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At least 1,045 records · Page 58

Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers

Properly determining the driving range is critical for accurately predicting the sales and social benefits of battery electric vehicles (BEVs). This study proposes a framework for optimizing the driving range by minimizing the sum of battery price, electricity cost, and range limitation cost referred to as the "range-related cost" as a measurement of range anxiety. The objective function is linked to policy-relevant parameters, including battery cost and price markup, battery utilization, charging infrastructure availability, vehicle efficiency, electricity and gasoline prices, household vehicle ownership, daily driving patterns, discount rate, and perceived vehicle lifetime. Qualitative discussion of the framework and its empirical application to a sample (N=36664) representing new car drivers in the United States is included. The quantitative results strongly suggest that ranges of less than 100 miles are likely to be more popular in the BEV market for a long period of time. The average optimal range among U.S. drivers is found to be largely inelastic. Still, battery cost reduction significantly drives BEV demand toward longer ranges, whereas improvement in the charging infrastructure is found to significantly drive BEV demand toward shorter ranges. In conclusion, the bias of a single-range assumption and the effects of range optimization and diversification in reducing such biases are both found to be significant.

33 ADVANCED PROPULSION SYSTEMS↗

Gasoline Engine and Fuels Offering Reduced Fuel Consumption and Emissions: Vehicle Modeling Final Report

The Gasoline Engine and Fuels Offering Reduced Fuel Consumption and Emissions (GEFORCE) project was proposed in response to the U.S. Department of Energy’s Funding Opportunity Announcement 0991 by a team made up of the members of the Coordinating Research Council (CRC) and the research staff at Oak Ridge National Laboratory (ORNL.) The project focused on investigating the potential benefits that might be attained through synergistic use of specific engine technology together with fuels formulated to represent potential directions that high-octane fuels of the future might progress. A stated objective in the DOE FOA was to demonstrate a 25% reduction in petroleum consumption through optimization of the engine technologies together with a suitable fuel. An advanced engine was constructed and used with a matrix of research fuels to investigate potential avenues for efficiency improvement. The engine incorporated technologies expected to become mainstream for boosted engines in the next 10 to 20 years. These included increased compression ratio, a two-stage turbocharger, and cooled external exhaust gas recirculation (EGR). The fuel matrix was designed to investigate impacts from research octane number (RON), volumetric ethanol content, and the final boiling point of the fuel. The engine calibration was optimized for each fuel individually and data collected to enable vehicle system modelling that projected energy consumption, fuel economy, tailpipe CO₂ emissions, and impact on petroleum consumption for an industry-average mid-size sedan. The engine calibration and data collection were carried out at IAV in Michigan and is the subject of a separate report. IAV provided the engine data to Oak Ridge National Laboratory to support the vehicle modelling portion of the project. The vehicle modelling results show the following trends: Ethanol content does have a consistently strong influence on the fuel economy results for all cycles and all fuels. Among the fuels of a nominal RON level, increasing ethanol content consistently lowers fuel economy, with the 30% ethanol fuels always providing the lowest fuel economy for a given RON level. However, in some cases the energy consumption improvement allows the 30% ethanol fuels to match the fuel economy of the ethanol-free fuel P. These observations underscore the importance of both engine efficiency and fuel volumetric energy content on vehicle fuel economy.; There was no consistent trend in the projected energy consumption results for differences in fuel T90 for all fuels and cycles. Fuel economy projections did show a consistent trend, with the higher T90 fuel providing slightly greater fuel economy when compared to the low T90 fuel of the same ethanol content. The observed trends were consistent with differences in the heating value of the fuels.; The 102-RON fuels provided reduced energy consumption and greater fuel economy for the advanced engine on all drive cycles. The engine compression ratio of 11.5 was higher than would typically be used in a turbocharged engine when 92-RON fuel use is expected. Hence, the engine experiences more efficiency degradation from knock avoidance when using the 92-RON fuels. This degradation causes the fuel economy results for the 92-RON fuels to be lower than those for the 102-RON fuels.; Fuels E and F (92-RON, 30% ethanol) are projected to achieve 10% or greater reduction in petroleum consumption, with fuels K (102-RON, 30% ethanol) and O (97 RON, 30% ethanol) achieving greater than a 20% reduction. Fuel L (102-RON, 30% ethanol) achieves greater than 25% reduction, meeting the petroleum reduction target of the project. All of the fuels that achieve 10% or greater reduction in petroleum consumption are 30% ethanol blends.; Increasing final boiling point increased fuel economy at fixed ethanol content when the 102-RON fuels were used. This trend is a result of differences in the volumetric energy content of the fuels and the projected energy consumption values for the fuels. In the case of the 102-RON fuels, increasing final boiling point also resulted in an increase in the energy content of the fuel. There was not a consistent trend between the energy content and final boiling point for the 92-RON fuels at fixed ethanol content.

