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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Resource Occurrence and Productivity in Existing and Proposed Wind Energy Lease Areas on the Northeast US Shelf

States in the Northeast United States have the ambitious goal of producing more than 22 GW of offshore wind energy in the coming decades. The infrastructure associated with offshore wind energy development is expected to modify marine habitats and potentially alter the ecosystem services. Species distribution models were constructed for a group of fish and macroinvertebrate taxa resident in the Northeast US Continental Shelf marine ecosystem. These models were analyzed to provide baseline context for impact assessment of lease areas in the Middle Atlantic Bight designated for renewable wind energy installations. Using random forest machine learning, models based on occurrence and biomass were constructed for 93 species providing seasonal depictions of their habitat distributions. We developed a scoring index to characterize lease area habitat use for each species. Subsequently, groups of species were identified that reflect varying levels of lease area habitat use ranging across high, moderate, low, and no reliance on the lease area habitats. Among the species with high to moderate reliance were black sea bass ( Centropristis striata ), summer flounder ( Paralichthys dentatus ), and Atlantic menhaden ( Brevoortia tyrannus ), which are important fisheries species in the region. Potential for impact was characterized by the number of species with habitat dependencies associated with lease areas and these varied with a number of continuous gradients. Habitats that support high biomass were distributed more to the northeast, while high occupancy habitats appeared to be further from the coast. There was no obvious effect of the size of the lease area on the importance of associated habitats. Model results indicated that physical drivers and lower trophic level indicators might strongly control the habitat distribution of ecologically and commercially important species in the wind lease areas. Therefore, physical and biological oceanography on the continental shelf proximate to wind energy infrastructure development should be monitored for changes in water column structure and the productivity of phytoplankton and zooplankton and the effects of these changes on the trophic system.

17 WIND ENERGY↗
DOE OSTI2022

A Double-Edged Sword: The Role of Prior Knowledge in Memory Aging

People accumulate knowledge throughout their lifespan and the accumulated knowledge influences how we encode and retrieve information in memory processing. This study aims to investigate the role of knowledge in associative memory across the adult lifespan, and specifically examines the effects of two material properties that interact with prior knowledge: congruency – whether the material is congruent with people’s prior knowledge, and ambiguity – whether the material is ambiguous to interpret based on prior knowledge. 273 participants (aged 22–70 years old) completed an incidental memory task online. Participants were shown pictures depicting an object in a scene and judged if the object was likely or unlikely to be in the particular scene. Later, in the recognition test, participants were asked to identify if the exact picture was presented earlier. The pictures were manipulated to have varying levels of congruency, meaning that some depicted likely object–scene pairs and some unlikely. We also measured how different the likely/unlikely judgment for each object–scene pair was across all participants to determine the ambiguity level of the object–scene pair: some were more likely to receive diverse responses across people, whereas others are unambiguously consistent (or inconsistent) with common knowledge shared by most people. We used mixed-effects logistic regressions to predict memory outcome for each trial as a function of age, age 2 , congruency/ambiguity, and their interactions. The object–scene pairs perceived as congruent had higher hit rates than incongruent ones, as well as higher false alarm rates, especially in middle-aged and older people. Higher ambiguity was also related to both greater true and false memory, independent of age. Finally, the effect of ambiguity only emerged when the object–scene pair was perceived incongruent. The results suggest that people rely on prior knowledge to process new information and that this reliance improves hit responses, but also induces false memories particularly for middle-aged and older people, suggesting a double-edged role of knowledge in associative memory and its disproportionate influence on memory aging. Over-reliance on knowledge in older adults, which has been suspected in other cognitive processes, may be one of the mechanisms underlying associative memory decrease in aging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
DOE OSTI2026

Understanding Isomeric Effects on Properties of Aviation Fuels via a Group Contribution Method: Preprint

The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Autonomous Operations for Advanced Reactors Utilizing Supervisory Control

