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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Solvent-free and low temperature synthesis of chalcogenide Na superionic conductors for solid-state batteries

Sodium chalcogenide ionic conductors are attractive candidates as solid electrolytes (SEs) in solid-state Na metal batteries. They show the advantages of high ionic conductivity of 10 –4 –10 –2 S cm –1 at room temperature and great chemical stability in air. However, simple, efficient, and scalable approaches for the synthesis of chalcogenide solid electrolytes (SEs) are required. In this work, we report a solvent-free mixing to form dry intermediate products, which are subjected to different treatments (electron-beam assisted method or low temperature heating (≤150 °C)) to produce pure phase of Na 3 SbS 4-y Se y (0 ≤ y < 2) chalcogenides. Heavy Se-doping in Na 3 SbS 4 results in the tetragonal-to-cubic phase transition as well as a significant change of Sb-S bonding in Raman spectra. Among all chalcogenide SEs, Na 3 SbS 3 Se showed the highest ionic conductivity of 3.75 × 10 –4 S cm –1 at room temperature, 47% higher than that of pristine Na 3 SbS 4 . Moreover, the Se-dopant also enhanced the electrochemical stability towards Na metal in solid-state batteries. The solid-state Na||FeS 2 battery with Na 3 SbS 3 Se SE displayed long-term cycling ability up to 1,000 cycles within the voltage window of 1.0–2.7 V and retained a specific capacity of 105 mAh g –1 after 600 cycles. As a result, this technique promotes the practical applications of chalcogenide SEs in solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

Ambient-Pressure Chemical Recycling of Thermoset Carbon Fiber-Reinforced Polymers via Low-Temperature Solvolysis: Techno-Economic and Life-Cycle Assessment

Carbon fiber-reinforced polymer (CFRP) waste is rapidly accumulating, with global volumes projected to exceed 500,000 tons annually by 2050. Virgin carbon fiber production is extremely energy- and cost-intensive, and growing demand has intensified supply-chain vulnerabilities associated with precursor availability and reliance on critical materials. Scalable recycling technologies therefore offer not only environmental benefits but also opportunities to reduce material costs and strengthen domestic composite supply chains. This study introduces an efficient, mild chemical recycling method for epoxy-amine CFRPs, utilizing acetic acid (80 wt%) and zinc acetate (1 wt%) at atmospheric pressure and low temperature (~110 °C). The solvolysis process preserves residual resin (~40%) on recycled carbon fibers (rCF), enhancing interfacial bonding in remanufactured composite materials. Recycled fibers retain ~90% of their mechanical properties, and recovered oligomers show potential for reuse in epoxy formulations, supporting material circularity. Techno-economic analysis (TEA) and life-cycle assessment (LCA) confirm the scalability and sustainability of this process.

Recycling CFRP↗
DOE OSTI2025

Red-Light-Driven Biophotochemical Diode Based on a Microorganism–Silicon Nanowire Interface for Stable and Efficient Bias-Free CO 2 Reduction

Artificial photosynthesis offers a promising route for sustainable liquid fuel and feedstock production, yet integrating efficient CO 2 reduction catalysts with light-harvesting systems remains challenging. Here, in this study, we present a biophotochemical diode that couples microorganism-driven CO 2 reduction with glycerol oxidation, enabled by silicon nanowire photoelectrodes under varying red-light intensities. Tuning the biotic-abiotic interface─by increasing biocatalyst loading and adjusting the catholyte pH to mitigate local alkalization─significantly improves performance and stability. The enhanced-loading biocathode maintains a high faradaic efficiency across a wide potential range, even under elevated light intensities. At 60 mW/cm 2 , the system achieves a bias-free current density of 3.5 mA/cm 2 . Long-term stability testing at 40 mW/cm 2 demonstrates stable operation for over 100 h. The photoanode generates valuable C 3 products, primarily glycerate and lactate, enhancing the economic viability. This work showcases the importance of microenvironmental control at the biotic-abiotic interface and establishes a scalable platform for light-driven CO 2 reduction using earth-abundant silicon.

