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DOE OSTI2025

Enhancing cathode composites with conductive alignment synergy for solid-state batteries

Enhancing transport and chemomechanical properties in cathode composites is crucial for the performance of solid-state batteries. Our study introduces the filler-aligned structured thick (FAST) electrode, which notably improves mechanical strength and ionic/electronic conductivity in solid composite cathodes. The FAST electrode incorporates vertically aligned nanoconducting carbon nanotubes within an ion-conducting polymer electrolyte, creating a low-tortuosity electron/ion transport path while strengthening the electrode’s structure. This design not only mitigates recrystallization of the polymer electrolyte but also establishes a densified local electric field distribution and accelerates the migration of lithium ions. The FAST electrode showcases outstanding electrochemical performance with lithium iron phosphate as the active material, achieving a high capacity of 148.2 milliampere hours per gram at 0.2 C over 100 cycles with substantial material loading (49.3 milligrams per square centimeter). This innovative electrode design marks a remarkable stride in addressing the challenges of solid-state lithium metal batteries.

Science & Technology - Other Topics↗

DOE OSTI2023

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Are CO2 batteries ready for the grid? A cross-cutting deployment checklist

Metal-CO2 batteries have attracted considerable research interest in the past decade due to the utilization of the greenhouse CO2 gas. The low cost and abundance of CO2 feedstocks, together with high specific capacity metal anodes, make this technology a promising candidate for electricity-demanding data centers and grids. However, nonstandard data reporting, ambiguous hardware designs, and questionable cycling parameters have emerged as the field has grown. A clear checklist and standardized reporting format for research and development are urgently needed. Here, after carefully re-analyzing 15 Na-CO2 and 9 Li-CO2 studies, we propose a general checklist and a flowchart for CO2 batteries and related gas conversion systems. Specific performance values from a deployment-oriented perspective, based on commercial lithium iron phosphate batteries, are integrated into the list. We frame research and development efforts around grid-relevant outputs, including areal rate and capacity, cyclability, gas utilization, and safety. While illustrated for Na- and Li-CO2 cells, this framework is directly transferable to other CO2 batteries and air/oxygen batteries, helping align benchtop demonstrations with systematic metrics that ultimately govern future deployment.

Wu, Wenda [ORNL] (ORCID:000900033307687X)↗

DOE OSTI2026

Mitigating electrochemical degradation in CsPbBr{sub 3} gamma detectors by organic and inorganic encapsulation.

CsPbBr3 perovskite semiconductors have emerged as a leading candidate for nextgeneration radiation detectors because of their exceptional charge transport properties, defect tolerance, and record-breaking sensitivity and energy resolution. Their long-term stability, however, is hindered by electrode-driven electrochemical decomposition, which is accelerated by moisture- and oxygen-assisted ion migration during operation. Here, we investigated organic and inorganic encapsulation strategies as both environmental barriers and means to suppress interfacial degradation pathways. Atomic layer deposition (ALD) of Al2O3 provided a conformal passivation layer that blocked environmental ingress, suppressed ionic diffusion, reduced leakage current, enhanced energy resolution and expanded the operational electric-field window beyond 5 kV∙cm1 . By contrast, organic encapsulants such as paraffin wax and polystyrene slowed moisture diffusion but did not suppress interfacial reactions, with wax extending stability to over 90 days. These results show that ALD-Al2O3 suppresses dominant interfacial degradation pathways, enabling stable, high-field operation and advancing the practical deployment of CsPbBr3 γ-ray detectors.

Unal, Mustafa↗

DOE OSTI2024

Direct Visualization of Defect‐Controlled Diffusion in van der Waals Gaps

Abstract Diffusion processes govern fundamental phenomena such as phase transformations, doping, and intercalation in van der Waals (vdW) bonded materials. Here, the diffusion dynamics of W atoms by visualizing the motion of individual atoms at three different vdW interfaces: hexagonal boron nitride (BN)/vacuum, BN/BN, and BN/WSe 2 , by recording scanning transmission electron microscopy movies is quantified. Supported by density functional theory (DFT) calculations, it is inferred that in all cases diffusion is governed by intermittent trapping at electron beam‐generated defect sites. This leads to diffusion properties that depend strongly on the number of defects. These results suggest that diffusion and intercalation processes in vdW materials are highly tunable and sensitive to crystal quality. The demonstration of imaging, with high spatial and temporal resolution, of layers and individual atoms inside vdW heterostructures offers possibilities for direct visualization of diffusion and atomic interactions, as well as for experiments exploring atomic structures, their in situ modification, and electrical property measurements of active devices combined with atomic resolution imaging.

