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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Integrating three plan evaluation approaches for coordinated heat resilience in cities across the Arizona urban corridor

Increasing heat poses a growing threat to cities worldwide due to both climate change and the urban heat island effect. While heat planning and governance are still emergent, research suggests that silos and conflicts within cities' networks of plans often impede heat resilience. Integrated heat resilience planning, therefore, requires a systematic and comprehensive analysis of the silos and conflicts relevant to heat resilience within networks of plans. This study is the first to combine three complementary plan evaluation methods to assess how cities' networks of plans address heat resilience. We applied 1) plan cross-referencing, 2) Plan Quality Evaluation for Heat Resilience, and 3) Plan Integration for Resilience Scorecard™ (PIRS™) for Heat to 19 plans from seven Arizona cities. We find similarities and differences in how these cities' networks of plans address heat hazards. The plans have consistently high-quality participation and coordination principles but lack details on vulnerability and climate change uncertainty, suggesting a need to move beyond immediate heat risks. We also identify opportunities to diversify policy mechanisms, spatially target high heat risk areas, and enhance the connection between planning efforts. These results validate that plan elements are interlinked and the importance of integrative plan development processes to improve heat resilience.

Extreme heat↗
DOE OSTI2025

Preliminary Analysis of Nuclear-Powered Data Center Scenarios

This report provides a comprehensive analysis of the potential for nuclear energy to meet the growing energy demands of data centers (DCs). It evaluates the technical, economic, and socio-environmental implications of coupling Nuclear Power Plants (NPPs) with DCs, providing initial responses to several key research questions: What is the potential increased energy demand from DCs in the U.S., in the short, medium and long term? The U.S. is experiencing a rapid increase in energy demand from DCs, with projections indicating a total increase of 24-74 GWy(e) by 2028. Meeting this demand with nuclear energy would require 27–85 GWe of installed capacity. While this surge is expected to slow in the long term, the DC industry needs reliable, scalable, and clean energy sources. How much nuclear capacity can be deployed to meet DC demand and in which timeframe? Several pathways for increasing nuclear capacity were identified, including uprates, restarts of recently retired reactors, power purchase agreements with existing fleet, and new construction. Approximately 20‒28 GWe of nuclear capacity could be dedicated to DCs by the early 2030s. How much High Assay Low Enriched Uranium (HALEU) would be needed to support some nuclear deployment scenarios for DCs? Meeting the deployment targets announced by Google and Amazon for the Kairos Power Fluoride-Salt-Cooled High-Temperature Reactor or KP-FHR (~500 MWe by 2035) and the Xe-100 (~1 GWe by 2040), respectively, requires ramping up 19.75% enriched HALEU production to ~6 t/yr by 2040. What types of nuclear energy/DC coupling options exist, and what are the different benefits/challenges? Five coupling options were analyzed, ranging from grid-connected configurations to colocated, behind-the-meter setups. Key design considerations include the proximity to high- and/or medium-voltage transmission lines, the desired internal fault tolerance, and the sources of alternative/backup power during outages. Each coupling option offers unique benefits and challenges in terms of reliability, system costs, regulation, timeline, etc. A list of NPP/DC deployment scenarios was developed, considering existing or newly built NPP or DC projects. Colocated DCs with new small modular reactors or large reactors on greenfield and brownfield sites are the focus of this report. What types of reactors, especially what size, may be incentivized by DCs? Reactor sizing optimization revealed that the ideal reactor size and number of units depend on DC demand, coupling configurations defined in this report, and other economic factors. Larger reactors are preferred for high-demand DCs and grid-connected systems, while larger number of smaller reactors are better suited for DC configurations without grid backup. Which sites may be compatible with co-located nuclear-powered DCs? Siting those projects is a complicated evaluation factoring local water resources, grid connection availability and reliability, IT infrastructure, local work force, proximity to population zones, etc. For this effort greenfield and brownfield sites such as retired coal-fired plants were used to evaluate this question. This evaluation is not meant to recommend any particular site but it highlights key siting criteria and demonstrates large-scale site availability. What are the socio-economic impacts of co-located nuclear-powered DCs? Those projects generate substantial economic benefits to the local economy, particularly in urban settings. Hyperscale DCs colocated with nuclear power plants (sized around 1 GW of power) can create nearly 1,700 jobs for annual operations and more than 7,300 jobs among the supply chain and local businesses as a result of increased household spending. Rural projects also provide significant benefits, but at lower magnitudes compared to urban deployments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

