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DOE OSTI2015

Bridging the PSI Knowledge Gap: A Multi-Scale Approach

Plasma-surface interactions (PSI) pose an immense scientific hurdle in magnetic confinement fusion and our present understanding of PSI in confinement environments is highly inadequate; indeed, a recent Fusion Energy Sciences Advisory Committee report found that 4 out of the 5 top five fusion knowledge gaps were related to PSI. The time is appropriate to develop a concentrated and synergistic science effort that would expand, exploit and integrate the wealth of laboratory ion-beam and plasma research, as well as exciting new computational tools, towards the goal of bridging the PSI knowledge gap. This effort would broadly advance plasma and material sciences, while providing critical knowledge towards progress in fusion PSI. This project involves the development of a Science Center focused on a new approach to PSI science; an approach that both exploits access to state-of-the-art PSI experiments and modeling, as well as confinement devices. The organizing principle is to develop synergistic experimental and modeling tools that treat the truly coupled multi-scale aspect of the PSI issues in confinement devices. This is motivated by the simple observation that while typical lab experiments and models allow independent manipulation of controlling variables, the confinement PSI environment is essentially self-determined with few outside controls. This means that processes that may be treated independently in laboratory experiments, because they involve vastly different physical and time scales, will now affect one another in the confinement environment. Also, lab experiments cannot simultaneously match all exposure conditions found in confinement devices typically forcing a linear extrapolation of lab results. At the same time programmatic limitations prevent confinement experiments alone from answering many key PSI questions. The resolution to this problem is to usefully exploit access to PSI science in lab devices, while retooling our thinking from a linear and de-coupled extrapolation to a multi-scale, coupled approach. The PSI Plasma Center consisted of three equal co-centers; one located at the MIT Plasma Science and Fusion Center, one at UC San Diego Center for Energy Research and one at the UC Berkeley Department of Nuclear Engineering, which moved to the University of Tennessee, Knoxville (UTK) with Professor Brian Wirth in July 2010. The Center had three co-directors: Prof. Dennis Whyte led the MIT co-center, the UCSD co-center was led by Dr. Russell Doerner, and Prof. Brian Wirth led the UCB/UTK center. The directors have extensive experience in PSI and material research, and have been internationally recognized in the magnetic fusion, materials and plasma research fields. The co-centers feature keystone PSI experimental and modeling facilities dedicated to PSI science: the DIONISOS/CLASS facility at MIT, the PISCES facility at UCSD, and the state-of-the-art numerical modeling capabilities at UCB/UTK. A collaborative partner in the center is Sandia National Laboratory at Livermore (SNL/CA), which has extensive capabilities with low energy ion beams and surface diagnostics, as well as supporting plasma facilities, including the Tritium Plasma Experiment, all of which significantly augment the Center. Interpretive, continuum material models are available through SNL/CA, UCSD and MIT. The participating institutions of MIT, UCSD, UCB/UTK, SNL/CA and LLNL brought a formidable array of experimental tools and personnel abilities into the PSI Plasma Center. Our work has focused on modeling activities associated with plasma surface interactions that are involved in effects of He and H plasma bombardment on tungsten surfaces. This involved performing computational material modeling of the surface evolution during plasma bombardment using molecular dynamics modeling. The principal outcomes of the research efforts within the combined experimental – modeling PSI center are to provide a knowledgebase of the mechanisms of surface degradation, and the influence of the surface on plasma conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Neutrino Theory in the Precision Era

This document summarises discussions on future directions in theoretical neutrino physics, which are the outcome of a neutrino theory workshop held at CERN in February 2025. The starting point is the realisation that neutrino physics offers unique opportunities to address some of the most fundamental questions in physics. This motivates a vigorous experimental programme which the theory community fully supports. \textbf{A strong effort in theoretical neutrino physics is paramount to optimally take advantage of upcoming neutrino experiments and to explore the synergies with other areas of particle, astroparticle, and nuclear physics, as well as cosmology.} Progress on the theory side has the potential to significantly boost the physics reach of experiments, as well as go well beyond their original scope. Strong collaboration between theory and experiment is essential in the precision era. To foster such collaboration, \textbf{we propose to establish a CERN Neutrino Physics Centre.} Taking inspiration from the highly successful LHC Physics Center at Fermilab, the CERN Neutrino Physics Centre would be the European hub of the neutrino community, covering experimental and theoretical activities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Experimental and Modeling Investigation of Overcooling Transients that Include Freezing, in Fluoride-Salt Cooled High-Temperature Reactors (FHRs). Final Report