Chemistry↗

DOE OSTI2026

Data-Driven Discovery and Experimental Validation of Solvent Polarity Effects on Conjugated Polymer Solution-to-Film Assembly Pathways

Understanding how solvent properties influence the solution-to-film assembly of conjugated polymers remains a critical challenge due to the complex and intertwined nature of polymer–solvent interactions. In this study, we integrate a data-driven framework with experimental validation to identify key parameters influencing the assembly and performance of poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno[3,2-b]thiophene)] (DPP-DTT) in organic field-effect transistors (OFETs). A machine learning (ML) approach identified the normalized Reichardt polarity parameter (E T N ) as a significant descriptor correlated with DPP-DTT hole mobility (μ). Systematic DPP-DTT devices fabricated using solvents across a wide E T N range revealed that higher E T N solvents yield enhanced μ. To elucidate the structural origins of high μ, we conducted comprehensive analyses using UV–vis–NIR spectroscopy and grazing incidence wide angle X-ray scattering (GIWAXS) measurements. The results revealed that films processed from high E T N solvents exhibit reduced paracrystallinity. By analyzing the solution-state behavior using optical microscopy and solution WAXS, we revealed polymer solubility differences in the various solvents and associated distinct polymer assembly pathways, elucidating why the high E T N solvent produces long-range ordered films. Notably, the high E T N solvent shows a pronounced preference for liquid-crystal (LC)-mediated assembly, providing a mechanistic explanation for the enhanced structural order. Therefore, these results demonstrate that solvent polarity, as evaluated by E T N , serves as an important parameter that plays a significant role in the DPP-DTT assembly pathway and resultant solid-state morphology. This work provides a strategy for integrating data science with experiments to identify critical parameters associated with complex polymer systems and helps guide rational process design for high-performance organic electronics.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Comparative Assessment of Battery Carbon Footprint Calculation Frameworks to Support U.S. Battery Manufacturing

A “battery passport” is a digital record that provides comprehensive information about an individual battery across its life cycle. This concept has been introduced by Battery Regulation (EU) 2023/1542 [1], which applies to batteries for electric vehicles (EVs), industrial batteries with a capacities greater than 2 kWh, and batteries for light means of transport (LMT) greater than 2kWh sold in the European Union (EU) market – impacting manufacturers and exporters across multiple jurisdictions globally. Among its reporting mandates, a key feature of battery passports is the requirement for a carbon footprint (CF) calculation methodology supported by enhanced data granularity to enable traceable and verifiable CF results. While no other jurisdictions have yet adopted formal battery passport requirements like the EU’s, some are developing CF calculation methods to comply with the EU Battery Regulation or are creating CF-related regulations that could evolve in a similar direction, reflecting the growing importance of battery CF guidelines for manufacturers seeking to remain competitive in global markets.

Zhang, Jingyi [Argonne National Laboratory (ANL), ↗

DOE OSTI2026

R&D GREET Battery Carbon Footprint Calculator

The Battery Carbon Footprint (CF) Calculator was developed to help U.S. battery manufacturers meet the carbon footprint reporting requirements of the EU Battery Regulation (EU) 2023/1542. The calculator incorporates several major battery carbon footprint frameworks, including the Joint Research Centre's Rules for the Calculation of the Carbon Footprint of Electric Vehicle Batteries (CFB-EV), RECHARGE's Product Environmental Footprint Category Rules for High Specific Energy Rechargeable Batteries for Mobile Applications (PEFCR), the Catena-X Product Carbon Footprint Rulebook (CX-PCF Rules), Battery Pass's Battery Carbon Footprint: Rules for Calculating the Carbon Footprint of the "Distribution" and "End-of-Life and Recycling" Life Cycle Stages, the Global Battery Alliance's Greenhouse Gas Rulebook: Generic Rules, Version 2.1, and the Ministry of Economy, Trade and Industry's draft Carbon Footprint Calculation Method for Automotive Batteries. The tool pairs these frameworks with foreground data from Argonne's R&D GREET models and integrates user-supplied background data covering battery manufacturing and supply chain activities. By bringing multiple international methodologies together in a single platform, the calculator enables manufacturers to evaluate product carbon footprints, improve data consistency, and prepare for evolving regulatory compliance and global market reporting requirements.