Automation is a critical tenet of reactor plant operations as reliance on nuclear energy increases. Nuclear power plants require a large workforce which does not scale with output; that is, the cost per megawatt increases as reactor output becomes smaller. The economic viability of advanced reactors, particularly small modular reactors (SMRs) and microreactors, requires a significantly reduced onsite workforce. The logical solution is establishing a systematic process of elimination of reliance on human operators, and to the extent possible, replacing these actions with automated functions. In this paper, we propose a method for such transformation to establish a robust technical basis to enable transition to autonomy. Our method is based on finite state automata (FSA)—also known as finite state machines (FSMs). Relying on this method allows us to exploit the rich set of mathematical proofs available in the field of regular languages. FSA are one of the mathematical tools to model discrete event systems (DES). These properties are applied to produce an automated startup controller for the Massachusetts Institute of Technology Research Reactor (MITR). The startup procedure is captured in terms of discrete changes from one state to another while an independent supervisory control system directs the sequence of states and alerts a human in the event of an abnormal operation. First, the design and behavior of the MITR rod control system were modeled in Simulink. Then, the startup procedure was applied to the rod control system and the DES performed a startup by procedurally withdrawing rods to the subcritical position. The simulation also stops rod motion in response to an uncontrollable event and restarts rod motion once the event has been cleared.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Adsorption-Engineered Hydrocarbon Ionomers for Durable Proton-Exchange Membrane Fuel Cells

Reducing reliance on perfluoroalkyl substances (PFAS) in proton-exchange membrane fuel cells requires hydrocarbon ionomers that combine high performance with long-term durability, a persistent challenge in catalyst-layer design. Here, we identify oxidation-driven ionomer-catalyst interfacial degradation as a dominant failure pathway in hydrocarbon ionomer-bonded cathodes and introduce an adsorption-engineering strategy to overcome this limitation. The comparison of a commercial sulfonated poly(phenylene) (Pemion) with structurally engineered sulfonated poly(fluorene)s demonstrated that electrode durability is governed by the interplay between ionomer adsorption strength and resistance to oxidative degradation on carbon-supported Pt catalysts. A poly(fluorene) ionomer with mobile alkyl sulfonic acid groups forms resilient interfaces, delivering 1.28 A cm−2 at 0.65 V under fully humidified H2/air conditions (80°C and 150 kPaabs), comparable to Pemion. After 90,000 accelerated potential cycles, the poly(fluorene)-bonded cathode exhibits significantly improved durability, with only 29% performance loss compared to 58% for Pemion; further molecular refinement reduces the loss to 17%, approaching that of Nafion-bonded cathodes (14%). These findings establish adsorption-engineered ionomer design that decouples interfacial anchoring from oxidative degradation as a general strategy for achieving durable, high-performance PFAS-free PEM fuel cell electrodes.

08 HYDROGEN↗
DOE OSTI2024

Precision Calcination Mechanism of CaCO 3 to High-Porosity Nanoscale CaO CO 2 Sorbent Revealed by Direct In Situ Observations

Deploying energy storage and carbon capture at scale is hindered by the substantial endothermic penalty of decomposing CaCO 3 to CaO and CO 2 , and the rapid loss of CO 2 absorption capacity by CaO sorbent particles due to sintering at the high requisite decomposition temperatures. The decomposition reaction mechanism underlying sorbent deactivation remains unclear at the atomic level and nanoscale due to past reliance on postmortem characterization methods with insufficient spatial and temporal resolution. Thus, elucidating the important CaCO 3 decomposition reaction pathway requires direct observation by time-resolved (sub-)nanoscale methods. Here, chemical and structural dynamics during the decomposition of CaCO 3 nanoparticles to nanoporous CaO particles comprising high-surface-area CaO nanocrystallites are examined. Comparing in situ transmission electron microscopy (TEM) and synchrotron X-ray diffraction experiments gives key insights into the dynamics of nanoparticle calcination, involving anisotropic CaCO 3 thermal distortion before conversion to thermally dilated energetically stable CaO crystallites. Time-resolved TEM uncovered a novel CaO formation mechanism involving heterogeneous nucleation at extended CaCO 3 defects followed by sweeping reaction front motion across the initial CaCO 3 particle. These observations clarify longstanding, yet incomplete, reaction mechanisms and kinetic models lacking accurate information about (sub-)nanoscale dynamics, while also demonstrating calcination of CaCO 3 without sintering through rapid heating and precise temperature control.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nanohybrid of Silver‐MXene: A Promising Sorbent for Iodine Gas Capture from Nuclear Waste