The goal of this project was an experimental and modeling investigation of overcooling transients that include freezing in Fluoride-Salt Cooled High-Temperature Reactors (FHRs). The most impactful product of this research collaboration between Professor Scarlat’s group at UW Madison and subsequently at UC Berkeley, and Dr. Rui Hu’s group at Argonne National Laboratory was the initial demonstration of SAM application to FHR transients, and the generation of a freezing module for the SAM code. The national laboratory and industry graduate student internships enabled by this project were valuable for workforce development, and also for knowledge transfer between university, national lab, and industry.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Nondestructive In Operando Imaging of Thin Film Composite Membrane Compaction Enhanced by AI-Based Segmentation

Reverse osmosis (RO) membranes are essential for desalination and water reuse, yet their permeability declines in high-pressure applications due to membrane compaction. This study investigates the structural and functional responses of commercial brackish, seawater, and high-pressure RO membranes at applied pressures up to 120 bar using a multiscale, nondestructive in operando scanning electron microscopy (iSEM) imaging platform. The iSEM technique reveals progressive densification across the composite membrane structure, which correlates with observed declines in water and solute permeance. To quantify these structural changes with greater fidelity, we combined X-ray computed tomography with AI-based segmentation enabling precise analysis of pore size distribution and thickness of the polysulfone support layer. Compared to traditional thresholding, AI segmentation accurately delineates material phases and void spaces, enhancing the reproducibility and resolution of morphological assessments. The results demonstrate that compaction-induced reductions in porosity and thickness strongly impact membrane transport properties. These findings provide mechanistic insights into the compaction behavior of RO membranes and underscore the potential for advanced imaging and AI-driven data analysis to guide the design of next-generation membranes with improved mechanical resilience and operational longevity.

13 HYDRO ENERGY↗

DOE OSTI2025

Proceedings for the Workshop on Applied Nuclear Data Activities 2025

The 2025 Workshop for Applied Nuclear Data Activities (WANDA) covered four topic areas in nuclear data: Nuclear Data and Deterrence, Nuclear Data Prioritization for Fusion, High-Assay Low-Enriched Uranium and Novel Moderators for Advanced Reactors, and Data Preservation and Data Workflows. The intention of this workshop is to connect different communities that are invested in nuclear data and have their own unique sets of needs for the purposes of sharing information, fostering collaboration in areas of shared interest, and leveraging synergistic capabilities. The attendance of federal program managers at these workshops is essential in creating awareness of the needs of their respective communities and in providing information to better guide funding investments. In each of these topical sessions, a general description of the nuclear data needs and/or capabilities was presented, along with discussions of existing capabilities that could be leveraged, potential synergistic needs or resources, and challenges that must be overcome for the application space to progress. There are many synergistic nuclear data needs among these application spaces. The discussion largely focused on increasing the accuracy of the nuclear data and better quantifying the data uncertainties that have the greatest impact on applications. The full-day session on data processing and data workflows was by nature intended to be synergistic and applicable to all technical sessions at WANDA. A notable common theme that was highlighted across all sessions was the need for accelerated delivery of nuclear data products across complex and time-consuming workflows.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Recent Advances in the Use of Ionic Liquids and Deep Eutectic Solvents for Lignocellulosic Biorefineries and Biobased Chemical and Material Production

Biorefineries, which process biomass feedstocks into valuable (bio)products, aim to replace fossil fuel-based refineries to produce energy and chemicals, reducing environmental and health hazards, including climate change, and supporting a sustainable economy. In particular, lignocellulose-based biorefineries, utilizing the most abundant renewable feedstock on Earth, have significant potential to supply sustainable energy, chemicals and materials. Ionic liquids (ILs, organic salts with low melting temperatures) and deep eutectic solvents (DESs, mixtures with eutectic points lower than the ideal mixture) are capable of dissolving some of the key lignocellulose polymers, and even the whole biomass. Furthermore, they have intrinsic advantages over molecular solvents, including safer usage profiles and high tunability, which allow tailored physicochemical properties. Such properties provide unique opportunities for the development of new processes that could unlock the full potential of future biorefineries. Here, we review the current state of lignocellulosic biomass processing with ILs and DESs, with a specific focus on the pretreatment chemistry, process flow and products from each component; followed by discussions on sustainability assessments and technological challenges. We aim to inform the research community about the opportunities, challenges and perspectives in developing truly sustainable lignocellulose-based biorefineries.