Zhang, Jingyi↗

DOE OSTI2022

Exceedance Probabilities and Recurrence Intervals for Extended Power Outages in the United States

This report provides estimates of recurrence intervals and conditional exceedance probabilities for major power outages by U.S. region between 2015 and 2021. Additionally, we provide estimates for grid management, particularly outages caused by California’s public safety power shutoffs (PSPS), and for natural outages caused by major hurricanes. Outage recurrence intervals are the average number of years between outage events, and conditional exceedance probabilities are the likelihoods that a customer who experiences a major power outage will experience an outage exceeding a given duration. Major outage events are those that affect 10,000 or more customers, as defined by the U.S. Department of Energy’s (DOE’s) Electric Emergency Incident and Disturbance Report, called OE-417 (DOE 2020). These results can be applied to determine the likelihood of experiencing long-duration outages, which can be integrated into cost-benefit analyses of resilience solutions and broader energy resilience studies.

Ericson, Sean [National Renewable Energy Laborator↗

DOE OSTI2026

A Reflection on the Evolution of Sodium/Metal Chloride (ZEBRA) Batteries and Lithium-Ion Cathode Materials (1975–2025): A Tribute to Johan Coetzer

This narrative summarizes, in retrospect, the enormous and quietly-conducted contribution of Dr Johan Coetzer, a South African scientist and entrepreneur, to the discovery, development and implementation of the high temperature sodium—metal chloride “ZEBRA” battery (Na/β-Al 2 O 3 , NaAlCl 4 /MCl 2 (M = Ni, Fe)). Here, this research activity was initiated in the mid 1970’s at the Council for Scientific and Industrial Research (CSIR) in South Africa and subsequently developed primarily in partnership with the Atomic Energy Research Establishment (AERE, Harwell, UK), Beta R&D (Derby, UK) and Daimler Benz (Germany), before being transferred to industry worldwide for electric vehicle and energy storage applications, albeit at a relatively low production rate. Coetzer’s wide-ranging, innovative and, at times, unconventional scientific approach also laid the foundation for the discovery and implementation of manganese-based spinel and layered metal oxide cathode materials for the Li-ion battery industry.

ZEBRA Battery↗

DOE OSTI2026

Evaluating technology upgrades as a complement to traditional bill assistance programs

State programs to improve energy affordability and reduce energy burden (the share of income spent on energy) could mitigate concerns about rising electricity prices. Many states offer bill assistance and rebates for weatherization and rooftop solar to income-qualifying households. Here, we analyse how these approaches may complement one another to improve energy affordability. Results illustrate trade offs between program costs and energy burden reduction, as well as between a program’s upfront and ongoing costs. Generally, the three strategies complement one another to improve energy affordability at lower net present cost than bill assistance alone, with varying effects across regions. Weatherization can reduce the solar installation capacity needed, and both together can reduce or eliminate ongoing reliance on bill assistance. Under full uptake among cost-effective households, fully rebated weatherization and solar rebated at $0.48/Watt, combined with bill assistance, yield the same modelled net present cost as bill assistance alone while reducing the share of low-income owner-occupied households with high energy burdens from 66% to 19%, compared with 34% under bill assistance alone. Absent tax credits, weatherization still reduces energy burden and bill assistance program costs, whereas solar may not be cost-competitive.

Forrester, Sydney P↗

DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗

DOE OSTI2025

Gauge theory of giant phonon magnetic moment in doped Dirac semimetals

Here, we develop a quantitative theory of phonon magnetic moment in doped Dirac semimetals. Our theory is based on an emergent gauge field approach to the electron-phonon coupling, applicable to gapless systems. We find that the magnetic moment is directly proportional to the electrical Hall conductivity through the phonon Hall viscosity. Our theory is combined with the first-principles calculations, allowing us to quantitatively implement it to realistic materials. Magnetic moments are found to be of the order of a Bohr magneton for Raman-active phonon modes in graphene and Cd 3 ⁢As 2 . Our results provide practical guidance for the dynamical generation of large magnetization in quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Abstract As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.

