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At least 523 records · Page 29

Sustainable recycling of cathode scraps via Cyrene-based separation

Separation of cathode materials from the current collector remains a challenging task for the recycling of both spent lithium-ion batteries and cathode scraps. Dissolving the organic binder polyvinylidene difluoride (PVDF) with an organic solvent to recover both cathode materials and Al foils is an efficient and promising method. However, the use of toxic solvents limits their practical application in recycling large amounts of cathode scraps generated during the manufacturing process. Cyrene, a bioderived green solvent, is proposed here for a solvent-based separation process. This study investigated a closed-loop recovery process to reclaim cathode materials, Al foils, and PVDF binder from cathode scraps. Furthermore, the reuse of the Cyrene solvent led to a circular recycling process. Additionally, the Cyrene-based separation process embraces a sustainable electrode recovery and reuse platform and paves the way for battery recycling.

25 ENERGY STORAGE↗

End-of-life flows of multiple cycle consumer products

Explicit expressions for the end-of-life flows (EOL) of single and multiple cycle products (MCPs) are presented, including deterministic and stochastic EOL exit. The expressions are given in terms of the physical parameters (maximum lifetime, T, annual cycling frequency, f, number of cycles, N, and early discard or usage loss). EOL flows are also obtained for hi-tech products, which are rapidly renewed and thus may not attain steady state (e.g. electronic products, passenger cars). A ten-step recursive procedure for obtaining the dynamic EOL flow evolution is proposed. Applications of the EOL expressions and the ten-step procedure are given for electric household appliances, industrial machinery, tyres, vehicles and buildings, both for deterministic and stochastic EOL exit, (normal, Weibull and uniform exit distributions). The effect of the physical parameters and the stochastic characteristics on the EOL flow is investigated in the examples: it is shown that the EOL flow profile is determined primarily by the early discard dynamics; it also depends strongly on longevity and cycling frequency: higher lifetime or early discard/loss imply lower dynamic and steady state EOL flows. The stochastic exit shapes the overall EOL dynamic profile: Under symmetric EOL exit distribution, as the variance of the distribution increases (uniform to normal to deterministic) the initial EOL flow rise becomes steeper but the steady state or maximum EOL flow level is lower. The steepest EOL flow profile, featuring the highest steady state or maximum level, as well, corresponds to skew, earlier shifted EOL exit (e.g. Weibull). Since the EOL flow of returned products consists the sink of the reuse/remanufacturing cycle (sink to recycle) the results may be used in closed loop product lifecycle management operations for scheduling and sizing reverse manufacturing and for planning recycle logistics. Decoupling and quantification of both the full age EOL and of the early discard flows is useful, the latter being the target of enacted legislation aiming at increasing reuse.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Recycling of additively printed rare-earth bonded magnets

Here, we describe an efficient and environmentally benign method of recycling of additive printed Nd-Fe-B polymer bonded magnets. Rapid pulverization of bonded magnets into composite powder containing Nd-Fe-B particles and polymer binder was achieved by milling at cryogenic temperatures. The recycled bonded magnets fabricated by warm compaction of ground cryomilled coarse composite powders and nylon particles showed improved magnetic properties and density. Remanent magnetization and saturation magnetization increased by 4% and 6.5% respectively, due to enhanced density while coercivity and energy product were retained from the original additive printed bonded magnets. This work presents a facile method that enables the direct reuse of end-of-life bonded magnets for remaking new bonded magnets. In addition to magnetic properties, mechanical properties comparable to commercial products have been achieved. This research advances efforts to ensure sustainability in critical materials by forming close loop supply chain.

36 MATERIALS SCIENCE↗

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗

Value-creating upcycling of retired electric vehicle battery cathodes

The electrification revolution in the automobile and other industries demands annual production capacity of batteries of at least 10 2 GWh, which presents a twofold challenge: supply of key materials such as cobalt and nickel and recycling when batteries are retired from use. Pyrometallurgical and hydrometallurgical recycling are currently used in industry but suffer from complexity, high costs, and secondary pollution. Here we report a molten-salt-based method for direct recycling (MSDR) that is environmentally benign and creates value on the basis of a techno-economic analysis using real-world data and price information. We also experimentally demonstrate the feasibility of MSDR by upcycling a low-nickel polycrystalline LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC) cathode material into Ni-rich (Ni > 65%) single-crystal NMCs with increased energy density (>10% increase) and outstanding electrochemical performance (>94% capacity retention after 500 cycles). This work may open opportunities for closed-loop recycling of electric vehicle batteries and manufacturing of next-generation NMC cathode materials.

