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The transition to electric vehicles (EVs) is pivotal for achieving energy security and integrating grid stability, with lithium-ion batteries (LIBs) playing a central role in this transformation. However, the conventional wet electrode manufacturing relying on N-methyl-2-pyrrolidone (NMP) solvent is energy intensive and costly. Dry processing (DP) has emerged as a promising alternative, eliminating solvents and using polytetrafluoroethylene (PTFE) binder for electrode fabrications. Despite its advantages, DP faces a critical challenge: poor interfacial adhesion between the hydrophobic PTFE binder and the hydrophilic cathode active material (CAM), particularly LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), which undermines electrode performance. Here, to address this, we introduced a novel vapor-phase trimethoxymethylsilane (TMMS) coating to hydrophobize the CAM surface, enhancing compatibility with PTFE. This surface modification significantly enhances binder – CAM interactions, enabling uniform mixing and robust electrode integrity without damaging the CAM particles. Our findings advance the feasibility of environmentally sustainable and cost-effective dry processing, representing a significant step toward sustainable battery manufacturing.
This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.
Abstract Epitaxy, a process to prepare crystalline materials in nanostructures and thin films, is the core technology for preparing high‐quality materials as a key enabler of next‐generation microelectronics and quantum information system. Progress in epitaxy has been expanding the choice of materials and their heterostructures beyond the combinations limited by materials compatibility. However, the improvement of material quality, physical implementation of materials with unique properties, and integration of incommensurate materials in an architecture have been the challenging issues. Emerging materials, including 2D materials and quantum materials, have opened opportunities to study epitaxy mechanisms and realize various functional devices. Acceleration of discovery and progress in epitaxy research should be accomplished by “understanding of epitaxy under various circumstances at multiple length scales” and “integration of experiments and models.” In the perspective, a basic summary of the status of epitaxially grown materials, the challenges in epitaxy research, and integration of modeling epitaxy and ultimate control of the epitaxy process with advanced characterization techniques are discussed.
An improved recycling and recovery process for the cladding material from spent nuclear fuels is very important toward confirming nuclear energy to support ongoing sustainable development of nuclear management by reducing waste and conserving resources. Nuclear spent fuel cladding materials such as zirconium alloys have economic values and can be recovered, and their recovery eliminates problems in waste disposal and conserves valuable resources. Over 110 published reports and journal articles are reviewed and summarized herein, with a main focus on documenting recovery techniques used to recover cladding materials from spent nuclear fuel and recent developments. Several recovery techniques which are used at present times, such as mechanical separation, chemical dissolution, and hydrometallurgical processes have been covered with examples and discussions. Difficulties within the recovery process are also discussed, and most probable areas for future research in improving efficiency and sustainability of recovering cladding material are identified and discussed at the end. Here, this review could be an important document to the field of spent nuclear fuel reprocessing, recovering valuables and thereby offering guidance on how to effectively manage, safely handle, and reduce nuclear waste. In addition to reducing the volume and radiotoxicity of high-level waste, this review also highlights the potential economic benefit of recovering zirconium from spent fuel cladding by relating typical zirconium metal prices to the mass of cladding per tonne of spent fuel, illustrating that the recoverable material value is non-negligible compared with back-end fuel-cycle costs.
Bridgestone aims to minimize resource depletion and greenhouse gas (GHG) emissions by using 100% sustainable materials by 2050. As part of this goal Bridgestone is working to develop a first-of-kind end-of-life recycling process for tire material circularity and the decarbonization of new tire production. Used tires can be gasified to produce intermediate syngas (H 2 + CO) that can be further converted into ethanol using mature technology. The ethanol can then be converted into butadiene, a key precursor of new tires, using patented PNNL technology, enabling circularity for end-of-life tires. Indeed, PNNL has developed a new patented thermocatalytic-based technology for the conversion of ethanol into butadiene that allows for high carbon efficiency and improved catalyst longevity compared to World War II baseline catalyst. The objective here is to continue the development of this processing with the goal of commercial deployment. This includes development of engineered catalysts (e.g., extrudates) and their evaluation under industrially relevant conditions for deployment of a pilot scale. If successful, Bridgestone will subsequently utilize this catalyst technology at pilot and then commercialization scale creating jobs in both construction sector and industry sector in a chosen location that promotes greater diversity, equity, and inclusion through key policies, training, and recruiting practices. Taken together, this work will support the U.S. Department of Energy goal for production of renewable chemicals with > 70% GHG emissions reduction relative to petroleum-derived counterparts and supporting > 1 MMT/ yr CO 2 e emissions reduction by 2030.