Optics↗

DOE OSTI2025

Reversible control over the distribution of chemical inhomogeneities in multiferroic BiFeO3

Abstract Despite the appeal of flawless order, semiconductor technology has demonstrated that implanting inhomogeneities into single-crystalline materials is pivotal for modern electronics. However, the influence of the local arrangement of chemical inhomogeneities on the material’s functionalities is underexplored. In this work, we control the distribution of chemical inhomogeneities in La 3+ -substituted ferroelectric BiFeO 3 thin films. By means of a stress- and composition-driven phase transition, we trigger the formation of a lattice of La 3+ -rich and La 3+ -poor layers. This ordering correlates with the emergence of an antipolar phase. An electric field restores the original ferroelectric phase and re-randomizes the distribution of the La 3+ inhomogeneities. Leveraging these insights, we tune the polar/antipolar phase coexistence to set the net polarization of La 0.15 Bi 0.85 FeO 3 to any desired value between its saturation limits. Finally, we control the net polarization response in device-compliant capacitor heterostructures to show that inhomogeneity-distribution control is a valuable tool in the design of functional oxide electronics.

Science & Technology - Other Topics↗

DOE OSTI2025

Feasibility of Multi-Use Ocean Thermal Energy Conversion (OTEC) Platforms

Tropical islands and coastal communities in the United States and internationally suffer from high energy costs, unreliable electrical supplies, poverty, and underemployment, which are all being exacerbated by climate change. Multi-use Ocean Thermal Energy Conversion (OTEC) systems could align with the goals and values of these underserved and remote communities as scalable platforms that can provide baseload power, freshwater, and food, as well as potentially support new industries. The cold, nutrient-rich deep seawater needed for OTEC’s power cycles has many promising byproduct applications such as seawater air conditioning (SWAC) for residential and industrial cooling, enhancing aquaculture farms (e.g., cooling and nutrients for seaweed, shellfish, finfish), producing freshwater through desalination, and generating efuels such as ammonia and hydrogen.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Polaron catastrophe within quantum acoustics

The quantum acoustic framework has recently emerged as a nonperturbative, coherent approach to electron–lattice interactions, uncovering rich physics often obscured by perturbative methods with incoherent scattering events. Here, we model the strongly coupled dynamics of electrons and acoustic lattice vibrations within this framework, representing lattice vibrations as coherent states and electrons as quantum wave packets, in a manner distinctively different from tight-binding or discrete hopping-based approaches. We derive and numerically implement electron backaction on the lattice, providing both visual and quantitative insights into electron wave packet evolution and the formation of acoustic polarons. We investigate polaron binding energies across varying material parameters and compute key observables—including mean square displacement, kinetic energy, potential energy, and vibrational energy—over time. Our findings reveal the conditions that favor polaron formation, which is enhanced by low temperatures, high deformation potential constants, slow sound velocities, and high effective masses. Additionally, we explore the impact of external electric and magnetic fields, showing that while polaron formation remains robust under moderate fields, it is weakly suppressed at higher field strengths. These results deepen our understanding of polaron dynamics and pave the way for future studies into nontrivial transport behavior in quantum materials.

Science & Technology - Other Topics↗

DOE OSTI2026

Oversizing and Part-Load Problems

Oversizing, the common engineering practice of specifying devices with capacity exceeding the actual load requirement, is a widespread practice across virtually all building technologies end-use categories, including HVAC, electrical systems, lighting, appliances, and plug loads. This practice, driven by factors like design uncertainty, institutional pressures, and risk aversion, results in wasted capital investment, control difficulties, and excessive energy consumption due to inefficient part-load operation. Part-load operation, where devices run below maximum capacity, is the dominant operating mode in most energy systems and presents a complex design challenge. Solutions to match output to load fall into three broad categories: constraining the output, adjusting the device’s internal behavior, and linking output to energy storage or other waste-heat reuse applications. The energy implications of part-load are critical, as efficiency often drops sharply as load decreases across a wide diversity of devices. To quantify the extent of this problem, we derive a dimensionless Part-Load Metric (PLM) based on device efficiency and its frequency distribution of operating hours at various output levels. The PLM quantifies the deviation of a device's actual efficiency from its maximum design efficiency. This metric also serves as a measure of "capital inefficiency," enabling engineers to compare the impact of different part-load solutions and providing a unified framework for evaluating performance across various devices and systems.

Meier, Alan↗