25 ENERGY STORAGE↗

Networks and interfaces as catalysts for polymer materials innovation

Autonomous experimental systems offer a compelling glimpse into a future where closed-loop, iterative cycles—performed by machines and guided by artificial intelligence (AI) and machine learning (ML)—play a foundational role in materials research and development. This perspective draws attention to the roles of networks and interfaces—of and between humans and machines—for the purpose of generating knowledge and accelerating innovation. Polymers, a class of materials with massive global impact, present a unique opportunity for the application of informatics and automation to pressing societal challenges. To develop these networks and interfaces in polymer science, the Community Resource for Innovation in Polymer Technology (CRIPT)—a polymer data ecosystem based on novel polymer data model, representation, search, and visualization technologies—is introduced. The ongoing co-design efforts engage stakeholders in industry, academia, and government to uncover rapidly actionable, high-impact opportunities to build networks, bridge interfaces, and catalyze innovation in polymer technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Emerging roles of circular RNAs in thyroid cancer

As the most common endocrine-related malignant tumor, the prevalence of thyroid cancer (TC) has soared strikingly over the past thirty years then verged to stabilization or even descension temporally. Distributed in a cell-specific manner, circular RNAs (circRNAs) is a novel class of non-coding RNAs characterized by its covalently closed loop without 5′-terminal cap and 3′-terminal poly A tail, which guarantee its distinctive evolutionary conservation and exonucleases resistance. Emerging evidence indicates that circRNA participates in the pathogenesis and carcinogenesis of several cancers including thyroid cancer. In this review, we concentrated on the connection between circRNAs and thyroid cancer so as to obtain a more profound understanding. We aim to discuss this relationship between TC and circRNAs by summarizing the effect of various circRNAs on tumor biological behaviors and clinical application, and systematically outlook the conceivable application of circRNAs in TC diagnosis and therapy.

60 APPLIED LIFE SCIENCES↗

Adaptive laboratory evolution and genetic engineering improved terephthalate utilization in Pseudomonas putida KT2440

Poly(ethylene terephthalate) (PET) is one of the most ubiquitous plastics and can be depolymerized through biological and chemo-catalytic routes to its constituent monomers, terephthalic acid (TPA) and ethylene glycol (EG). TPA and EG can be re-synthesized into PET for closed-loop recycling or microbially converted into higher-value products for open-loop recycling. Here, in this study, we expand on our previous efforts engineering and applying Pseudomonas putida KT2440 for PET conversion by employing adaptive laboratory evolution (ALE) to improve TPA catabolism. Three P. putida strains with varying degrees of metabolic engineering for EG catabolism underwent an automation-enabled ALE campaign on TPA, a TPA and EG mixture, and glucose as a control. ALE increased the growth rate on TPA and TPA-EG mixtures by 4.1- and 3.5-fold, respectively, in approximately 350 generations. Evolved isolates were collected at the midpoints and endpoints of 39 independent ALE experiments, and growth rates were increased by 0.15 and 0.20 h -1 on TPA and a TPA-EG, respectively, in the best performing isolates. Whole-genome re-sequencing identified multiple converged mutations, including loss-of-function mutations to global regulators gacS, gacA, and turA along with large duplication and intergenic deletion events that impacted the heterologously-expressed tphAB II catabolic genes. Reverse engineering of these targets confirmed causality, and a strain with all three regulators deleted and second copies of tphAB II and tpaK displayed improved TPA utilization compared to the base strain. Taken together, an iterative strain engineering process involving heterologous pathway engineering, ALE, whole genome sequencing, and genome editing identified five genetic interventions that improve P. putida growth on TPA, aimed at developing enhanced whole-cell biocatalysts for PET upcycling.

36 MATERIALS SCIENCE↗

Stabilizing a strongly nonlinear structure through shaker dynamics in fixed frequency voltage control tests

