Search Results (1,284)

The recognition of liquid–liquid phase separation (LLPS) as a widespread organizing principle has revolutionized our view of cellular biochemistry. By forming biomolecular condensates, cells spatially orchestrate reactions without membranes. However, the dysregulation of this precise physical organization is emerging as a driver of diverse pathologies, collectively termed “Condensatopathies.” Unlike traditional proteinopathies defined by static aggregates, these disorders span a dynamic spectrum of material state dysfunctions, from the failure to assemble essential compartments to the formation of aberrant, toxic phases. While research has largely focused on neurodegeneration and cancer, the impact of condensate dysfunction likely extends across broad physiological landscapes. A central unresolved challenge lies in deciphering the “molecular grammar” that governs the transition from functional fluids to pathological solids and, critically, visualizing these transitions in situ. This “material science” perspective presents a profound conundrum for drug discovery: how to target the collective physical state of a protein ensemble rather than a fixed active site. This review navigates the evolving therapeutic horizon, examining the limitations of current pharmacological approaches in addressing the complex “condensatome.” Moving beyond inhibition, we propose that the future of intervention lies in “reverse-engineering” the biophysical codes of phase separation. We discuss how deciphering these principles enables the creation of programmable molecular tools—such as synthetic peptides and state-specific degraders—designed to precisely modulate or dismantle pathological condensates, paving the way for a new era of precision medicine governed by soft matter physics. Full article

Low work function (LWF) materials are essential for enabling efficient systems’ behavior in applications ranging from vacuum electronics to energy conversion devices and next-generation opto-electronic interfaces. Recent advances in theory, characterization, and materials engineering have dramatically expanded the candidates for LWF systems, including alkali-based compounds, perovskites, borides, nitrides, barium and scandium oxides, 2D materials, MXenes, functional polymers, carbon materials, and hybrid architectures. This review provides a comprehensive overview of the fundamental mechanisms governing the work function (WF) and discusses the state-of-the-art measurement techniques, as well as the most used computational approaches for predicting and validating WF values. The recent breakthroughs in engineering LWF surfaces through different methods are discussed. Special emphasis is placed on the relationship between predicted and experimentally measured WF values, highlighting the role of surface contamination, reconstruction, and environmental stability. Performance, advantages, and limitations of major LWF material families are fully analyzed, identifying emerging opportunities for next applications. Finally, current and fundamental challenges in achieving scalable, stable, and reproducible LWF surfaces are considered, presenting promising research directions such as high-throughput computational discovery and in situ surface engineering with protective coatings. This review aims to provide a unified framework for understanding, achieving, and advancing LWF materials toward practical and industrially relevant technologies. Full article

Background: Efficient nucleic acid extraction is essential for reliable viral load testing, yet performance can differ widely depending on the extraction system and sample type. We compared three automated platforms, QIAcube, EZ1 Advanced, and Maxwell RSC, for their ability to recover cytomegalovirus (CMV) DNA and West Nile virus (WNV) RNA from common clinical matrices. Methods: Mock specimens were prepared using whole blood, plasma, serum, and urine collected from two donors. Samples were spiked with CMV or WNV culture material and extracted in triplicate on each platform according to the manufacturers’ protocols. Viral loads were measured using ELITech ELITE MGB assays on the InGenius system. Whole blood samples were also tested after a 1:4 dilution. Matrix-specific standard curves were applied, and viral loads were compared using Wilcoxon rank-sum tests with false-discovery rate adjustment. Results: Extraction efficiency differed substantially by platform and specimen type. For CMV, QIAcube consistently produced the highest DNA recovery across all matrices, with particularly large differences in plasma and serum, where EZ1 and Maxwell RSC yielded significantly lower loads. The WNV results varied by matrix: EZ1 and QIAcube performed similarly in plasma, while Maxwell RSC achieved the highest RNA recovery in whole blood. While the QIAcube exhibited reduced WNV recovery in blood, it achieved the best performance in serum, as specified by the kit. No significant platform differences were observed for urine. Diluting whole blood reduced variability between platforms. Conclusions: Both sample matrix and extraction system strongly influence nucleic acid recovery. These results highlight the need for matrix-specific validation and standardized extraction approaches in molecular diagnostics. Full article