33 ADVANCED PROPULSION SYSTEMS↗

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development and deployment of advanced materials and components fabricated via additive manufacturing with a specific focus on laser powder bed fusion (LPBF). As an initial case study, the program has selected 316H stainless steel (SS) as an initial material around which to develop a code case development strategy. This strategy involves two parallel approaches: (1) an equivalency approach whereby round-robin testing across multiple collaborating laboratories demonstrates repeatability in processing and direct comparisons with conventional wrought 316H material and (2) a revolutionary approach to code qualification combining in situ data collection and high-fidelity modeling to capture, predict, and bound the performance of LPBF 316HSS components. As part of this campaign, this work package has initiated an extensive process optimization campaign across three laboratories, each printing variations of LPBF 316HSS using three different LPBF units (Concept Laser, EOS, and Renishaw). In FY23, ORNL has focused on unique experimental designs spanning wide ranges in energy inputs and turning knobs such as scan speed, laser power, hatch spacing, layer thickness, spot size, scan rotation, and more. On the Concept Laser M2, 72 different combinations of processing variables were investigated with duplicate samples and different powder compositions. In total, 252 samples were printed with combined in situ sensing data. A parallel design of experiments was conducted on the Renishaw AM400 with an additional 390 printed specimens for analysis. All 642 miniature specimens, each with unique features included in each print to capture geometry-related heterogeneity, were subjected to high-throughput x-ray computed tomography (XCT) analysis to enable the downselection of specific processing parameters of interest. Then, using electrical discharge machining (EDM), miniature tensile specimens were extracted for mechanical testing and microscopy investigations. From the analysis performed in FY23, it was found that powder composition drastically affects the resulting microstructure and mechanical performance of 316SS. Specifically, changing from 316L to 316HSS powder results in a wide range of grain sizes with varying degrees of preferred grain orientation, which increases as a function of energy density. It was also found that due to stored heat in thin fin–type features, large microstructural differences can be seen within one part printed with one set of processing parameters. These variations in microstructure features, including grain size, the nanoscale dislocation structure, and grain texture, will all affect the irradiation performance and high-temperature mechanical performance of LPBF 316HSS parts. Two sets of concept laser processing parameters, spanning both refined and columnar grain structures, were scaled to print larger 316H builds for campaign testing (high-temperature creep and irradiation). In addition, at least two optimized processing parameter sets were identified for the Renishaw AM400 for round-robin testing in FY24 with Argonne National Laboratory. Future work includes printing samples using identical parameters identified by partner institutions, providing material for corrosion and high-temperature mechanical testing, and continuing evaluations of heterogeneity in larger printed parts.

36 MATERIALS SCIENCE↗

FY20 ASC IC L2 Milestone 7180: Performance Portability of SIERRA Mechanics Applications to ATS-1 and ATS-2. Executive Summary

The overall goal of this work was to accelerate simulations supporting the nuclear deterrence (ND) mission through improved performance of key algorithms in the ASC IC Sierra multi-physics application suite. This work focused on porting and optimizing algorithms for the graphics processing units (GPU) on the second ASC advanced technology system (ATS-2), while maintaining or improving performance on commodity technology systems (CTS) and ATS-1. Furthermore, these algorithmic developments used the ASC developed Kokkos performance portability abstraction library to maintain high performance across platforms using identical code, and enable sustainable reduced-cost migration and performance optimization to emerging hardware.