Bifurcations are commonly encountered during force controlled swept and stepped sine testing of nonlinear structures, which generally leads to the so-called jump-down or jump-up phenomena between stable solutions. There are various experimental closed-loop control algorithms, such as control-based continuation and phase-locked loop, to stabilize dynamical systems through these bifurcations, but they generally rely on specialized control algorithms that are not readily available with many commercial data acquisition software packages. A recent method was developed to experimentally apply sequential continuation using the shaker voltage that can be readily deployed using commercially available software. By utilizing the stabilizing effects of electrodynamic shakers and the force dropout phenomena in fixed frequency voltage control sine tests, this approach has been demonstrated to stabilize the unstable branch of a nonlinear system with three branches, allowing for three multivalued solutions to be identified within a specific frequency bandwidth near resonance. Recent testing on a strongly nonlinear system with vibro-impact nonlinearity has revealed jumping behavior when performing sequential continuation along the voltage parameter, like the jump phenomena seen during more traditional force controlled swept and stepped sine testing. Here, this paper investigates the stabilizing effects of an electrodynamic shaker on strongly nonlinear structures in fixed frequency voltage control tests using both numerical and experimental methods. The harmonic balance method is applied to the coupled shaker-structure system with an electromechanical model to simulate the fixed voltage control tests and predict the stabilization for different parameters of the model. The simulated results are leveraged to inform the design of a set of experiments to demonstrate the stabilization characteristics on a fixture-pylon assembly with a vibro-impact nonlinearity. Through numerical simulation and experimental testing on two different strongly nonlinear systems, the various parameters that influence the stability of the coupled shaker-structure are revealed to better understand the performance of fixed frequency voltage control tests.

42 ENGINEERING↗

Automated control and optimization of laser-driven ion acceleration

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

47 OTHER INSTRUMENTATION↗

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗

Multicolor Three-Dimensional Tracking for Single-Molecule Fluorescence Resonance Energy Transfer Measurements

Single-molecule fluorescence resonance energy transfer (smFRET) remains a widely utilized and powerful tool for quantifying heterogeneous interactions and conformational dynamics of biomolecules. However, traditional smFRET experiments either are limited to short observation times (typically less than 1 ms) in the case of “burst” confocal measurements or require surface immobilization which usually has a temporal resolution limited by the camera framing rate. We developed a smFRET 3D tracking microscope that is capable of observing single particles for extended periods of time with high temporal resolution. The confocal tracking microscope utilizes closed-loop feedback to follow the particle in solution by recentering it within two overlapping tetrahedral detection elements, corresponding to donor and acceptor channels. We demonstrated the microscope’s multicolor tracking capability via random walk simulations and experimental tracking of 200 nm fluorescent beads in water with a range of apparent smFRET efficiency values, 0.45-0.69. We also demonstrated the microscope’s capability to track and quantify double-stranded DNA undergoing intramolecular smFRET in a viscous glycerol solution. In future experiments, the smFRET 3D tracking system will be used to study protein conformational dynamics while diffusing in solution and native biological environments with high temporal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integration of pH Control into Chi.Bio Reactors and Demonstration with Small-Scale Enzymatic Poly(ethylene terephthalate) Hydrolysis

Small-scale bioreactors that are affordable and accessible would be of major benefit to the research community. In previous work, an open-source, automated bioreactor system was designed to operate up to the 30 mL scale with online optical monitoring, stirring, and temperature control, and this system, dubbed Chi.Bio, is now commercially available at a cost that is typically 1–2 orders of magnitude less than commercial bioreactors. In this work, we further expand the capabilities of the Chi.Bio system by enabling continuous pH monitoring and control through hardware and software modifications. For hardware modifications, we sourced low-cost, commercial pH circuits and made straightforward modifications to the Chi.Bio head plate to enable continuous pH monitoring. For software integration, we introduced closed-loop feedback control of the pH measured inside the Chi.Bio reactors and integrated a pH-control module into the existing Chi.Bio user interface. We demonstrated the utility of pH control through the small-scale depolymerization of the synthetic polyester, poly(ethylene terephthalate) (PET), using a benchmark cutinase enzyme, and compared this to 250 mL bioreactor hydrolysis reactions. The results in terms of PET conversion and rate, measured both by base addition and product release profiles, are statistically equivalent, with the Chi.Bio system allowing for a 20-fold reduction of purified enzyme required relative to the 250 mL bioreactor setup. Through inexpensive modifications, the ability to conduct pH control in Chi.Bio reactors widens the potential slate of biochemical reactions and biological cultivations for study in this system, and may also be adapted for use in other bioreactor platforms.

09 BIOMASS FUELS↗

Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction

High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatomic multicomponent compositions give rise to unique physicochemical characteristics, including lattice distortion, sluggish diffusion, high-entropy stabilization, and pronounced electronic heterogeneity, that collectively generate diverse and synergistic active sites inaccessible in conventional alloys. This review summarizes recent progress in HEA-based bifunctional electrocatalysis, with a focus on the fundamental mechanisms governing HER and ORR activity, stability, and selectivity. We discuss advances in synthesis strategies, ranging from confined growth and step-alloying to scalable continuous-flow methods, that enable precise control over composition, size, and surface structure. Complementary computational and data-driven approaches, including density functional theory, machine-learning-assisted screening, and descriptor development, are highlighted as essential tools for navigating the vast HEA design space and establishing structure−property relationships. Particular attention is paid to adsorption-energy distributions, multisite cooperativity, and environmental effects under realistic electrochemical conditions. Finally, we outline current challenges and future opportunities for integrating mechanistic understanding with AI-guided, closed-loop design frameworks to accelerate the discovery of next-generation HEA bifunctional electrocatalysts for sustainable energy conversion.