09 BIOMASS FUELS↗

DOE OSTI2026

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗

DOE OSTI2025

AI Benchmark Democratization and Carpentry

Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring dynamic, AI-focused workflows. Rapid evolution in model architectures, scale, datasets, and deployment contexts makes evaluation a moving target. Large language models often memorize static benchmarks, causing a gap between benchmark results and real-world performance. Beyond traditional static benchmarks, continuous adaptive benchmarking frameworks are needed to align scientific assessment with deployment risks. This calls for skills and education in AI Benchmark Carpentry. From our experience with MLCommons, educational initiatives, and programs like the DOE's Trillion Parameter Consortium, key barriers include high resource demands, limited access to specialized hardware, lack of benchmark design expertise, and uncertainty in relating results to application domains. Current benchmarks often emphasize peak performance on top-tier hardware, offering limited guidance for diverse, real-world scenarios. Benchmarking must become dynamic, incorporating evolving models, updated data, and heterogeneous platforms while maintaining transparency, reproducibility, and interpretability. Democratization requires both technical innovation and systematic education across levels, building sustained expertise in benchmark design and use. Benchmarks should support application-relevant comparisons, enabling informed, context-sensitive decisions. Dynamic, inclusive benchmarking will ensure evaluation keeps pace with AI evolution and supports responsible, reproducible, and accessible AI deployment. Community efforts can provide a foundation for AI Benchmark Carpentry.

von Laszewski, Gregor [Virginia U.]↗

DOE OSTI2025

Testing CP properties of the Higgs boson coupling to τ leptons with heterogeneous graphs

In this paper we explore the possibility of utilizing Deep Learning in measuring the CP properties of the coupling of Higgs boson to τ leptons at the High Luminosity Large Hadron Collider. We employ three Deep Learning (DL) networks, Multi-Layer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Transformer Network (GTN) to enhance signal-to-background separation. The angle between τ lepton decay planes at the detector level is CP-sensitive observables, and we develop Heterogeneous Graphs that integrate diverse node and edge structures to incorporate the CP-sensitive observable efficiently. Using simplified detector simulations we estimate the reconstruction accuracy of the angle between τ lepton planes at the detector level, considering hadronic τ decay modes and standard model backgrounds. With $\sqrt{s}$ = 14 TeV and $\mathcal{L}$ = 100 fb -1 , MLP excludes CP mixing angles above 20° at 68% confidence level (CL), while GCN and GTN achieve exclusions at 90% CL and 95% CL, respectively. The networks also achieve a 3σ significance in excluding a pure CP-odd state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Diffusion of Innovations: Interplay of Social, Economic, Technological, and Policy Drivers in the Solar Industry. Summary of UT Austin Student Capstone Research Projects

The University of Texas at Austin’s Policy Research Project (PRP), a nine-month (two semesters) capstone, is a keystone of the core curriculum at the LBJ School of Public Affairs. In PRPs, small groups of students, under the mentorship of a faculty director, take on real-world problems that require special knowledge and skill sets. PRPs expose students to challenges in formulating and executing research, and in communicating academic research and related complex data to broader stakeholder communities and decision makers. The PRP structure is an innovative and effective approach for integrating research within the teaching and training of graduate students who are preparing themselves to address important real-world problems at the intersection of society, economics, technology, and policy. The project summaries below describe seven papers developed during September 2017 – May 2018 as part of a PRP on “Diffusion of Innovations: Interplay of Social, Economic, Technological, and Policy Drivers in the Solar Industry.” Twenty graduate students, drawn from the LBJ School’s Masters in Public Affairs and Masters in Global Policy Studies programs and the Jackson School Geoscience’s Energy and Earth Resources program, participated in this PRP. Dr. Varun Rai, Associate Professor and Associate Dean for Research at the LBJ School, directed the PRP, with support from his research team including: Dr. Ariane Beck, Dr. Ashok Sekar, D. Cale Reeves, and Erik Funkhouser. Clients for the project included the U.S. Department of Energy (Casey Canfield), Lawrence Berkeley National Laboratory (Ben Hoen, Galen Barbose Joachim Seel, Naïm Darghouth, Ryan Wiser), and National Renewable Energy Laboratory (Benjamin Sigrin, Eric O’Shaughnessy). The seven projects separately addressed one of the following topics: (1) low- and middle-income PV adoption, (2) modeling economic and information intervention design, (3) evaluation of DOE’s Solar in Your Community Challenge, (4) property value impacts near large-scale solar facilities, (5) solar market maturity and evolution of business models, (6) social media data for predicting PV adoption, and (7) individual-level variation in adoption of innovations. Many of the papers relied on data collected and curated by Lawrence Berkeley National Laboratory, including data embedded within the annual Tracking the Sun and Utility-Scale Solar reports. Each of the seven teams in the PRP prepared a research paper. The PRP culminated with a full-day conference at UT Austin in May 2018 to present findings from the seven projects in this PRP to a broad audience of about 75 experts from academia, national labs, industry, and government from across the country.

