A new tool for shape and structure optimization of soft materials
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Bicomponent nano- and microfiber aerogels for effective management of junctional hemorrhage
Managing junctional hemorrhage is challenging due to ineffective existing techniques, with the groin being the most common site, accounting for approximately 19.2% of potentially survivable field deaths. Here, we report a bicomponent nano- and microfiber aerogel (NMA) for injection into deep, narrow junctional wounds to effectively halt bleeding. The aerogel comprises intertwined poly(lactic acid) nanofibers and poly(ε-caprolactone) microfibers, with mechanical properties tunable through crosslinking. Optimized aerogels demonstrate improved resilience, toughness, and elasticity, enabling rapid re-expansion upon blood contact. They demonstrate superior blood absorption and clotting efficacy compared to commercial products (i.e., QuikClot® Combat Gauze and XStat®). Most importantly, in a lethal swine junctional wound model (Yorkshire swine, both male and female, n = 5), aerogel treatment achieved immediate hemostasis, a 100% survival rate, no rebleeding, hemodynamic stability, and stable coagulation, hematologic, and arterial blood gas testing.
3D printing of micro-nano devices and their applications
In recent years, the utilization of 3D printing technology in micro and nano device manufacturing has garnered significant attention. Advancements in 3D printing have enabled achieving sub-micron level precision. Unlike conventional micro-machining techniques, 3D printing offers versatility in material selection, such as polymers. 3D printing technology has been gradually applied to the general field of microelectronic devices such as sensors, actuators and flexible electronics due to its adaptability and efficacy in microgeometric design and manufacturing processes. Furthermore, 3D printing technology has also been instrumental in the fabrication of microfluidic devices, both through direct and indirect processes. This paper provides an overview of the evolving landscape of 3D printing technology, delineating the essential materials and processes involved in fabricating microelectronic and microfluidic devices in recent times. Additionally, it synthesizes the diverse applications of these technologies across different domains.
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.
Influenza A virus rapidly adapts particle shape to environmental pressures
Enveloped viruses such as influenza A virus (IAV) often produce a mixture of virion shapes, ranging from 100 nm spheres to micron-long filaments. Spherical virions use fewer resources, while filamentous virions resist cell-entry pressures such as antibodies. While shape changes are believed to require genetic adaptation, the mechanisms of how viral mutations alter shape remain unclear. Here we find that IAV dynamically adjusts its shape distribution in response to environmental pressures. We developed a quantitative flow virometry assay to measure the shape of viral particles under various infection conditions (such as multiplicity, replication inhibition and antibody treatment) while using different combinations of IAV strains and cell lines. We show that IAV rapidly tunes its shape distribution towards spheres under optimal conditions but favours filaments under attenuation. Our work demonstrates that this phenotypic flexibility allows IAV to rapidly respond to environmental pressures in a way that provides dynamic adaptation potential in changing surroundings.
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