At Risk Population Estimates for Belarus, Poland and Slovakia with Machine Learning

High-resolution gridded population modeling is crucial for various applications, including disaster response planning, infectious disease spread modeling, climate change impact estimation, policy development, and more. Multiple gridded population datasets have been developed, each tailored to meet specific objectives. Among them, LandScan Global dataset is designed to represent ambient and unwarned population distributions. However, this dataset relies on a statistical approach that requires manual adjustments, making it time consuming and labour intensive. Existing machine learning (ML) methods often train and test at different spatial resolutions, potentially leading to inflated results, and they rely on Census population totals for disaggregation. To address these limitations, in this study we developed population estimates using ML models trained and tested at a consistent 30 arc-second resolution (≈1 square kilometer), specifically using Random Forest (RF) and XGBoost. These models were trained on 2020 datum to predict for 2021 for three countries: Belarus, Poland, and Slovakia. Our findings show that both RF (MAE varies from 5.75 to 13.25) and XGBoost (MAE varies from 8.15 to 23.44) model performance is close to LandScan Global estimates. Furthermore, neither of the models performed the best across all grid cells: the RF model was more effective in areas with lower populations, while XGBoost excelled in more densely populated regions. The proposed approach can be used for countries where the Census data is not available.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2025

Housing-Performance Atlas of Baltimore Row Homes: Archetype-Based Multi-Hazard Baseline of Energy, Heat, Survivability, and Durability

Baltimore’s historic row-home neighborhoods face escalating risks to energy, heat, and durability under intensifying climate stress. This study develops a Housing-Performance Atlas that quantifies multi-hazard performance for eight representative archetypes using DesignBuilder/EnergyPlus Version 7.3.1.003, under Baltimore TMY3 boundary conditions. Performance is evaluated across the following four adaptation domains: energy use intensity, passive survivability during 72 h outage events, roof overheating exposure (>150 °F exceedance hours), and material service life derived from ISO 15686 and synthesized into Lean and Full Deficit Indices for comparative resilience ranking. Results show that EUI ranged from 46.7 to 67.6 kBtu ft −2 ·yr −1 , survivability from 0 to 23 h, and roof temperatures exceeded 150 °F for 150–210 h, shortening roof service life by up to 10 years. Composite Lean and Full Deficit Indices ranged 7.8–92.4, ranking Model 5 (end-unit, flat roof, two-story with basement) as the most resilient configuration and Model 8 (end-unit, pitched roof, three-story above-grade) as the least resilient due to compounded overheating and energy losses. Heat-related domains accounted for nearly 70% of overall resilience deficits, confirming thermal safety and roof reflectivity as retrofit priorities. The Housing-Performance Atlas establishes a reproducible diagnostic framework linking simulation, service life, and resilience metrics to guide cost-effective, climate-responsive retrofits in Baltimore’s aging urban housing stock.