Artificial Photosynthesis↗
DOE OSTI2025

Nano-Engineered Interfaces in Dual-Layer Electrodes for Protonic Ceramic Cells with Enhanced Stability and Kinetics

Enhancing interfacial stability and charge transfer in protonic ceramic cells (PCCs) remains a critical challenge, as structural degradation and interfacial resistance often compromise durability and efficiency. Here, we report a nanoengineered dual-layer oxygen electrode architecture designed to address these limitations by introducing a fine-grained nanoparticle interfacial contact layer beneath a porous catalytic backbone. The nanoscale powders, through enhanced sintering activity, densify into a robust interfacial layer that promotes strong chemical bonding, uniform adhesion, and continuous ionic/electronic pathways with the BCZYYb electrolyte. This hierarchical architecture mitigates delamination, redistributes mechanical stress, and establishes efficient charge and mass transport channels without relying on corrosive surface treatments. Electrochemical evaluation demonstrates that the dual-layer design markedly reduces interfacial polarization resistance and accelerates electrode kinetics. Compared to the single-layer counterpart, the architecture achieves a peel strength of 44.53 N/cm 2 , a 40% improvement in peak power density (0.96 W cm –2 at 600 °C), and a 130% enhancement in electrolysis current density (4.78 A cm –2 at 1.57 V). Faradaic efficiency remains as high as 88% under high steam concentrations, underscoring minimal charge loss during practical operation. Notably, the electrode retains stability across 450–600 °C and under transient voltage cycling, with impedance spectra confirming suppressed interfacial resistance growth over prolonged use. These results highlight nanoscale interface engineering as a powerful route to enhance both mechanical robustness and electrochemical kinetics in PCCs. The demonstrated scalability and durability of this architecture provide a versatile platform for advancing solid-state electrochemical systems, including reversible fuel cells and high-efficiency hydrogen production technologies.

Faradaic efficiency↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2020

NREL's Capabilities in Organism Development and Fermentation Sciences Research and Development (R&D)

NREL is developing robust microbial strains and scalable fermentation technologies for converting biomass sugars to fuels and biochemicals, and one-carbon (C1) gaseous substrates (e.g., CO2, CO, CH4) to high-value products. Core capabilities include: (1) Fermentation optimization of aerobic, microaerophilic, and anaerobic cultivation processes of gas and traditional liquid fermentation R&D; (2) Gas mass transfer R&D and CFD modeling for improved reactor design and gas mixing (3) Metabolic engineering in CRISPR editing tools to improve titer, rates, and yields; (4) Probing and optimizing carbon, redox, and energy flux using transcriptomics and fluxomic techniques; and (5) Consolidated bioprocessing (CBP) using cellulose and hemicellulose directly to minimize pretreatment.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS,SYNTHETIC ↗
DOE OSTI2021

Atomic Layer Deposition with TiO2 for Enhanced Reactivity and Stability of Aromatic Hydrogenation Catalysts

Hydrogenation of aromatic molecules in fossil- and bio-derived fuels is essential for decreasing emissions of harmful combustion products and addressing growing concerns around urban air pollution. In this work, we used atomic layer deposition to significantly enhance the hydrogenation performance of a conventional supported Pd catalyst by applying an ultrathin coating of TiO2 in a scalable powder coating process. The TiO2-coated catalyst showed substantial gains in the conversion of multiple aromatic molecules, including a 5-fold improvement in turnover frequency versus the uncoated catalyst in the hydrogenation of naphthalene. This activity enhancement was maintained upon scaling the coating synthesis process from 3 to 100 g. Based on the results from Xray photoelectron spectroscopy, X-ray absorption spectroscopy, and computational modeling, the activity enhancement was attributed to ensemble effects resulting from partial TiO2 coverage of the Pd surface rather than fundamental changes to the Pd electronic structure. Additional durability testing confirmed that the TiO2 coating improved the thermal and hydrothermal stability of the catalyst as well as tolerance toward sulfur impurities in the reactant stream. Using an economic model of an industrial deep hydrogenation process, we found that an increase in catalyst activity or lifetime of 2× would justify even a relatively high estimate for the cost of TiO2 atomic layer deposition coatings at scale

