbOx/SiO2 catalyst exhibited excellent catalytic performance in selective methane/ammonia reforming. Specifically, the Ir-NbOx/SiO2 catalyst showed significantly higher activity (turnover frequency 8.5 s–1), selectivity (75%), and stability than the Ir/SiO2 analog, whereas the NbOx/SiO2 counterpart was almost inactive. This contrasts with ethane/ammonia reforming via C–C cleavage, for which the bimetallic Ir-NbOx/SiO2 was less active than Ir/SiO2, demonstrating tuned selectivity toward C–H activation rather than C–C cleavage due to the Ir/NbOx synergy. Importantly, an Ir-NbOx/SiO2 reference catalyst, prepared by conventional impregnation/calcination/reduction steps, was found to be inactive, highlighting the value of the SOMC catalyst preparation approach using well-defined heterobimetallic precursors. These results represent a significant advance over existing catalysts due to the atomic-scale synergy between Ir and NbOx sites, enabling access to activity and selectivity regimes inaccessible to monometallic analogs.

Wu, Jiachun↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