Housing-Performance atlas↗
DOE OSTI2024

Fast Room-Temperature Mg-Ion Conduction in Clay-Like Halide Glassy Electrolytes

The discovery of mechanically soft solid-state materials with fast Mg-ion conduction is crucial for the development of solid-state magnesium batteries. In this paper, novel magnesium gallium halide compounds are reported that achieve high ionic conductivity of 0.47 mS cm -1 at room temperature. These Mg-ion conductors obtained by ball milling Mg and Ga salts exhibit clay-like mechanical properties, enabling intimate contact at the electrode–electrolyte interface during battery cycling. With a combination of experimental and computational analysis, this study identifies that the soft-clay formation is induced by partial anion exchange during milling. This partial anion exchange creates undercoordinated magnesium ions in a chlorine-rich environment, yielding fast Mg-ion conduction. This work demonstrates the potential of clay-like halide electrolytes for all-solid-state magnesium batteries, with possible further extension to other multivalent battery systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mechanochemical in Situ Encapsulation of Palladium in Covalent Organic Frameworks

Palladium-encapsulated covalent organic frameworks (Pd/COFs) have garnered enormous attention in heterogeneous catalysis. However, the dominant ex situ encapsulation synthesis is tedious (multistep), time-consuming (typically 4 days or more), and involves the use of noxious solvents. Here we develop a mechanochemical in situ encapsulation strategy that enables the one-step, time-efficient, and environmentally benign synthesis of Pd/COFs. By ball milling COF precursors along with palladium acetate (Pd(OAc) 2 ) in one pot under air at room temperature, Pd/COF hybrids were readily synthesized within an hour, exhibiting high crystallinity, uniform Pd dispersion, and superb scalability up to gram scale. Moreover, this versatile strategy can be extended to the synthesis of three Pd/COFs. Remarkably, the resulting Pd/DMTP-TPB showcases extraordinary activity (96-99% yield in 1 h at room temperature) and broad substrate scope (>10 functionalized biaryls) for the Suzuki-Miyaura coupling reaction of aryl bromides and arylboronic acids. Furthermore, the heterogeneity of Pd/DMTP-TPB is verified by recycling and leaching tests. Finally, the mechanochemical in situ encapsulation strategy disclosed herein paves a facile, rapid, scalable, and environmentally benign avenue to access metal/COF catalysts for efficient heterogeneous catalysis.

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

A general flame aerosol route to high-entropy nanoceramics

High-entropy ceramics are an emerging class of materials with fascinating characteristics. However, elemental immiscibility and crystal complexity limit the development of a general synthesis strategy, and common methods yield bulk materials. Here, we introduce a transformative non-equilibrium flame aerosol technique for synthesizing high-entropy nanoceramics. This scalable, one-step process enables the production of high-entropy oxide nanoceramics with an unprecedented diversity of crystal structures, including fluorite-phase materials that integrate up to 22 distinct cation elements. The method’s capacity for entropic stabilization and grain refinement significantly improves the thermal stability of these nanostructures. In a representative application, a Pt-(MgCoNiCuZn)O high-entropy single-atom catalyst showed superior activity and long-term stability, maintaining constant CO 2 conversion over 670 h and dramatically outperforming conventional catalysts. Finally, the general approach opens a vast composition and structure space for the creation of high-entropy oxide nanomaterials for application across diverse fields, including catalysis, energy storage, sensing, and thermal management.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗
DOE OSTI2026

Who is benefiting from the dramatic decline in U.S. cancer mortality? Place-based evidence of disparities in rates of improvement

After decades of increasing cancer mortality, U.S. rates declined from 1991 to 2019, a 32% decrease. we investigated rates of cancer mortality improvement across 2954 counties and selected characteristics associated with mortality improvements. Data was 21,381,009 county-level neoplasm deaths gleaned from death certificates via CDC WONDER. Analytical techniques included GIS and Moran’s I, OLS, GWR models, and trend comparisons. Counties with the greatest improvement (reduction) in cancer mortality tended to be coastal, higher-income, metropolitan locations. OLS model (R 2 = 0.65) indicated that greatest improvements were observed in counties with higher initial mortality ($\beta =.32$) closely followed by percent urban ($\beta =.31$) and median household income ($\beta =.16$). Whereas percent Black residents ($\beta =-.06$), and percent with education beyond high school ($\beta =-.10$) was less associated on outcomes. Highest income counties were the first to experience improvement in cancer mortality, the highest rates of mortality decline, and the greatest reduction in excess deaths. Even though there was significant improvement in cancer mortality nationally, there were variations in the degree of improvement linked to county location, income, and urbanisation. These results underlie the need to expand place-based initiatives designed to advance cancer health and more equitable improvements in cancer mortality outcomes.