atomic layer↗
DOE OSTI2022

Scalable High-H 2 Flux, Robust Thin Film Solid Oxide Electrolyzer

This project was aimed at the development of proton-conducting SOEC (P-SOEC) technology that has the potential to meet key DOE H 2 production targets. A decreased proton resistance of the electrolyte and Faradaic efficiency improvements were sought to increase the fraction of consumed electrolysis power that is used to actually generate H 2 , while simultaneously decreasing cost dramatically. Moreover, the development project was intended to yield maximum durability through the use of a steam protective layer. Furthermore, sputtering was used to overcome processing challenges that have hampered P-SOEC development, while the low-temperature operation goal of 500 °C was expected to aid in mitigating thermally activated long-term degradation. The approach to high-performance, lower-temperature SOECs leveraged our existing SOFC Ni-cermet anode support and extensive thin-film sputtering layer-deposition experience. Rather than an all-in-one, reversible fuel cell approach which has many unacceptable tradeoffs, we focused on the many benefits to hydrogen generating SOECs, including the existence of synergies for reduced manufacturing costs (e.g., SOECs and SOFCs share supporting layers and overall manufacturing processing). The end result of this project was expected to increase current performance at 500 °C from approximately 0.8 A/cm 2 (at 60% Faradaic efficiency) at 1.4 V to > 1 A/cm 2 (at > 95% Faradaic efficiency) with a > 40% reduction in system cost and to enable operation of P-SOECs in steam contents >> 20% for a goal of a > 40,000 hours lifetime. To enable 500 °C operation in a very high steam atmosphere (> 20%), we proposed the use of a sputtered dense thin film (~0.1-1 µm thick) of high-stability Ba(Zr,Y)O 3 (BZY) to protect the Ba(Ce,Zr,Y,Yb)O 3 (BCZYYb) electrolyte. The BZCYYb, in turn, blocks the hole conductivity of the BZY to boost Faradaic efficiency. As FE increases, more of the consumed electricity is used in electrolysis to generate H 2 , rather than being shunted. Additionally, as cell resistance decreases, the voltage required to maintain current decreases, as well as the power required to generate the same amount of H 2 . With the proposed enhancements, these two factors result in the final 46% decrease in power needed to run the system. Likewise, a production rate of 50,000 kg H 2 /day will require 55% less active area, such that a system will need only 650 cells for an 80 cm 2 active area instead of ~1,440. Taking the 2016 DOE projected current cost and modifying the electricity cost and linearly scaling the other costs (except thermal feedstock) based on the cell area improvement, results in a 44% decrease in lifetime system cost, or a decrease from $\$$4.95/kg H 2 to $\$$2.75/kg H 2 . This is well below the 2018 DOE target of $\$$4/kg H 2 . The results from this project showed that we can create a P-SOEC with enhanced steam stability using two different electrolytes (i.e., one on top of the other) and achieve sufficiently low area specific resistance (ASR) to achieve the target performance. Unfortunately, due to extended delays at the beginning of the project and related supply chain and equipment access issues, we were not able to completely show increased Faradaic efficiency for the P-SOEC and therefore were unable to demonstrate the full proof of concept within the first budget period budget. While there are still challenges that remain to be solved, significant progress was made during this project and the concept still has merit that warrants further development.

08 HYDROGEN↗
DOE OSTI2020

Probing solid-state reaction through microstrain: A case study on synthesis of LiCoO 2

Solid-state reaction has been widely adopted as a low-cost and scalable approach to synthesize inorganic materials for industrial applications. However, a special caution must be paid to carefully control the synthesis condition in order to obtain final products with desired structure and physical/chemical properties. In this work, LiCoO 2 was investigated as a model material to illustrate the complexity of the solid-state reaction, as well as its condition control. Taking the advantage of the high flux and high penetration capability of synchrotron X-ray source, in-situ high-energy X-ray diffraction was deployed to investigate the structural evolution of materials during the solid-state reaction while ex-situ high-resolution X-ray diffraction was utilized to quantify the residual microstrains of LiCoO 2 . It is shown that the microstrain is a sensitive indicator to probe the completeness of the solid-state reactions, and that it also provides a more quantitative way to establish the structure-property relationship of materials. It can serve as a sensitive indicator for the rational design of synthesis process for functional materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Immersive Industrialized Construction Environments for Energy Efficiency Construction Workforce

The National Renewable Energy Laboratory is actively developing and testing Immersive Industrialized Construction Environments (IICE) for construction automation and worker-machine interaction to investigate possible solutions and increase workforce productivity. At full scope and matured functionality, IICE allows us to accelerate the development of and better explore industrialized construction approaches such as prefabrication. IICE also enables wider adoption of energy-efficient products and Industry 4.0 construction automation through worker-machine interaction pilots. Industry 4.0 and industrialized construction approaches can encourage workforce specialization in energy efficiency construction, address the lack of multi-skilled workers, and increase workforce productivity with construction automation. However, recent attempts to integrate these concepts with the industry have only been moderately successful. To address this, focusing the pedagogy on using a digital twin, its digital models, and virtual reality could make the experience of continuing education on construction automation more affordable, accessible, scalable, immersive, and safer, and could greatly improve the efficiency and robustness of the building and construction industry. IICE accurately represents the realities of construction uncertainties without having to create full scale physical prototypes of machines. In this paper, we address the following research question: How can a digital twin and its models in virtual reality enhance the learning experience and productivity of energy efficiency construction workers to gain the skills in operating Industry 4.0 components such as construction automation and handling energy-efficient products in industrialized construction factories and on-site? We introduce original research on developing IICE and present preliminary findings from time and motion pilot studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Separation of High Purity Oxygenated Aromatics from Catalytic Fast Pyrolysis Organic Streams