14 SOLAR ENERGY↗

DOE OSTI2011

Proceedings of RIKEN BNL Research Center Workshop: Brookhaven Summer Program on Nucleon Spin Physics

Understanding the structure of the nucleon is of fundamental importance in sub-atomic physics. Already the experimental studies on the electro-magnetic form factors in the 1950s showed that the nucleon has a nontrivial internal structure, and the deep inelastic scattering experiments in the 1970s revealed the partonic substructure of the nucleon. Modern research focuses in particular on the spin and the gluonic structure of the nucleon. Experiments using deep inelastic scattering or polarized p-p collisions are carried out in the US at the CEBAF and RHIC facilities, respectively, and there are other experimental facilities around the world. More than twenty years ago, the European Muon Collaboration published their first experimental results on the proton spin structure as revealed in polarized deep inelastic lepton-nucleon scattering, and concluded that quarks contribute very little to the proton's spin. With additional experimental and theoretical investigations and progress in the following years, it is now established that, contrary to naive quark model expectations, quarks and anti-quarks carry only about 30% of the total spin of the proton. Twenty years later, the discovery from the polarized hadron collider at RHIC was equally surprising. For the phase space probed by existing RHIC experiments, gluons do not seem to contribute any to the proton's spin. To find out what carries the remaining part of proton's spin is a key focus in current hadronic physics and also a major driving force for the new generation of spin experiments at RHIC and Jefferson Lab and at a future Electron Ion Collider. It is therefore very important and timely to organize a series of annual spin physics meetings to summarize the status of proton spin physics, to focus the effort, and to layout the future perspectives. This summer program on 'Nucleon Spin Physics' held at Brookhaven National Laboratory (BNL) on July 14-27, 2010 [http://www.bnl.gov/spnsp/] is the second one following the Berkeley Summer Program taken place in June of 2009. This program at BNL focused on theory and had many presentations on a wide range of theoretical aspects on nucleon spin, from perturbative-QCD calculations to models, and to the first principle lattice calculation. It also had a good number of summary talks from all major experimental collaborations on spin physics. The program facilitated many discussions between theorists as well as experimentalists. With five transparencies from each presentation at the Summer Program, this proceedings provides a valuable summary on the status and progress, as well as the future prospects of spin physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Hydrogen Technology and Energy Curriculum (HyTEC)

The Lawrence Hall of Science of the University of California, Berkeley has collaborated with scientists and engineers, a local transit agency, school districts, and a commercial curriculum publisher to develop, field-test nationally, and publish a two-week curriculum module on hydrogen and fuel cells for high school science. Key partners in this project are the Schatz Energy Research Center (SERC) of Humboldt State University, the Alameda-Contra Costa Transit District (AC Transit), FilmSight Productions, Lab-Aids, Inc., and 32 teachers and 2,370 students in field-test classrooms in California, Connecticut, Ohio, New York, South Carolina, and Washington. Field-test teachers received two to three days of professional development before teaching the curriculum and providing feedback used for revision of the curriculum. The curriculum, titled Investigating Alternative Energy: Hydrogen and Fuel Cells and published by Lab-Aids, Inc., includes a teachers guide (with lesson plans, resources, and student handout pages), two interactive computer animations, a video, a website, and a laboratory materials kit. The project has been disseminated to over 950 teachers through awareness workshops at state, regional, and national science teacher conferences.