97 MATHEMATICS AND COMPUTING↗

Transient Efficiency, Flexibility, and Reliability Optimization of Coal-Fired Power Plants - Final Program Review

This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of sub-critical coal fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗

A High-Performance Sintered Iron Electrode for Rechargeable Alkaline Batteries to Enable Large-Scale Energy Storage

Iron-based alkaline rechargeable batteries such as iron-air and nickel-iron batteries are particularly attractive for large-scale energy storage because these batteries can be relatively inexpensive, environment- friendly, and also safe. Therefore, our study has focused on achieving the essential electrical performance and cycling properties needed for the widespread use of iron-based alkaline batteries in stationary and distributed energy storage applications.We have demonstrated for the first time, an advanced sintered iron electrode capable of 3500 cycles of repeated charge and discharge at the 1-hour rate and 100% depth of discharge in each cycle, and an average Coulombic efficiency of over 97%. Such a robust and efficient rechargeable iron electrode is also capable of continuous discharge at rates as high as 3C with no noticeable loss in utilization. We have shown that the porosity, pore size and thickness of the sintered electrode can be selected rationally to optimize specific capacity, rate capability and robustness. As a result, these advances in the electrical performance and durability of the iron electrode enables iron-based alkaline batteries to be a viable technology solution for meeting the dire need for large-scale electrical energy storage.

25 ENERGY STORAGE↗

Fabrication of Nanoporous Alumina Ultrafiltration Membrane with Tunable Pore Size Using Block Copolymer Templates

Control over nanopore size and 3D structure is necessary to advance membrane performance in ubiquitous separation devices. Here, inorganic nanoporous membranes are fabricated by combining the assembly of cylinder-forming poly(styrene-block-methyl methacrylate) (PS-b-PMMA) block copolymer and sequential infiltration synthesis (SIS). A key advance relates to the use of PMMA majority block copolymer films and the optimization of thermal annealing temperature and substrate chemistry to achieve throughfilm vertical PS cylinders. The resulting morphology allows for direct fabrication of nanoporous AlOx by selective growth of Al2O3 in the PMMA matrix during the SIS process, followed by polymer removal using oxygen plasma. Control over the pore diameter is achieved by varying the number of Al2O3 growth cycles, leading to pore size reduction from 21 to 16 nm. 3D characterization, using scanning transmission electron microscopy tomography, reveals that the AlOx channels are continuous through the film and have a gradual increase in pore size with depth. Finally, the ultrafiltration performance of the fabricated AlOx membrane for protein separation as a function of protein size and charge is demonstrated.

block copolymers↗

Fuel Effects on Advanced Compression Ignition Load Limits

In order to increase the efficiency of light-duty gasoline engines, the Co-Optimization of Fuels and Engines (Co-Optima) initiative from the U.S. Department of Energy is investigating multi-mode combustion strategies. Multi-mode combustion can be describe as using conventional spark-ignited combustion at high loads, and at the part-load operating conditions, various advanced compression ignition (ACI) strategies are being investigated to increase efficiency. Of particular interest to the Co-Optima initiative is the extent to which optimal fuel properties and compositions can enable higher efficiency ACI combustion over larger portions of the operating map. Extending the speed-load range of these ACI modes can enable greater part-load efficiency improvements for multi-mode combustion strategies. In this manuscript, we investigate fuel effects for six different fuels, including four with a research octane number (RON) of 98 and differing fuel chemistries, iso-octane, and a market representative E10 fuel, on the load limits for two different ACI strategies: spark-assisted compression ignition (SACI) and partial fuel stratification-gasoline compression ignition (PFS-GCI) operation. Experimental results show that limits to intake boosting limit high load operation for most fuels, but high smoke emissions for high particulate matter index (PMI) fuels under SACI conditions could also be a limitation. Contrastingly, low load is limited by combustion efficiency, but these effects have more pronounced variation with fuel chemistry for PFS-GCI than with SACI. Additional, distinct effects affecting autoignition timing and peak heat release at higher speeds were identified for fuels having different low temperature heat release (LTHR) propensities for both ACI modes.