Thermochemical conversion of lignocellulosic biomass offers an attractive route to produce fuels and chemicals. Specifically, catalytic fast pyrolysis (CFP) oils have been shown to contain a high concentration of oxygenated aromatics, which can be isolated as value-added co-products. Purification of oxygenated aromatics can be a viable pathway to more economically competitive biorefineries, yet the isolation of highly pure coproduct streams remains technically challenging due to the high chemical heterogeneity of thermochemical process steams. Here, we demonstrate an industrially scalable process for isolating phenol, a widely used precursor to plastics, from CFP oil. Importantly, we leverage standard industrial processes, such as distillation, liquid-liquid extraction, and crystallization, to achieve phenol with 97% purity. In addition, the primary distillation of the CFP oil yielded several other fractions that contain chemicals with the potential to be isolated (e.g. cyclic ketones, cresols, and guaiacols). This work provides a technically feasible path to high purity oxygenated aromatics that has the potential to offset fuel production costs in a biorefinery.

09 BIOMASS FUELS↗
DOE OSTI2026

Toward Sustainable Lithium Recovery: A Universal Hydrothermal Approach for Lithium Extraction

The rapid growth of lithium-ion battery (LIB) deployment presents critical challenges in sustainable end-of-life management and raw material recovery. Conventional pyrometallurgical and hydrometallurgical methods suffer from high energy demand, lithium loss, and complex wastewater treatment. This study established a universal, highly efficient, and sustainable hydrothermal route for lithium extraction and material recovery from various spent lithium-ion battery cathodes using 1,2,4,5-benzenetetracarboxylic acid (BTCA). The optimized process achieved over 99% lithium leaching efficiency for lithium iron phosphate (LFP) and LiNi x Mn y Co 1–x–y O 2 (NMC), with transition metal coleaching below 1%. It was also broadly applicable to lithium manganese oxide, lithium cobalt oxide, and black mass, achieving 98.5%, 98.95%, and 94.06% leaching efficiencies, respectively. The extracted lithium was directly converted into battery-grade lithium sources, while transition metals were recovered as oxides. Unreacted BTCA was efficiently regenerated and reused without degradation. Electrochemical evaluation confirmed that cathode materials synthesized with recovered lithium exhibit comparable performance to commercial products. Compared to conventional hydrometallurgy, the BTCA-based process increased revenue by over 40% and reduced greenhouse gas emissions by up to 39%. This closed-loop, chemistry-agnostic strategy offered a scalable and economically viable solution for industrial LIB recycling, enabling resource circularity and reducing dependency on primary critical materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Co-Firing Switchgrass and Waste Coal in A Power Plant: A Techno-Economic and Life Cycle Evaluation for The Ohio River Valley (SWITCH) (Final Technical Report for Ohio State/FE0032204)