The increasing reliance on nuclear energy as a significant low-carbon power source necessitates effective solutions for managing radioactive emissions. This study introduces a novel application of MXene nanohybrids, specifically silver-MXene (Ag-Ti 3 C 2 T x ), as an effective sorbent for radioiodine off-gas capture at an operating temperature of 150 °C. Through comprehensive material characterization, including X-ray diffraction, scanning and transmission electron microscopies, energy-dispersive X-ray spectroscopy, Raman spectroscopy, thermogravimetric analysis, inductively coupled plasma optical emission spectroscopy, and gas sorption analyses, the successful loading of Ag nanoparticles onto Ti 3 C 2 T x is confirmed and the subsequent formation of AgI upon iodine capture. The results demonstrate that Ag-Ti 3 C 2 T x exhibits superior iodine uptake compared to traditional silver-based sorbents such as silver mordenite zeolite (AgZ) and silver-functionalized silica aerogel (AgAero). The Ag-Ti 3 C 2 T x achieves an iodine loading of 946 mg g −1 , significantly outperforming AgZ (131 mg g −1 ). These findings highlight the potential of Ag-Ti 3 C 2 T x as a highly efficient, thermally stable sorbent for radioiodine capture, and potentially addressing key limitations of existing materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

The Impact of Catalyst Layer Composition and Structure on Performance and Durability of PEMWE Anodes

A study of various compositions of anodes for proton exchange membrane water electrolysis aimed at reducing precious metal content and system costs without compromising performance and durability is presented. A key challenge in current water electrolysis technologies is the reliance on high iridium loadings to ensure sufficient catalytic activity, electronic conductivity, and durability for the oxygen evolution reaction. To address this, catalyst layers based on the stable but kinetically limited rutile phase of iridium oxide are combined with platinum nanoparticles and carbon-based additives to improve structural properties and ink processability. By systematically varying the volume ratios of carbon to precious metals and ionomer to solids, compositional trends have been identified, and significant performance improvements have been achieved. Structural and elemental analysis confirms improved dispersion of platinum and iridium can be achieved, as well as electronic conductivity improvements within the catalyst layer. Polarization curve analysis has shown the ability of added Pt and C to significantly increase catalytic activity. These results highlight the potential positive impact of Pt and C on anode structure, composition, and cell performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Iron Single‐Atom Catalyzed N‐Alkylation of Amines with Alcohols via Solvent‐Free Borrowing Hydrogen Strategy

Abstract Industrial hydrogenation is a pivotal process in chemical synthesis. However, it has significant drawbacks, including high cost, safety risks associated with the use of molecular hydrogen gas, and substantial energy demands due to the need for elevated temperatures and pressures to achieve satisfactory yields. The borrowing hydrogen synthesis, which enables the transfer of hydrogen between molecules, offers a promising approach for green, one‐pot synthesis of industrially important chemicals and intermediates. Despite its potential, the broad application remains limited due to the reliance on toxic solvents, expensive noble metal catalysts, and the still restricted efficiency and substrate scope. In this study, the first solvent‐free strategy for theN‐alkylation of amines with alcohols is presented, employing an N‐doped graphene‐supported Fe single‐atom catalyst (Fe SA @N‐G; 1.06 wt.%). This approach achieves superior conversion and selectivity (up to 99%) along with record values for turnover number (TON, 1032.7) and turnover frequency (TOF, 413.1 h -1 ) for the coupling reaction of aniline with benzyl alcohol, surpassing all previously reported catalysts. DFT calculations, combined with experimental data, elucidated the reaction mechanism and identified the Fe 1 (III)‐N 4 active site participating in Fe‐H hydride transfer and containing two pyrrolic and two pyridinic nitrogens bound to the Fe center. The developed technology is further supported by the catalyst's excellent scalability, reusability, and performance under continuous‐flow conditions. Additionally, the exceptional efficiency of the single‐atom catalyst is demonstrated across more than 50 substrates, including reactions involving both aliphatic and aromatic amines with aliphatic and aromatic alcohols. The industrial applicability of this technology is validated through the synthesis of pharmaceutically relevant compounds, including stimulant drugs, antihistamines, and pharmaceutical intermediates.