33 ADVANCED PROPULSION SYSTEMS↗

Deployment of Dynamic Neural Network Optimization to Minimize Heat Rate During Ramping for Coal Power Plants (Final Technical Report)

Much success was achieved throughout the course of this project. A successful implementation of Dynamic Neural Network Optimization (D-NNO) was coupled with Adaptive Predictive Controls (APC) and a novel hardware installation comprised of an advanced sensor network (ASN) measuring mass-weighted averages of flue gas constituents above the horizontal superheater of a coal-fired utility boiler. From 2019 through 2023 (including an extension due to COVID delays), the team was able to prototype, evaluate, deploy, iterate, and ultimately finalize an advanced closed-loop control D-NNO system which demonstrated the ability to: •improve unit efficiency ~2.0% relative to unoptimized operation (represented as total fuel fired per MWh generated) •improve unit NOx emission rates 10%+ beyond static optimization baselines •improve unit temperature stability as much as 58% and on average 12% •improve operating load stability as much as 35% The culmination of this project has generated an advanced methodology of deploying specially designed recurrent neural networks (long short-term memory, gated recurrent unit, encoder-decoder networks, transformers, etc.), customized trajectory planning and closed-loop optimization modules capable of adapting to live electric grid responses and demands, self-tuning and adaptive expert controls constantly adjusting prediction parameters to real-time unit behavior, and a hardware/software package able to reliably calculate net unit heat rate (NUHR) in real-time using flue gas constituents, machine learning, and known combustion relationships. Through this real-time NUHR value, immediate feedback on system adjustments relative to operating efficiency was available, allowing for rapid improvements to system performance. In addition to development and deployment of the advanced D-NNO system, the approach methodology has been readily commercialized through the project platform Griffin Open Systems, LLC, the D-NNO software platform host. Similar methodologies to those developed by this project have already been deployed at 5 other units across the United States, with another 6 implementations scheduled, and more expected. Over the course of the project, multiple academic papers were submitted and accepted for publication within esteemed academic journals, and PhD students were trained and graduated, as well as undergraduate students becoming involved and participating to project objectives.

01 COAL, LIGNITE, AND PEAT↗

Coal to Carbon Fiber (C2CF) Continuous Processing for High Value Composites (Final Report)

Coal tar is a condensed and recovered by-product of the coking of metallurgical coal for steel production. The heaviest fraction of distilled coal tar is an isotropic pitch largely used as a binder in the manufacturing of carbonaceous electrodes for primary aluminum smelting and in electric arc furnaces. Coal tar pitch offers high carbon yield upon carbonization. In this project, a process to convert the domestically sourced isotropic coal tar pitch, containing very low particulates (QI = 0.32 wt.%), to form flow-domain mesophase pitch amenable for melt spinning into precursor (green) fibers for carbon fiber, was developed and optimized. The final reproducible processing developed is reviewed in this report, along with several characterizations of the mesophase pitch. A final definition of the characteristics of a ‘spinnable’ mesophase pitch is presented. With this mesophase pitch, reproducible and stable multifilament melt spinning was developed, producing green fiber tows with filament diameters of approximately 20 m. The multifilament melt spinning was the most challenging aspect of the project and required the most effort. The best practices learned from this project for melt spinning are reviewed in this report. Once spun, the green fibers were oxidatively stabilized, carbonized and graphitized under inert gas atmosphere to form the final carbon fibers. Given the relatively high softening point of the mesophase pitch, no issues of interfilament fusion were observed during batch oxidation, and subsequent batch carbonization and graphitization went smoothly in all cases. An ~ 80 wt.% conversion of the green fiber mass to final carbon fiber was achieved. After graphitization, the carbon fibers showed high tensile moduli (most were ~ 600 GPa, or 87 Msi) consistent with commercially-available high-performance pitch-based carbon fiber. However, tensile strength and strain to failure were comparatively low. Further work to reduce defects in and on the fiber surfaces would increase these properties. SEM imaging of the graphitic fiber textures is presented herein. Rudimentary composites were fabricated from the carbon fibers and characterized showing similar modulus to baseline composites fabricated with commercial carbon fiber. Finally, a basic economic analysis was done showing the potential to increase the value of the isotropic coal tar pitch by up to 13.6 to 136 times based on a carbon fiber value of $\$$5/lb to $\$$50/lb, respectively. Moreover, the site case study suggests that the coal tar from the single integrated steel mill could supply production of up to 16 kt/yr of carbon fiber. Finally, a technological gap analysis was done which shed light on remaining technical challenges. These challenges included: recovery and utilization of condensates from the mesophase pitch processing, further advancing and increasing stability of the multifilament melt spinning processing, optimization of the oxidation processing, defect reduction for increased carbon fiber strength, and the development of a weaving process towards carbon fiber fabrics. To maximize the coal value chain, the primary objectives of this project were to (a) develop and scale efficient processing technology for producing melt-spinnable mesophase pitch from isotropic coal tar pitch, (b) clarify and simplify tedious continuous fiber processing technologies (particularly multifilament melt spinning of mesophase pitch) towards the efficient production of high performance carbon fiber, and (c) demonstrate and characterize representative composite parts derived from the final carbon fiber. Immense progress was made on all 3 objectives and is detailed in this report.