developing world↗
DOE OSTI2025

Designing Future Energy Systems with Generative AI

Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Electrochemical lithium extraction from hectorite ore

Electrochemical technologies add a unique dimension for ore refinement, representing tunable methods that can integrate with renewable energy sources and existing downstream process flows. However, the development of electrochemical extraction technologies has been impeded by the technological maturity of hydro- and pyro-metallurgy, as well as the electrical insulating properties of many metal oxide ores. The fabrication and use of carbon/insulating material composite electrodes has been a longstanding method to enable electrochemical activation. Here, using real hectorite ore, we employ this technical approach to fabricate hectorite-carbon black composite electrodes (HCCEs) and achieve electrochemical activation of hectorite. Anodic polarization results in lithium-ion release through a multi-step chemical and electrochemical mechanism that results in 50.7 ± 4.4% removal of lithium from HCCE, alongside other alkaline ions. This technical proof-of-concept study underscores that electrochemical activation of ores can facilitate lattice deterioration and ion removal from ores.

25 ENERGY STORAGE↗
DOE OSTI2024

Intra- and inter-subtype HIV diversity between 1994 and 2018 in southern Uganda: a longitudinal population-based study

There is limited data on human immunodeficiency virus (HIV) evolutionary trends in African populations. We evaluated changes in HIV viral diversity and genetic divergence in southern Uganda over a 24-year period spanning the introduction and scale-up of HIV prevention and treatment programs using HIV sequence and survey data from the Rakai Community Cohort Study, an open longitudinal population-based HIV surveillance cohort. Gag (p24) and env (gp41) HIV data were generated from people living with HIV (PLHIV) in 31 inland semi-urban trading and agrarian communities (1994–2018) and four hyperendemic Lake Victoria fishing communities (2011–2018) under continuous surveillance. HIV subtype was assigned using the Recombination Identification Program with phylogenetic confirmation. Inter-subtype diversity was evaluated using the Shannon diversity index, and intra-subtype diversity with the nucleotide diversity and pairwise TN93 genetic distance. Genetic divergence was measured using root-to-tip distance and pairwise TN93 genetic distance analyses. Demographic history of HIV was inferred using a coalescent-based Bayesian Skygrid model. Evolutionary dynamics were assessed among demographic and behavioral population subgroups, including by migration status. 9931 HIV sequences were available from 4999 PLHIV, including 3060 and 1939 persons residing in inland and fishing communities, respectively. In inland communities, subtype A1 viruses proportionately increased from 14.3% in 1995 to 25.9% in 2017 (P < .001), while those of subtype D declined from 73.2% in 1995 to 28.2% in 2017 (P < .001). The proportion of viruses classified as recombinants significantly increased by nearly four-fold from 12.2% in 1995 to 44.8% in 2017. Inter-subtype HIV diversity has generally increased. While intra-subtype p24 genetic diversity and divergence leveled off after 2014, intra-subtype gp41 diversity, effective population size, and divergence increased through 2017. Intra- and inter-subtype viral diversity increased across all demographic and behavioral population subgroups, including among individuals with no recent migration history or extra-community sexual partners. This study provides insights into population-level HIV evolutionary dynamics following the scale-up of HIV prevention and treatment programs. Continued molecular surveillance may provide a better understanding of the dynamics driving population HIV evolution and yield important insights for epidemic control and vaccine development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2025

The relationship between below average cognitive ability at age 5 years and the child’s experience of school at age 9

Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.