Chemistry↗
DOE OSTI2020

Effect of Fluorination on Lithium Transport and Short-Range Order in Disordered-Rocksalt-Type Lithium-Ion Battery Cathodes

Fluorine substitution is a critical enabler for improving the cycle life and energy density of disordered rocksalt (DRX) Li-ion battery cathode materials which offer prospects for high energy density cathodes, without the reliance on limited mineral resources. Due to the strong Li–F interaction, fluorine also is expected to modify the short-range cation order in these materials which is critical for Li-ion transport. In this work, density functional theory and Monte Carlo simulations are combined to investigate the impact of Li–F short-range ordering on the formation of Li percolation and diffusion in DRX materials. Additionally, the modeling reveals that F substitution is always beneficial at sufficiently high concentrations and can, surprisingly, even facilitate percolation in compounds without Li excess, giving them the ability to incorporate more transition metal redox capacity and thereby higher energy density. It is found that for F levels below 15%, its effect can be beneficial or disadvantageous depending on the intrinsic short-range order in the unfluorinated oxide, while for high fluorination levels the effects are always beneficial. Using extensive simulations, a map is also presented showing the trade-off between transition-metal capacity, Li-transport, and synthetic accessibility, and two of the more extreme predictions are experimentally confirmed.

25 ENERGY STORAGE↗
DOE OSTI2022

Next-Generation Cobalt-Free Cathodes – A Prospective Solution to the Battery Industry's Cobalt Problem

Lithium-ion batteries are overreliant on cobalt containing cathodes. Current projections estimate that hundreds of millions of electric vehicles (EVs) will be on the road by 2050, and this ever-growing demand threatens to deplete global cobalt reserves at an alarming rate. Moreover, cobalt supply chain issues have significantly increased cobalt prices throughout the last decade. As such, energy storage research and development need to reduce the reliance on cobalt to meet ever-growing demand for lithium-ion batteries. The present review summarizes the science and technology gaps and potential of numerous cobalt-free Li-ion cathodes including layered, spinel, olivine, and disordered rock-salt systems. Despite the promising performance of these Co-free cathodes, scale-up and manufacturing bottlenecks associated with these materials must also be addressed to enable widespread adoption in commercial batteries. Overall, this review broadly highlights the enormous promise of “zero-cobalt” Li-ion batteries to enable sustainable production of EVs in the coming decades.

25 ENERGY STORAGE↗
DOE OSTI2026

Electrochemically recovered struvite matches conventional fertilizers in soil nutrient supply

Phosphorus (P) is an essential macronutrient for agriculture, yet global reliance on finite phosphorite reserves raises concerns about long-term food and nutrient security. Struvite (MgNH 4 PO 4 ·6H 2 O), a mineral recovered from municipal wastewater, represents a sustainable and circular source of P, but uncertainty remains regarding its nutrient availability relative to conventional fertilizers. This study evaluated the fertilizer value and environmental safety of electrochemically recovered struvite (RecoP) in comparison with diammonium phosphate (DAP), triple superphosphate (TSP), and rock phosphate (RockP). Nutrient availability, potential contaminant concentrations, and soil P and nitrogen (N) dynamics were assessed using standardized chemical analyses and an 8-week incubation experiment across three soils with contrasting soil types. RecoP contained 14.7% citrate-soluble P, significantly greater than RockP and comparable to DAP and TSP. Across all soils, RecoP increased both readily available and moderately labile P pools (p < 0.01), matching the performance of conventional fast-release fertilizers. Despite containing approximately half the total of DAP, RecoP stimulated nitrification rates that were 2.2 times greater than the control and exceeded those of TSP, suggesting that fertilizer-derived N bioavailability, rather than total N loading, governed microbial N transformation responses. Concentrations of potentially toxic elements in RecoP were consistently well below US Environmental Protection Agency regulatory limits thresholds. Collectively, these results indicate that RecoP functions as a readily available source of both P and N, comparable to widely used conventional fertilizers, while meeting environmental safety standards. RecoP therefore represents a viable component of sustainable nutrient management strategies that support P recycling and agricultural productivity.