08 HYDROGEN↗

DOE OSTI2025

L-SCIE and SUPCRTNE model and database development

This progress report (Level 3 Milestone Number M3SF-25LL010301052) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Host Rock Properties & Processes Work Package SF-25LL01030105. The focus of this milestone is to create an initial SUPCRTNE database and extensively test the SUPCRTNE code. We prepared a draft manuscript describing our workflow and approach for developing a next-generation thermodynamic database for the Spent Fuel and High Level Waste Disposition campaign. The goal is to ease any future formal software qualification effort as was done on the Yucca Mountain Project for codes including SUPCRT92 and EQ3/6. We are attempting to extend the SUPCRTNE database to include more species, mainly from the NEA volumes. We can also take advantage of thermodynamic database activities that are ongoing at Thermochimie and as part of the EURADII program. However, our effort is limited by the funds available and the changes to program scope that was initiated in mid-FY25.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Lower Cost Renewable Nanoparticle Platform to Increases Production of Cathode Material (CRADA Final Report)

Employees of Sylvatex synthesized candidate cathode materials for lithium-ion batteries in PI’s labs and at the Molecular Foundry using sustainable methods. They then benchmarked the synthesized materials against available commercial cathode materials in lithium half-cells (coin cell configurations) to compare performance and ensure that their materials met or exceeded metrics of the commercial materials.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Consensus on Intermediate Scale Salt Field Test Design

This report summarizes the first stage in a collaborative effort by Sandia, Los Alamos, and Lawrence Berkeley National Laboratories to design a small-diameter borehole heater test in salt at the Waste Isolation Pilot Plant (WIPP) for the US Department of Energy Office of Nuclear Energy (DOE-NE). The intention is to complete test design during the remainder of fiscal year 2017 (FY17), and the implementation of the test will begin in FY18. This document is the result of regular meetings between the three national labs and the DOE-NE, and is intended to represent a consensus of these meetings and discussions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Draft Impacts: Modeling and Lab Validation (CRADA Final Report)

Chimney draft is a significant source of variability in real-world wood heater performance, yet laboratory tests conducted for certification do not capture this variability. Because draft varies with climate, chimney height, and home conditions, a heater that performs well in the lab can perform quite differently when installed in a home. Until now, wood heater manufacturers and installers have lacked the tools needed to anticipate how draft will vary across real installations or to advise on corrective measures when draft is too low or too high. The goal of this project was to develop and validate an open-source draft prediction tool for cordwood heater chimney systems. Over 18 months, Lawrence Berkeley National Laboratory (LBNL) and the Hearth, Patio & Barbecue Association (HPBA) collaborated to build a physics-based, Python tool that predicts how chimney draft evolves during operation, from ignition through steady burning. HPBA convened a stakeholder group of manufacturers and venting experts who provided feedback throughout development. LBNL validated the tool against laboratory measurements from a catalytic and a non-catalytic cordwood heater operated across a range of chimney heights and room-pressure conditions.

42 ENGINEERING↗

DOE OSTI2025

High-emissivity, thermally robust emitters for high power density thermophotovoltaics

Thermal radiative energy transport is essential for high-temperature energy harvesting technologies, including thermophotovoltaics (TPVs) and grid-scale thermal energy storage. However, the inherently low emissivity of conventional high-temperature materials constrains radiative energy transfer, thereby limiting system performance and technoeconomic viability. Here, in this study, we demonstrate ultrafast femtosecond laser-material interactions to transform diverse materials into near-blackbody surfaces with broadband spectral emissivity above 0.96. This enhancement arises from hierarchically engineered light-trapping microstructures enriched with nanoscale features, effectively decoupling surface optical properties from bulk thermomechanical properties. These laser-blackened surfaces (LaBS) exhibit exceptional thermal stability, retaining high emissivity for over 100 h at temperatures exceeding 1,000°C, even in oxidizing environments. When applied as TPV thermal emitters, Ta LaBS double electrical power output from 2.19 to 4.10 W cm −2 at 2,200°C while sustaining TPV conversion efficiencies above 30%. This versatile, largely material-independent technique offers a scalable and economically viable pathway to enhance emissivity for advanced thermal energy applications.

laser-blackened surfaces↗