GPU Monte Carlo Simulation Code with a taste of RASPA We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.
Purpose: Municipal Solid Waste (MSW) is a potentially valuable sustainable feedstock for fuel and chemical production due to its carbon-rich content and low cost. This study aims to assess the material handling properties of paperand plastic-rich MSW feedstocks to mitigate equipment failure and processing downtime. Methods: The material handling properties of crumbled MSW feedstocks were measured using apowder rheometer with mass flow hopper calculations to assess handling performance. Inverse gas chromatography was use to measure the surface energy differences between feedstocks. Electron microscopy and Raman spectroscopy was used to evaluate microscale features that may contribute to material handling differences. Results: Plastic and paper rich feedstocks crumbled to a nominal 2 mm particle size were observed to have similar flow and handling characteristics with reasonable hopper outlets. 2 mm plastic rich crumbles, with their higher bulk density, exhibited superior flow performance. By contrast, 4 mm material required significantly larger hopper outlets, indicating poor flowability. Paper rich and 4 mm plastic rich samples displayed broad particle size distributions, which contributed to particle interlocking, jamming, and other flow issues. Electron microscopy revealed that plastic rich samples were significantly smoother, enhancing their flowability compared to the rougher, paper rich materials. Conclusions: This study establishes critical material handling baselines for processing MSW as a viable feedstock for fuel and chemical production. The findings highlight the importance of optimizing particle size and feedstock composition to improve flowability and handling performance.
Biorefineries, which process biomass feedstocks into valuable (bio)products, aim to replace fossil fuel-based refineries to produce energy and chemicals, reducing environmental and health hazards, including climate change, and supporting a sustainable economy. In particular, lignocellulose-based biorefineries, utilizing the most abundant renewable feedstock on Earth, have significant potential to supply sustainable energy, chemicals and materials. Ionic liquids (ILs, organic salts with low melting temperatures) and deep eutectic solvents (DESs, mixtures with eutectic points lower than the ideal mixture) are capable of dissolving some of the key lignocellulose polymers, and even the whole biomass. Furthermore, they have intrinsic advantages over molecular solvents, including safer usage profiles and high tunability, which allow tailored physicochemical properties. Such properties provide unique opportunities for the development of new processes that could unlock the full potential of future biorefineries. Here, we review the current state of lignocellulosic biomass processing with ILs and DESs, with a specific focus on the pretreatment chemistry, process flow and products from each component; followed by discussions on sustainability assessments and technological challenges. We aim to inform the research community about the opportunities, challenges and perspectives in developing truly sustainable lignocellulose-based biorefineries.
Refractory alloys (RAs) are promising materials due to their exceptional physicochemical properties, but most research remains at the laboratory scale. For broader adoption, advancements in manufacturing are essential. Because their high stability makes conventional methods like machining and casting difficult, additive manufacturing (AM) is emerging as an effective approach for fabricating refractory alloy components. However, AM's repeated non-equilibrium thermal cycles introduce undesired features (e.g. defects, anisotropic microstructures, and residual stresses), which are magnified due to RAs’ unique properties. This paper comprehensively reviews the state-of-the-art methods of AM for refractory alloys. It explores data analytics techniques to establish design rules based on multi-fidelity experimental and computational methods. Furthermore, it investigates integrated, collaborative efforts to harmonise standalone databases, information, knowledge, and predictive models at multi-physics, multi-stage, and multi-scale. Unlike the existing literature that focuses primarily on material systems or process fundamentals, this work provides an integrated perspective on AM of refractory alloys from a data analytics standpoint, highlighting the roles of integrated computational materials engineering (ICME), verification, validation, and uncertainty quantification (VV&UQ), and digital twin-driven qualification in overcoming data scarcity and accelerating rapid qualification.
A common method of determining elastic material properties is utilizing an ultrasonic pulse receiver. This is a non-destructive test (NDT) causing no plastic deformation of the material. It requires a pulse generator, oscilloscope, and two transducers to measure the sound velocity of a material. Both longitudinal and shear sound velocities may be obtained by utilizing the appropriate transducer. Acoustic nondestructive testing methods are often used in manufacturing certification processes and for material defect detection in industry. These methods can also be utilized to directly measure the elastic moduli of a material without damaging the material. Overall, it is a low-cost experiment with minimal preparation, and is easy to use.