Bowe, Andrea K.↗
DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗
DOE OSTI2025

Ionic‐Liquid Free and Flexible Transistors Made of 2D Material Inks

The development of thin-film transistors (TFTs) using 2D materials is crucial for enabling scalable, low-cost, and flexible electronics. Currently, 2D TFTs with the highest performance have been achieved by using ionic-liquid gating (ILG), a technique suited for proof-of-concept studies. However, ILG suffers from slow switching speeds, temperature sensitivity, poor long-term stability, and integration challenges, making it unsuitable for practical use. Moreover, typical fabrication methods for 2D TFTs involve harsh conditions such as strong acids or high temperatures (>300 °C), limiting integration with flexible substrates. This work provides the first demonstration of an ILG-free, all-2D-material TFT fabricated onto a flexible substrate. Water-based graphene and hexagonal boron nitride (h-BN) inks are printed to deposit the electrodes and dielectric layers, respectively. The MoS 2 channel is produced via supramolecular interfacial self-assembly, yielding uniform, monolayer-rich films transferable to rigid and flexible substrates. The resulting TFTs operate below 3 V, exhibit negligible leakage current, and achieve field-effect mobilities up to 0.46 cm 2 V −1 s −1 (rising to 2.47 cm 2 V −1 s −1 with silver electrodes) measured under ambient conditions, while maintaining excellent mechanical flexibility. This work establishes a low-cost and scalable solution-processable platform for flexible electronics based on 2D materials that match requirements for practical applications.

2D materials↗
DOE OSTI2025

Interface chemistry informs about cathode and anode degradation during fast charging

Here, the aim of this study is to examine the development and makeup of the Solid Electrolyte Interphase (SEI) and Cathode Electrolyte Interphase (CEI) in lithium-ion batteries during rapid charging. Using X-ray photoelectron spectroscopy (XPS) and depth profiling, we investigate the chemical modifications occurring on the electrode surfaces during the initial formation and subsequent fast charge-discharge cycles. Our research shows that the anode's SEI initially comprises a thin layer rich in ketones, which then transforms into a thicker layer dominated by carbonaceous compounds during rapid charging cycles. Following assembly, lithium fluoride (LiF) quickly becomes a key element of the SEI, and its presence continues to grow substantially during the formation cycle, remaining the primary component throughout subsequent cycles. Initially, the cathode forms a thin oxide layer rich in ketones, with no noticeable carbonaceous CEI. The CEI primarily comprises LiF, which experiences an increase during the formation cycle and retains a thin layer of carbon coating after the initial rapid discharge. These findings show how the interphase layers impact the performance and stability of lithium-ion batteries, especially during fast charging for electric vertical take-off and landing (eVTOL) vehicles.

Fast charging↗
DOE OSTI2025

Ligand‐Induced Crystallization Control in MAPbBr 3 Hybrid Perovskites for High Quality Nanostructured Films

AbstractControlling the formation of hybrid perovskite thin films is crucial in obtaining high‐performance optoelectronic devices, since factors like morphology and film thickness have a profound impact on a film's functionality. For light‐emitting applications grain sizes in the sub‐micrometer‐range have previously shown enhanced brightness. It is therefore crucial to develop simple, yet reliable methods to produce such films. Here, a solution‐based synthesis protocol for the on‐substrate formation of MAPbBr3 (MA = methylammonium) nanostructures by adding the bifunctional rac‐3‐aminobutyric acid to the precursor solution is reported. This synthesis route improves key optical properties such as photoluminescence quantum yields and life times of excited states by inducing a controlled slow‐down of the film formation and suppressing agglomeration effects. In situ spectroscopy reveals a delayed and slowed down crystallization process, which achieves synthesis of perovskite structures with much reduced defect densities. Further, aggregation can be controlled by the amount of amino acid added and adjusting the synthesis protocol allows to produce cubic crystallites with targeted size from nanometer to micrometer scales. The nanocrystalline MAPbBr3 samples show enhanced amplified spontaneous emission (ASE) intensities, reduced ASE thresholds and purer ASE signals, compared to pristine films, even under intense optical driving, making them promising structures for lasing applications.

36 MATERIALS SCIENCE↗