Magnesium–sulfur (Mg-S) batteries represent a novel category of multivalent energy storage systems, characterised by enhanced theoretical energy density, material availability, and ecological compatibility. Notwithstanding these benefits, the practical implementation of this approach continues to be hindered by ongoing issues, such as polysulfide shuttle effects, slow Mg²⁺ transport, and significant interfacial instability. This study emphasises recent progress in utilising transition metal chalcogenides (TMCs) as cathode materials and modifiers to overcome these challenges. We assess the structural, electrical, and catalytic characteristics of TMCs such as MoS₂, CoSe₂, WS₂, and TiS₂, highlighting their contributions to improving redox kinetics, retaining polysulfides, and enabling reversible Mg²⁺ intercalation. The review synthesises results from experimental and theoretical studies to offer a thorough comprehension of structure–function interactions. Particular emphasis is placed on morphological engineering, modulation of electronic conductivity, and techniques for surface functionalisation. Furthermore, we examine insights from density functional theory (DFT) simulations that corroborate the observed enhancements in electrochemical performance and offer predictive direction for material optimisation. This paper delineates nascent opportunities in Artificial Intelligence (AI)-enhanced materials discovery and hybrid system design, proposing future trajectories to realise the potential of TMC-based Mg-S battery systems fully. Full article

High variability in multi-product manufacturing environments and rapidly changing customer demands make decentralized coordination of work-in-process (WIP) and production resources increasingly important. However, the intrinsic rigidity of conventional centralized and monolithic manufacturing execution systems (MESs) renders them unsuitable for such highly dynamic environments. To address this limitation, this study proposes an agent-based distributed, decentralized MES architecture. The manufacturing execution process is realized through collaboration among constituent agents based on an employment network (EmNet). Specifically, three types of agents are introduced: WIPAgents (representing WIPs), PAgents (representing processing resources), and MHAgents (representing material-handling resources). Collaboration among agents (e.g., collaborator discovery, partner selection, and data sharing/exchange) is facilitated by a data-space-based collaboration platform which was introduced in our prior work. To validate the proposed architecture, we built a digital-twin-based simulation testbed and conducted simulation experiments. The experimental results confirm the validity and operational feasibility of the proposed architecture. Full article

This study introduces an optimized and selective extraction methodology using dichloromethane/methanol (DCM/MeOH, 95:5, v/v) in combination with accelerated solvent extraction (ASE) for the targeted stilbenoid and phenanthrene derivatives from five orchid species: Cattleya nobilior (root), Cymbidium defoliatum (root and bulb), Dendrobium phalaenopsis (stem), Encyclia linearifolioides (leaf), and Phalaenopsis aphrodite (root). Sequential extraction was performed with hexane, followed by DCM/MeOH (95:5 and 1:1, v/v) under controlled temperatures (70 °C for hexane, 100 °C for DCM/MeOH), using three static cycles per stage. Chemical profiling by high-performance liquid chromatography with a diode-array-detector and tandem mass spectrometry (HPLC-DAD-MS/MS) enabled the identification of twenty specialized metabolites—seven stilbenoids and thirteen phenanthrenes—several reported here for the first time, including crepidatuol B, dendrosinen D, and coeloginanthridin. The analytical method showed excellent separation of structurally related phenolic compounds, demonstrating the efficiency of the extraction protocol and the selectivity of the solvent system. Many of the identification metabolites are known for cytotoxic, antioxidant, anti-inflammatory, and metabolic regulatory properties, while newly detected compounds remain unexplored and present promising candidates for future biological evaluation. The broad distribution of these metabolites across the studied orchids enhances the current understanding of their phytochemical diversity and suggests chemotaxonomic relevance within the Orchidaceae family. Importantly, the extraction strategy requires minimal plant material, offering ecological advantages when working with rare or endangered species. Overall, this environmentally conscious extraction approach provides a robust platform for metabolic discovery and supports future research in natural products chemistry, plant ecology, drug discovery, structure–activity relationships studies and biotechnological applications. Full article

Magnetooptics (MO) explores light—matter interactions in magnetized media and has advanced rapidly with progress in materials science, spectroscopy, and integrated photonics. This review highlights recent developments in fundamental principles, experimental techniques, and emerging applications. We revisit the canonical MO effects: Faraday, MO Kerr effect (MOKE), Voigt, Cotton—Mouton, Zeeman, and Magnetic Circular Dichroism (MCD), which underpin technologies ranging from optical isolators and high-resolution sensors to advanced spectroscopic and imaging systems. Ultrafast spectroscopy, particularly time-resolved MOKE, enables femtosecond-scale studies of spin dynamics and nonequilibrium processes. Hybrid magnetoplasmonic platforms that couple plasmonic resonances with MO activity offer enhanced sensitivity for environmental and biomedical sensing, while all-dielectric magnetooptical metasurfaces provide low-loss, high-efficiency alternatives. Maxwell-based modeling with permittivity tensor ($\epsilon$) and machine-learning approaches are accelerating materials discovery, inverse design, and performance optimization. Benchmark sensitivities and detection limits for surface plasmon resonance, SPR and MOSPR systems are summarized to provide quantitative context. Finally, we address key challenges in material quality, thermal stability, modeling, and fabrication. Overall, magnetooptics is evolving from fundamental science into diverse and expanding technologies with applications that extend far beyond current domains. Full article