In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.
High-efficiency and low-cost catalysts for oxygen evolution reaction (OER) are critical for electrochemical water splitting to generate hydrogen, which is a clean fuel for sustainable energy conversion and storage. Among the emerging OER catalysts, transition metal dichalcogenides have exhibited superior activity compared to commercial standards such as RuO 2 , but inferior stability due to uncontrolled restructuring with OER. Here, in this study, we create bimetallic sulfide catalysts by adapting the atomic ratio of Ni and Co in Co x Ni 1-x S y electrocatalysts to investigate the intricate restructuring processes. Surface-sensitive X-ray photoelectron spectroscopy and bulk-sensitive X-ray absorption spectroscopy confirmed the favorable restructuring of transition metal sulfide material following OER processes. Our results indicate that a small amount of Ni substitution can reshape the Co local electronic structure, which regulates the restructuring process to optimize the balance between OER activity and stability. This work represents a significant advancement in the development of efficient and noble metal-free OER electrocatalysts through a doping-regulated restructuring approach.
Tantalum is a refractory metal used in a variety of harsh environment applications. Additive manufacturing of tantalum is limited based on its high melting point and affinity for oxygen. Directed Energy Deposition is an additive manufacturing technique with rapid deposition time and compositional flexibility within builds. Additively manufactured tantalum is susceptible to a variety of material and process-based defects. Various lack-of-fusion defects were identified resulting from excessive powder feed rates or insufficient laser power. Oxygen impurities in some samples caused cracking and increased material hardness. Precipitates were identified in the highly oxidized samples which were printed immediately after the build chamber was opened. High-density, low-defect parts were successfully produced. The effects of scan speed, laser power, and powder feed rate on density and defects were analyzed. A processing window was identified for producing high-quality parts which requires adequately high laser power and lower powder feed rate.
Experiments with graphene have demonstrated that 2D van der Waals materials can be stable, robust, and efficiently manipulated at the level of individual atomic planes. However, the stability and manipulation of 1D van der Waals materials and individual atomic chains remains elusive. Here, in this work, the ability to exfoliate and process two representative van der Waals materials containing 1D motifs, namely MoI 3 and Ta 2 Se 8 I, at the scale of individual atomic chains is demonstrated. High‐resolution transmission electron microscopy and atomic force microscopy studies confirm the presence of stable individual atomic chains of MoI 3 at room temperature. It is further shown that 1D van der Waals materials with low exfoliation energy, such as Ta 2 Se 8 I, can be processed with electron beams to achieve suspended individual atomic chains. Ab initio calculations corroborate the findings regarding the cleavage energies and the thermodynamic stability of individual atomic chains in these 1D van der Waals materials. These results demonstrate that the top‐down approach in material processing can be extended to the scale of individual chains.
The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.
Material design and accessible manufacturing are often at odds with each other, calling for creative solutions to adapt high-performance materials to available processes. This challenge is represented well by in-mold electronics, an innovative approach to the manufacture of 3D circuitry and electronic components that offers game-changing advantages. In-mold electronics relies on vacuum forming processes, which are historically limited to thermoplastics. Extending these methods to include thermosets would enable manufacturing of robust components with desirable properties. Here, we provide a solution to make thermoset materials amenable to vacuum forming. Specifically, an ambient polymerization is used to transition a liquid monomeric solution to an elastomeric gel. These free-standing gels can then be vacuum formed, and the reaction can be completed via frontal polymerization. Thermoset materials produced with this method have properties that provide benefits over traditionally employed thermoplastic substrates and enable 3D device integration into environmentally demanding architectural, automotive, and extraterrestrial structures.
Liquid-fueled, thermal-spectrum molten salt breeder reactors (TS-MSBRs) offer the potential for affordable, safe, inexhaustible energy with minimal potential for nuclear material misuse and without significant actinide waste generation. Realizing the full set of TS-MSBR capabilities is only now becoming possible with the advent of advanced fuel-salt processing techniques, improved materials, and a more detailed understanding of fuel-salt properties. Additionally, modern higher-fidelity modeling and simulation methods enable a more detailed evaluation of TS-MSBR design options. TS-MSBRs, however, remain immature and will require substantial, sustained development resources.