01 COAL, LIGNITE, AND PEAT↗

Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics: A DOE-NIH Joint Workshop Executive Summary

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. Here, the workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

digital twins↗

Recent advances in MoS{sub 2} nanostructured materials for energy and environmental applications – A review

Molybdenum disulfide (MoS{sub 2}), a layered transition metal dichalcogenide with an analogous structure to graphene, has attracted enormous attention worldwide owing to its use in a variety of applications such as energy storage, energy conversion, environmental remediation and sensors. MoS{sub 2} and graphene have almost similar functional properties such as high charge carrier transport, high wear resistance and good mechanical strength and friction. However, MoS{sub 2} is advantageous over graphene due to its low-cost, abundancy, tailorable morphologies and tuneable band gap with good visible light absorption properties. In this review, we have focussed mainly on recent advances in MoS{sub 2} nanostructured materials for the applications in the broad area of energy and environment. Special attention has been paid to their applications in dye-sensitized solar cells, supercapacitor, Li-ion battery, hydrogen evolution reaction, photocatalysis for the degradation of organic pollutants, chemical/bio sensors and gas sensors. Finally, the challenges to design MoS{sub 2} nanostructures suitable for energy and environmental applications are also highlighted. - Highlights: • Applications of MoS{sub 2} nanostructures towards energy, environmental remediation and sensors were summarized. • Recent advances in the development of MoS{sub 2} nanostructured materials were systematically investigated. • Wide range of energy applications including DSSCs, supercapacitors, Li-ion batteries and HER were reviewed. • Key challenges and optimization of structure for superior performance associated with MoS{sub 2} nanostructures were discussed. • Advances of environmental remediation based on MoS{sub 2} nanostructures were significantly highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Understanding intercalation chemistry for sustainable aqueous zinc–manganese dioxide batteries

Rechargeable aqueous Zn-MnO 2 technology combines one of the oldest battery chemistries with favourable sustainability characteristics, including safety, cost and environmental compatibility. However, the ambiguous charge storage mechanism presents a challenge to fulfil the great potential of this energy technology. In this work, we leverage on advanced electron microscopy, electrochemical analysis and theoretical calculations to look into the intercalation chemistry within the cathode material, or α-MnO 2 more specifically. We show that Zn 2+ insertion into the cathode is unlikely in the aqueous system; rather, the charge storage process is dominated by proton intercalation to form α-H x MnO 2 . We further reveal anisotropic lattice change as a result of entering protons proceeding from the surface into the bulk of α-MnO 2 , which accounts for the structural failure and capacity decay of the electrode upon cycling. Our work not only advances the fundamental understanding of rechargeable zinc batteries but also suggests the possibility to optimize proton intercalation kinetics for better-performing cell designs. A rechargeable aqueous Zn-MnO 2 battery features a combination of favourable sustainability characteristics from safety to cost. The authors deploy advanced characterizations and theoretical calculations to provide fresh insight into the charge storage mechanism, which not only closes an ongoing debate but suggests ways forward.

25 ENERGY STORAGE↗

Optimizing Energy Use in Pulp & Paper with DOE’s Energy Intensive Industries Resources

The U.S. pulp and paper industry is the third-largest energy consumer in manufacturing, accounting for roughly 10% of sector energy use. Improving energy efficiency reduces operating costs and strengthens competitiveness. To support this effort, the U.S. Department of Energy (DOE), through Oak Ridge National Laboratory (ORNL), launched the Energy Intensive Industries (EII) Initiative. A two-year pilot across 45 industrial sites identified more than 4 trillion Btu/year in potential energy savings. This presentation outlines plans for a follow-up technical assistance program tailored to pulp and paper mills. Available resources include a cost-savings scoping tool, implementation planning guidance, and technical support for applying advanced methods such as Pinch Analysis for integrated process-utilities optimization. The session introduces key Pinch Analysis principles and highlights case studies demonstrating measurable improvements. ORNL also seeks industry feedback on barriers to efficiency improvements, including technology gaps and resource needs. DOE’s broader objective is to accelerate productivity and economic competitiveness across U.S. energy-intensive industries.