This paper advances a unified theoretical framework that synthesises Shane and Eckhardt’s individual–opportunity nexus, Ramoglou and Tsang’s opportunities-as-propensities perspective, and Davidsson’s tripartite model of new venture ideas, external enablers, and opportunity confidence. Building on these foundations, the paper develops an entrepreneurial entanglement model that explains how opportunities emerge as probabilistic propensities within dynamic configurations of agents, artefacts, distributed agencies, and spatiotemporal conditions. The model clarifies how material artefacts, socio-cognitive processes, and environmental shifts jointly shape the emergence, visibility, and realisation of entrepreneurial possibilities. By situating opportunity formation within an entangled field—rather than within isolated acts of discovery or creation—the framework deepens understanding of how entrepreneurial actions give rise to potentialities and how these potentialities become actualised under conditions of uncertainty. The analysis contributes to both theory and practice by offering a relational, mechanism-based account of how entrepreneurial behaviour and environmental factors intersect to structure the formation and realisation of opportunities. Full article

Temperature-responsive hydrogels are sophisticated stimuli-responsive biomaterials that undergo rapid, reversible sol–gel phase transitions in response to subtle thermal stimuli, most notably around physiological temperature. This inherent thermosensitivity enables non-invasive, precise spatiotemporal control of material properties and bioactive payload release, rendering them highly promising for advanced biomedical applications. This review critically surveys recent advances in the design, synthesis, and translational potential of thermo-responsive hydrogels, emphasizing nanoscale and hybrid architectures optimized for superior tunability and biological performance. Foundational systems remain dominated by poly(N-isopropylacrylamide) (PNIPAAm), which exhibits a sharp lower critical solution temperature near 32 °C, alongside Pluronic/Poloxamer triblock copolymers and thermosensitive cellulose derivatives. Contemporary developments increasingly exploit biohybrid and nanocomposite strategies that incorporate natural polymers such as chitosan, gelatin, or hyaluronic acid with synthetic thermo-responsive segments, yielding materials with markedly enhanced mechanical robustness, biocompatibility, and physiologically relevant transition behavior. Cross-linking methodologies—encompassing covalent chemical approaches, dynamic physical interactions, and radiation-induced polymerization are rigorously assessed for their effects on network topology, swelling/deswelling kinetics, pore structure, and degradation characteristics. Prominent applications include on-demand drug and gene delivery, injectable in situ gelling systems, three-dimensional matrices for cell encapsulation and organoid culture, tissue engineering scaffolds, self-healing wound dressings, and responsive biosensing platforms. The integration of multi-stimuli orthogonality, nanotechnology, and artificial intelligence-guided materials discovery is anticipated to deliver fully programmable, patient-specific hydrogels, establishing them as pivotal enabling technologies in precision and regenerative medicine. Full article

Organ-on-a-chip (OoC) or tissue-on-a-chip (ToC) technologies represent a significant advancement in enabling modeling of human organ and tissue physiology for medical study, although further development is required for these technologies to reach widespread adoption. OoC/ToC are three-dimensional (3D) microfluidic platforms that overcome limitations of traditional two-dimensional (2D) cell culture or animal models, providing an alternative environment for disease study, drug interactions, and tissue regeneration. The design of these systems is complex, requiring advanced fabrication techniques and careful selection of biomaterials with consideration of material toxicity, optical clarity, stability, and flexibility. A key innovation in this field is the multi-organ-on-a-chip (MOC) technology, which links multiple organ systems on a single platform. This enables the study of systemic diseases and the complex communication between organs, which is not possible with single-organ models. Furthermore, OoC/ToC technology holds immense potential for personalized medicine. By using patient-specific cells, these devices can create disease models that reflect an individual’s unique genetic and phenotypic variations, paving the way for tailored therapeutic interventions. The integration of real-time sensors within these devices also facilitates high-throughput screening and accelerates drug discovery. While the development and optimization of these systems is still in its early stages, OoC/ToC technologies have already demonstrated promise in a number of translational research applications. Full article

The Variational Quantum Eigensolver (VQE) is a quantum algorithm for estimating ground-state energies, with promising applications in material science, drug discovery, and battery research. A key challenge is the limited number of qubits available on current quantum devices, which restricts the size of molecular systems that can be studied. To address this limitation, we apply the Fragment Molecular Orbital (FMO) method in combination with VQE, referred to as FMO-VQE. This approach divides a system into smaller fragments, making the quantum calculations more tractable. While earlier studies demonstrated this method only for hydrogen clusters, we extend the application to lithium cobalt oxide, a widely used cathode material in lithium-ion batteries. Using FMO-VQE, we estimate the ground-state energy of this complex system while reducing the number of required qubits from 24 to 14, without significant loss of accuracy compared to classical methods. This reduction highlights the potential of FMO-VQE to overcome hardware limitations and make quantum simulations of larger molecules feasible. The results suggest a practical path for applying near-term quantum computers to real-world challenges, opening opportunities for advancements in the battery industry and drug design. Full article

