Taking dynamic covalent chemistry out of the lab and into reprocessable industrial thermosets
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A combination of measures limits demand for critical materials in Sweden’s electric car transition
Electrification of passenger cars will result in an increased demand for critical raw materials. Here we estimate the quantities of nickel, manganese, cobalt, lithium, and graphite that could be required for a transition to electric cars in Sweden and how different measures can limit material demand. We find notable reduction potentials for shorter battery range—enabled by improved charging infrastructure, increased vehicle energy efficiency, and reduced travel demand compared to a reference scenario. The reduction potentials for downsizing and more lightweight cars, and car sharing are more modest. The combined impact of these measures would be 50–75% reduction in cumulative demand and 72–87% reduction in in-use stock in 2050, depending on the material and battery chemistry pathway. Generally, the reduction potentials are larger than the potential contributions from recycling, suggesting that these complementary measures may be more effective in reducing material demand.
Categorizing robots by performance fitness into the tree of robots
Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.
The evolution of lithium-ion battery recycling
Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which will require recovering materials from spent LIBs and reintegrating them into new batteries. In this Review, we outline the current state of LIB recycling, evaluating industrial and developing technologies. Among industrial technologies, pyrometallurgy can be broadly applied to diverse electrode materials but requires operating temperatures of over 1,000 °C and therefore has high energy consumption. Hydrometallurgy can be performed at temperatures below 200 °C and has material recovery rates of up to 93% for lithium, nickel and cobalt, but it produces large amounts of wastewater. Developing technologies such as direct recycling and upcycling aim to increase the efficiency of LIB recycling and rely on improved pretreatment processes with automated disassembly and cleaner mechanical separation. Additionally, the range of materials recovered from spent LIBs is expanding from the cathode materials recycled with established methods to include anode materials, electrolytes, binders, separators and current collectors. Achieving an efficient recycling ecosystem will require collaboration between recyclers, battery manufacturers and electric vehicle manufacturers to aid the design and automation of battery disassembly lines.
Dynamic optimizers for complex industrial systems via direct data-driven synthesis
The chemical process industry (CPI) faces significant challenges in improving sustainability and efficiency while maintaining conservative principles for managing cost, complexity, and uncertainty. This work introduces a data-driven approach to dynamic real-time optimization (D-RTO) that addresses the aforementioned concerns by directly extracting process optimization policies from historical plant data. Our method constructs a value function to evaluate trajectory quality and employs weighted regression to derive improved policies. When applied to a plant-wide industrial process control problem, the proposed optimizer demonstrates superior performance in adapting to disturbances while maintaining stability and product quality. These results challenge conventional assumptions regarding the potential of data-driven optimization in the CPI. Although limitations exist due to the black-box nature of neural networks, this study presents a promising avenue for enhancing operational efficiency in industrial settings. The proposed approach offers a practical solution for process optimization, as it leverages readily available historical data and does not require extensive modeling efforts. By demonstrating significant efficiency improvement on a realistic industrial benchmark problem, this work paves the way for the adoption of data-driven optimization techniques in real-world CPI applications.
A microscale thermophoresis-based enzymatic RNA methyltransferase assay enables the discovery of DNMT2 inhibitors
RNA methyltransferases (MTases) have recently become increasingly important in drug discovery. Yet, most frequently utilized RNA MTase assays are limited in their throughput and hamper this rapidly evolving field of medicinal chemistry. This study developed a microscale thermophoresis (MST)-based split aptamer assay for enzymatic MTase investigations, improving current methodologies by offering a non-proprietary, cost-effective, and highly sensitive approach. Our findings demonstrate the assay’s effectiveness across different RNA MTases, including inhibitor characterization of METTL3/14, DNMT2, NSUN2, and S. aureus TrmD, enabling future drug discovery efforts. Using this concept, a pilot screening on the cancer drug target DNMT2 discovered several hit compounds with micromolar potency.
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