Alloys↗

Catalysis as an Enabling Science for Sustainable Polymers

The replacement of current petroleum-based plastics with sustainable alternatives is a crucial but formidable challenge for the modern society. Catalysis presents an enabling tool to facilitate the development of sustainable polymers. This review provides a system-level analysis of sustainable polymers and outlines key criteria with respect to the feedstocks the polymers are derived from, the manner in which the polymers are generated, and the end-of-use options. Specifically, we define sustainable polymers as a class of materials that are derived from renewable feedstocks and exhibit closed-loop life cycles. Among potential candidates, aliphatic polyesters and polycarbonates are promising materials due to their renewable resources and excellent biodegradability. The development of renewable monomers, the versatile synthetic routes to convert these monomers to polyesters and polycarbonate, and the different end-of-use options for these polymers are critically reviewed, with a focus on recent advances in catalytic transformations that lower the technological barriers for developing more sustainable replacements for petroleum-based plastics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ion-Specific Precipitation of Extractants Enables Rare-Earth Separation and Wastewater Remediation from Solvent Extraction of Critical Elements

The increasing demand for rare-earth elements (REEs) necessitates sustainable recovery strategies, particularly from secondary sources, such as electronic waste. Solvent extraction is the primary industrial method for REE separation; however, the unintentional dissolution of extractants into wastewater poses serious environmental risks, leading to organic contamination and process inefficiencies. Existing wastewater treatment methods struggle to remove these persistent pollutants, underscoring the need for innovative recovery approaches. Herein, we present a ligand-mediated precipitation strategy that simultaneously recovers REEs and removes dissolved extractants from solvent extraction wastewater. We show that residual extractants in the aqueous phase can selectively bind REEs, inducing their precipitation while leaving transition metals in solution. By integrating FTIR spectroscopy, EDS, XPS, EXAFS, and SAXS, we elucidate the mechanism of ion-specific precipitation and the local coordination environment of metal ions in the precipitate. Importantly, we demonstrate that the precipitated extractants can be efficiently recovered and reused, providing a closed-loop solution that enhances sustainability. Applying this method to leachates from samarium–cobalt (Sm–Co) and neodymium–iron–boron (NdFeB) mixed magnets, we achieve highly selective REE precipitation under mild conditions, demonstrating a scalable and cost-effective pathway for REE recovery, wastewater purification, and extractant recycling. In conclusion, by integrating element-specific ligand-mediated precipitation with extractant reuse, this work offers a transformative approach to REE separation that reduces the environmental impact while improving resource efficiency.

E-waste↗

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗

Capturing Deuteration Effects in a Molecular Mechanics Force Field: Deuterated THF and the THF–Water Miscibility Gap

Deuteration is a common chemical modification used in conjunction with experiments such as neutron scattering, NMR, and Fourier-transform infrared for the study of molecular systems. Under the Born–Oppenheimer (BO) approximation, while the underlying potential energy surface remains unchanged by isotopic substitutions, isotopic substitution still alters intramolecular vibrations, which in turn may alter intermolecular interactions. Molecular mechanics (MM) force fields used in classical molecular dynamics simulations are assumed to represent local approximations of the BO potential energy surfaces, and hence, MD simulations using simple isotopic mass substitutions should capture BO-compatible isotope effects. However, standard MM force-field parameterizations do not directly fit to the local harmonic quantum mechanical (QM) Hessian that describes the BO surface, but rather to QM normal-modes and/or mass-dependent internal-coordinate derived distortion energies. In this study, using tetrahydrofuran (THF)–water mixtures as our model system, we show that not only does a simple mass-substitution approach fail to capture an experimentally characterized deuteration effect (the loss of the closed-loop miscibility gap associated with the complete deuteration of THF) but also it is necessary to generate new MM force-field parameters that correctly describe isotopic dependent vibrations to capture the experimental deuteration effect. We show that the origin of this failure is a result of using mass-dependent features to fit the THF MM force field, which unintentionally biases the bonded terms of the force field to represent only the isotopologue used during the original force-field parameterization. In addition, we make use of our isotopologue-corrected force field for D 8 THF to examine the molecular origins of the isotope-dependent loss of the THF–water miscibility gap.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