Abandoned coal mine lands (AMLs) represent one of the most persistent environmental challenges in the United States. Prior to the enactment of the Surface Mining Control and Reclamation Act (SMCRA) in 1977, coal mining operations were not legally required to reclaim disturbed lands, leaving behind approximately 500,000 AML sites nationwide. These sites pose severe environmental and health risks, including acid mine drainage, soil and water contamination, and spontaneous combustion of waste coal piles. Millions of Americans live within one mile of these AMLs, underscoring the urgency of remediation. Traditional reclamation practices, such as planting cool-season grasses, often fail to fully restore ecological function or leverage the economic potential of these lands. This project addressed these challenges by developing integrated strategies for resource recovery, land reclamation, and sustainable energy production. This project evaluated an integrated strategy to convert this liability into an opportunity by recovering waste coal and co-firing it with switchgrass (Panicum virgatum L.) cultivated on reclaimed or marginal AML areas in existing coal-fired power plants. Switchgrass not only provides a renewable feedstock but also aids in land reclamation and carbon sequestration. 1) Remote Sensing and Machine Learning for Waste Coal Identification Using Sentinel-2 satellite imagery and supervised classification, we applied four machine learning models to detect historical waste coal piles. Random Forest achieved the highest accuracy (precision: 86%, recall: 77%). Time-series analysis revealed gradual vegetation recovery since 1986, indicating natural reclamation processes in historical sites, while active mining areas showed ongoing disturbance. This workflow enables scalable monitoring and prioritization of reclamation efforts. 2) UAS-Based Stockpile Volume Estimation To quantify recoverable waste coal, we evaluated Unmanned Aerial Systems (UAS) equipped with Light Detection and Ranging (LiDAR) and multispectral sensors. Structure-from-Motion (SfM) photogrammetry combined with interpolated Digital Terrain Models (DTMs) achieved strong agreement with LiDAR reference volumes (Root Mean Square Error (RMSE) ≈147 m 3 , Mean Absolute Percentage Error (MAPE) ≈2%). Sensitivity analysis confirmed that spatial resolution significantly influences accuracy, emphasizing the need for high-resolution data for precise volume estimation. This approach offers a scalable, cost-effective, and accurate alternative to conventional ground-based surveys. 3) Switchgrass Cultivation for Bioenergy and Water Quality Improvement We assessed the hydrological and water quality impacts of converting AMLs to switchgrass production areas using the Soil and Water Assessment Tool (SWAT). Results showed that converting 10% of the watershed area into the switchgrass production zone reduced streamflow by 3.1%, total suspended solids by 18.1%, total nitrogen by 7.6%, and total phosphorus by 6.2%, while achieving biomass yields of 8.6–9.2 metric tons per hectare. These findings highlight switchgrass as a dual-benefit strategy for land reclamation and bioenergy feedstock production. 4) Integrated Co-Firing and CCS for Carbon-Negative Power Generation We modeled co-firing scenarios using the Power Plant Flexible Model (PPFM) to evaluate plant efficiency, greenhouse gas (GHG) emissions, and levelized cost of electricity (LCOE). Without carbon capture and storage (CCS), increasing switchgrass co-firing ratios reduced LCOE from $\$$150/MWh at 0% biomass to $\$$110/MWh at full substitution. Under CCS, costs remained higher (~$\$$250/MWh at 0% biomass) but decreased to $\$$200/MWh at 100% biomass, while enabling net-zero or carbon-negative electricity due to switchgrass sequestration benefits. Although CCS introduced efficiency penalties, pairing it with biomass co-firing offset these impacts and maximized climate benefits. Overall, optimizing co-firing ratios between 60-100%, supported by reliable logistics and storage strategies, emerged as a practical pathway to balance affordability, sustainability, and net-zero or negative GHG emissions while promoting productive reuse of AMLs.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Tandem particle-slurry batch reactors for solar water splitting (Final Scientific/Technical Report)

Economically, particle slurry reactors are projected to be one of the most promising technologies for solar photoelectrochemical hydrogen production, according to a 2009 techno-economic analysis commissioned by the US DOE and performed by Directed Technologies, Inc. The Fuel Cell Technologies Office’s Multi-Year Research, Development and Demonstration (MYRD&D) goals and targets are to reduce the cost of H 2 produced from renewable sources at the plant gate (i.e. not including delivery, dispensing, or storage) to < $2.00/gge, equivalent to ~$2.00/kg H 2 . Research results from our techno-economic modeling research suggest that this target could be met using particle slurry reactors assuming STH efficiencies in the range of 5 – 10%, materials lifetimes of < 1 year, and nanoparticles that cost up to 20 times more than projected costs of TiO 2 -coated Fe 2 O 3 nanoparticles. Although most large worldwide research efforts directed at solar photoelectrochemical hydrogen production focus on wafer-based designs, the projected lower cost for a particle slurry reactor at these disparate projected STH efficiencies clearly suggests that particle slurry reactors could be a scalable and deployable technology, assuming several challenges are overcome. These major technological challenges include the demonstration of a vertically-stacked-vessel architecture that is capable of operating sustainably while mostly relying on diffusion and natural convection to mix the redox shuttles between the vessels, and the demonstration that photocatalyst particles can operate at an overall 1% STH efficiency or larger when incorporated into this two-vessel design. Our research adds to the understanding of photocatalytic reactors for solar water splitting through numerical modeling results and experimental results. Numerical models were developed to simulate relevant device physics including particle and reactor dimensions which affect optical, transport, and rheological properties, electrocatalytic and photovoltaic properties of particles at various temperatures, and properties of redox shuttles and separators. Moreover, theoretical maximum solar-to-hydrogen efficiencies for ensembles of particles like in photocatalyst reactors were modeled and simulated and shown to equal or exceed those of photoelectrochemical designs under most scenarios. These results help determine constraints on the reactor that will enable more optimal designs for future prototypes. In parallel, experiments were performed to identify the most effective redox shuttles and to empirically validate the numerical models and simulations. Toward the latter, state-of-the-art light-absorbing particles and electrocatalysts were synthesized and characterized physically and photoelectrochemically for water electrolysis and redox chemistry with redox shuttles in the form factor of mesoporous electrodes and free-floating particles. The most promising materials candidates were used in a suspension reactor to evaluate performance toward photocatalytic H 2 production and results from the two measurements were compared. Predominantly, state-of-the-art cocatalyst-modified Rh-doped SrTiO 3 and BiVO 4 particles were further characterized to assess for their ability to perform visible-light-driven H 2 and O 2 evolution, respectively, and results were similar to those reported for the state-of-the-art in the peer-reviewed literature. Outcomes from this work inform the public of the effectiveness and promise of solar photocatalytic water splitting for clean and renewable hydrogen production. This work may also help increase research interest and funding for photocatalysis projects, which will accelerate development of a technology that will benefit the public by generating fuel while emitting few greenhouse gases and pollutants.

