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Exploring effects of platelet contractility on the kinetics, thermodynamics, and mechanisms of fibrin clot contraction

Mechanisms of blood clot contraction – platelet-driven fibrin network remodeling, are not fully understood. We developed a detailed computational ClotDynaMo model of fibrin network with activated platelets, whose clot contraction rate for normal 450,000/µl human platelets depends on serum viscosity η, platelet filopodia length l, and weakly depends on filopodia traction force f and filopodia extension-retraction speed v. Final clot volume is independent of η, but depends on v, f and l. Analysis of ClotDynaMo output revealed a 2.24 TJ/mol clot contraction free energy change, with ~67% entropy and ~33% internal energy changes. The results illuminate the “optimal contraction principle” that maximizes volume change while minimizing energy cost. An 8-chain continuum model of polymer elasticity containing platelet forces, captures clot contractility as a function of platelet count, η and l. The ClotDynaMo and continuum models can be extended to include red blood cells, variable platelet properties, and mechanics of fibrin network.

First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes

MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.

Rapid brain tumor classification from sparse epigenomic data

Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.

A comprehensive review of KCC-1 fibrous silica for water treatment

The growing global demand for freshwater necessitates advanced water treatment technologies. This review highlights the application of fibrous silica spheres, KCC-1, in water remediation, focusing on the removal of heavy metals and organic dyes. KCC-1’s unique fibrous morphology, high surface area, and physicochemical properties make it a promising adsorbent. This work examines its synthesis, modifications, and advantages, providing insights into optimizing KCC-1-based adsorbents for sustainable water treatment.

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.

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