Kharal, Sudarshan [Ohio Univ., Athens, OH (United ↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Upcycling of Waste Plastics into Carboxylic Acids for Biodegradable Surfactants

This work outlines a process for producing high‐purity (>95%) carboxylate surfactants from post‐consumer recycled high‐density polyethylene (PCR‐HDPE). The approach involves the thermal depolymerization of PCR‐HDPE via pyrolysis, followed by fractional distillation to isolate C9–C14 olefins. These olefins undergo hydroformylation using cobalt carbonyl catalysts to generate aldehydes, which are subsequently oxidized to carboxylic acids using Pinnick oxidation under mild aqueous‐phase conditions. Neutralization of the resulting carboxylic acids with sodium hydroxide produces plastic‐derived carboxylate surfactants (PDCs) in the form of sodium carboxylates. Subsequent purification steps ensure surfactant‐grade purity and enable accurate assessment of physicochemical properties. The resulting PDCs are evaluated for critical micelle concentration (CMC), foamability, surface tension reduction, and calcium ion tolerance, demonstrating competitive behavior with conventional anionic carboxylate surfactants. This route provides a sustainable alternative for surfactant production, reducing reliance on fossil‐derived feedstocks and valorizing plastic waste streams through chemical upcycling.

Biodegradable surfactants↗
DOE OSTI2026

Effect of Antagonistic Binder–Catalyst Interactions on Catalytic Activity in Lithium–Sulfur Batteries

Lithium-sulfur (Li-S) batteries are a promising next-generation energy storage solution, as they can reduce reliance on critical transition metals while offering high energy densities. However, their deployment is hindered by low sulfur utilization and the formation/diffusion of lithium polysulfides (LiPSs). While transition-metal catalysts and polymeric binders have been independently developed to enhance redox kinetics and LiPS adsorption, their mutual compatibility has remained largely unexplored. We show here that binder-catalyst interactions can significantly impact catalytic performance. Employing TiO 2 as a generic catalyst, the electrochemical performance is shown to depend strongly on the binder environment. TiO 2 paired with lithiated polyacrylic acid (LiPAA) shows benign interactions, resulting in enhanced cycle life. In contrast, pairing TiO 2 with protonated PAA produces antagonistic interactions that hinder Li 2 S growth. A mechanistic analysis unveils that the carboxylic H atom in PAA promotes COO − coordination to Ti sites, occupying catalytic centers and suppressing LiPS adsorption, increasing charge transfer and diffusion resistances. This phenomenon is observed across multiple catalysts, indicating that COOH-functionalized binders may broadly hinder catalytic activity. Overall, this study underscores the need for holistic cathode design and identifies binder-catalyst compatibility as an important parameter for high-performance Li-S batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

A Rapid, Sustainable, One‐step Mechanochemical Strategy for Synthesizing Gold Nanoparticle‐Doped Covalent Organic Frameworks

Doping gold nanoparticles within covalent organic frameworks (AuNPs@COFs) has garnered enormous momentum due to their unique properties and broad applications. Nevertheless, prevailing multi-step synthesis is plagued with low time efficiency, eco-unfriendliness, and tedious protocols. Herein, we introduce a rapid, sustainable, scalable, one-step mechanochemical strategy for synthesizing up to four AuNPs-doped COFs via steel ball milling within an hour under ambient conditions. This approach overcomes the synthetic barriers of conventional multi-step solution-based methods, such as extended reaction times (5 days), milligram scale, the use of toxic solvents, elevated temperatures, and reliance on external reducing agents. One exemplary AuNPs@COF (AuNPs@DMTP-TPB) exhibits high crystallinity, porosity, small AuNP size, and uniform dispersion (5.4±0.6 nm), surpassing its counterpart synthesized via multi-step solution-based methods (6.4±1.1 nm). Notably, the gram-scale synthesis of AuNPs@DMTP-TPB can be successfully achieved. Control experiments suggest that the in situ formation of AuNPs is attributed to the galvanic reduction of gold precursor by stainless steel apparatus. As a proof-of-concept catalytic application, AuNPs@DMTP-TPB demonstrates remarkable catalytic activity and recyclability for the aqueous reduction of 4-nitrophenol under ambient conditions. This study provides an environmentally benign and fast pathway to synthesize AuNPs@COFs via mechanochemistry for the first time, opening tremendous possibilities for heterogeneous catalysis and beyond.

Nailwal, Yogendra↗