08 HYDROGEN↗
DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Upcycling Mixed Spent Ni-Lean Cathodes into Ni-Rich Polycrystalline Cathodes

Sustainable battery recycling is vital for conserving resources and reducing environmental impacts. Current open- and closed-loop recycling strategies often focus on recovering individual components, making the reuse of mixed cathode materials a complex challenge. Meanwhile, the research on upcycling has been limited to using pristine cathode feedstocks and virgin materials for synthesis. Here, to address this issue, we present an upcycling approach for spent Ni-lean mixed cathode materials that integrate an upcycling hydrometallurgical recycling process with traditional hydrometallurgical methods. This strategy achieves a utilization of 92.31 mol % of recycled materials, enabling the regeneration of Ni-rich cathode materials while significantly reducing the reliance on virgin resources. The regenerated 83Ni cathode materials demonstrate physical properties comparable to those produced from virgin materials. Electrochemical evaluations using single-layer pouch cells show that both recycled and virgin cathodes exhibit initial specific capacity close to 201.1 mAh/g and maintain approximately 88 % capacity retention after 500 cycles. Additionally, 2Ah cells confirmed these findings, delivering 85 % capacity retention after about 900 cycles. Techno-economic analysis demonstrates notable environmental benefits, including reductions in greenhouse gas emissions and energy consumption, achieving 232.75 MJ/kg of product, which is 8.6 % lower than traditional methods and comparable to direct upcycling. Furthermore, the upcycling hydrometallurgical recycling process generates the highest profit, proving its economic viability. This scalable and versatile process is adaptable to varying transition metal compositions, facilitating a closed-loop recycling system that bridges mixed spent cathodes with next-generation cathode materials, and offers a sustainable solution for managing waste battery materials.

Hydrometallurgical recycling↗
DOE OSTI2024

ChemPren: a new and economical technology for conversion of waste plastics to light olefins

With the ever-increasing demand for plastics, sustainable recycling methods are key necessities. Here, the current plastics industry can manage to recycle only 10% of the 400 million metric tons of plastic produced globally. Waste plastics, in the current infrastructure, land up mostly in landfills. Although a lot of research efforts have been spent on processing and recycling co-mingled mixed plastics, energy-efficient sustainable and scalable routes for plastic upcycling are still lacking. Catalytic valorization of waste plastic feedstock is one of the potential scalable routes for plastic upcycling. Silica-alumina based materials, and zeolites have shown a lot of promise. A major interest lies in restricting catalyst deactivation, and refining product selectivity and yield for such catalytic processes. This article highlights ChemPren technology as a clean energy solution to waste plastic recycling. Co-mingled, mixed plastic feedstock along with spray dried, attrition resistant, ZSM-5 containing catalysts is preprocessed with an extruder to form optimally sized particles and fed into a fluidized bed reactor for short contact times to produce selectively and in high yields ethylenes, propylenes and butylenes. This techno-economic perspective indicates that the ChemPren technology can produce propylene at $\$$0.16 per lb, whereas the current selling price of virgin propylene is $0.54 per lb. This technology can serve as a platform for mixed plastic upcycling, with more advancements necessary in the form of robust and resilient catalysts and reactor operation strategies for tuning product selectivity.

25 - ENERGY STORAGE↗