Shape memory polymers (SMPs) represent an innovative class of materials that possess programmed, reversible shape-changing capabilities in response to external stimuli. The recent emergence of SMPs’ advanced manufacturing, specifically 4D printing, has created exceptional opportunities for use in biomedical engineering. This review presents a critical synthesis of the latest advances in the chemistry, biomedical applications, manufacturing strategies, and clinical translation of SMPs, highlighting vat photopolymerization techniques, such as stereolithography (SLA) and digital light processing (DLP). Notably, 4D-printed SMPs can promote spatiotemporally controlled architectures, and applications include minimally invasive implants, dynamic tissue scaffolds, and multifunctional drug delivery. This paper focuses on recent advances in resin design, multi-responsive and nanocomposite resins, AI-guided material discovery, and emerging biocompatible and biodegradable formulations, while outlining current roadblocks to clinical implementation, including cytotoxicity, sterilization, regulatory compliance, and device shelf-life. Our goal is to elucidate the relationship between material design, processing, and biomedical performance to inform researchers of potential future directions for 4D-printed SMPs and next-generation, patient-centered medical devices. Full article

Polymeric coatings play a pivotal role in enhancing the durability, functionality, and sustainability of structural materials exposed to harsh environmental conditions. Recent advances in artificial intelligence (AI) have transformed the development, optimization, and evaluation of these coatings by enabling data-driven material discovery, predictive performance modeling, and autonomous inspection. This review aims to provide a comprehensive overview on AI-driven polymeric coating strategies for structural applications, emphasizing the integration of machine learning, computer vision, and multi-physics simulations into traditional materials engineering frameworks. The discussion encompasses AI-assisted material selection methods for polymers, fillers, and surface modifiers; predictive models for corrosion, fatigue, and degradation; and intelligent evaluation systems using digital imaging, sensor fusion, and data analytics. Case studies highlight emerging trends such as self-healing, smart, and sustainable coatings that leverage AI to balance mechanical performance, environmental resistance, and carbon footprint. The review concludes with identifying current challenges—including data scarcity, model interpretability, and cross-domain integration—and proposes future research directions toward explainable, autonomous, and circular coating design pipelines. Full article

In the present study, molecular dynamics simulations with three interatomic potentials (Polymer Consistent Force Field, Adaptive Intermolecular Reactive Empirical Bond Order, and Tersoff) are employed to investigate strain-dependent interfacial thermal resistance across one-dimensional to two-dimensional junctions. Carbon nanotube–graphene junctions exhibit exceptionally low interfacial resistances (1.69–2.37 × 10⁻¹⁰ K·m²/W at 300 K)—two to three orders of magnitude lower than conventional metal–dielectric interfaces. Strain-dependent behavior is highly potential-dependent, with different potentials showing inverse, positive, or minimal strain sensitivity. Local phonon density of states analysis with Tersoff reveals that strain-induced spectral redistribution in graphene toward lower frequencies enhances phonon coupling with carbon nanotube modes. Temperature significantly affects resistance, with 37–59% increases at 10 K compared to 300 K due to long-wavelength phonon scattering. Boron nitride nanotube–hexagonal boron nitride nanosheet junctions exhibit 60% higher resistance (3.2 × 10⁻¹⁰ K·m²/W) with temperature-dependent strain behavior and spacing-insensitive performance. Interfacial resistance is independent of pillar height, confirming junction-dominated transport. The discovery of exceptionally low interfacial resistances and material-specific strain responses enables the engineering of thermally switchable devices and mechanically robust thermal pathways. These findings directly address critical challenges in next-generation flexible electronics where devices must simultaneously manage high heat fluxes while maintaining thermal performance under repeated mechanical deformation. Full article

Measuring spin polarization (P) of materials is essential for understanding their fundamental properties and for their application in spintronics. Point contact Andreev reflection (PCAR) spectroscopy is a straightforward yet powerful technique for measuring P. However, conventional analysis methods depend on iterative fitting procedures that are time-consuming, subjective, and often lead to non-unique solutions. This complexity arises from the interplay of multiple physical parameters with pressure, including temperature, superconducting gap, and interfacial barrier strength. Here, we present a machine learning (ML) approach that utilizes convolutional neural networks (CNNs) to facilitate the rapid and automated extraction of P from PCAR spectra. We validate the ML model by analyzing experimental PCAR spectra from various materials reported in the literature. The predicted parameters by the CNN model show excellent agreement with the literature values, demonstrating its robust performance across a wide range of materials and parameter sets. This approach significantly reduces analysis time while maintaining accuracy, providing a practical tool for material characterization, thus accelerating materials discovery for spintronics. Full article