Kamath, Dipti [ORNL] (ORCID:0000000278739994)↗

Development of process parameters and post-build conditions for qualification of LPBF 316 SS

To harness the potential of laser powder bed fusion (LPBF) 316H stainless steel (SS) for use in advanced nuclear reactors, extensive research efforts are needed to develop optimal laser processing parameters and appropriate heat treatments, taking into account various manufacturing platforms and locations. Furthermore, it is crucial to assess the material properties in environments relevant to reactor operating conditions. In collaboration with Oak Ridge National Laboratory (ORNL) and Los Alamos National Laboratory (LANL), Argonne National Laboratory (ANL) is dedicated to gaining insights into how the manufacturing process and post-build treatments impact the performance of LPBF 316H SS. This report provides an overview of ANL's research findings for FY23, focusing on LPBF 316H SS produced using a Renishaw AM400 laser system. Specifically, our research has concentrated on three key areas: 1) Optimum laser processing parameters: Leveraging the high-throughput printing technology, we systematically varied the laser power, exposure time and point distance within the same build. By measuring the as-printed porosity, we identified an optimum printing parameter window that produced materials with near-complete density; 2) Post-built treatment development: Materials printed with the optimum laser parameters underwent treatments including stress relief, solution annealing, and hot isostatic pressing. Subsequently, we fabricated tension, creep, and fatigue specimens from materials subjected to these different conditions. Over the next year, a series of scoping tests will be conducted to facilitate the selection of the most suitable post-build treatment condition; 3) Thermal aging effects on microstructure and properties: thermal aging at 550°C, 650°C and 750°C was conducted on LPBF 316H SS specimens up to 2500 h. Microstructural characterization was performed with electron microscopy, and the evolution of the dislocation cell structures and secondary phases was studied. Microhardness tests and tension tests were conducted to quantify changes in mechanical properties. The results revealed that the aged LPBF 316H SS exhibited a high density of Cr-rich and Mo-rich fine precipitates within grains due to the presence of a high density of dislocations and the globally distributed dislocation cell structures, which served as heterogeneous nucleation sites for secondary phases. Along grain boundaries, a Mo-Cr-rich phase, observed in 750°C-aged conditions, displayed significant thermal coarsening. Throughout the aging process, various mechanisms, including stress relaxation, dislocation cell structure recovery, solution hardening, and precipitation hardening, competed to influence materials strength. These results contribute towards establishing the technical basis for qualifying LPBF 316H SS for advanced nuclear reactor applications.

36 MATERIALS SCIENCE↗

Nanodiamonds in Advancing Biomedical Sciences

Nanodiamonds (NDs), tetrahedral carbon frameworks with size ranging from 1 to 100 nanometers, have gained growing attention in recent years due to their distinct optical, thermal, and mechanical properties compared to other carbon nanomaterials (e.g., graphene, carbon nanotubes, carbon dots). Combined with a high surface-to-volume ratio and tunable and chemically versatile surfaces, these support broad applications across catalysis, electronics, and life sciences. Moreover, the biocompatible characteristics of NDs enable their controllable interfacial interactions with biological systems, positioning them as excellent candidates for advancing cutting-edge biomedical sciences, particularly through the engineering of efficient material-biointerfaces that facilitate optimal interactions with biological systems. Among various forms of NDs, fluorescent nanodiamonds (FNDs) have emerged as some of the most impactful and rapidly advancing materials, demonstrating strong potential in ultrasensitive spin-enhanced bioimaging, high-precision biosensing, traceable drug delivery, and quantum-enabled biomedical technologies. This Perspective introduces the key principles underlying NDs and FNDs, including their structural properties, synthesis methods, and surface functionalization strategies. It also highlights emerging biomedical applications of NDs and FNDs, with particular emphasis on neurological disorders. Last, the article discusses current challenges in advancing NDs as a multifunctional platform for neural therapies with translational potential toward clinical trials.

36 MATERIALS SCIENCE↗