4172 papers
Condensed matter physics and materials science form a dynamic partnership, exploring how the collective behavior of atoms gives rise to the unique properties of solids and liquids. This field bridges the gap between fundamental quantum mechanics and the practical engineering of everything from flexible electronics to superconductors, turning abstract theories into tangible innovations that shape our daily lives.
At Gist.Science, we process every new preprint in this category directly from arXiv to make these complex discoveries accessible to everyone. Our team generates both plain-language overviews and detailed technical summaries for each paper, ensuring that researchers, students, and curious minds alike can grasp the latest breakthroughs without getting lost in dense jargon.
Below are the latest papers in condensed matter and materials science, organized by their most recent publication dates.
This paper introduces an enhanced non-contact steady-state temperature differential radiometry (SSTDR) platform that successfully measures the thermal conductivity and spectral emittance of molybdenum up to 3000 K with 7.9–11% uncertainty, effectively addressing critical data gaps for ultrahigh-temperature applications.
This first-principles study investigates how sodium intercalation into layered potassium birnessite alters its structural stability, ion diffusion barriers, vibrational modes, and electronic properties, revealing that the process induces significant lattice distortions and transforms the material into a tunable bipolar magnetic semiconductor with potential applications in energy storage and spintronics.
This review surveys the vapor-liquid-solid (VLS) synthesis of non-conventional nanowires (such as oxides, carbides, and chalcogenides) by comparing them to established group IV and III-V systems, analyzing the mechanistic factors hindering deterministic control, and outlining challenges and opportunities across precursor delivery, seed formation, and growth stages to guide future advancements in one-dimensional nanomaterials.
This paper provides a concise overview of strategies to enhance the thermoelectric efficiency of SnTe in the mid-temperature range by discussing synthesis methods, band structure engineering to improve the power factor, and nanostructuring to reduce thermal conductivity.
This study proposes a fully coupled chemo-mechanical phase-field framework demonstrating that mineral dissolution in acidic environments enlarges the fracture process zone and blunts crack tips, thereby inducing a transition from brittle to ductile failure modes governed by the competing timescales of chemical degradation and mechanical deformation.
This study demonstrates that solid-state high-harmonic generation in the superatomic semiconductor Re6Se8Cl2 is profoundly sensitive to thermal lattice fluctuations, which suppress coherent harmonic emission through electronic dephasing and phase dispersion, thereby establishing a new method for probing ultrafast lattice dynamics.
This review critically analyzes the reemergence of selenium solar cells, which have surpassed 10% efficiency, by examining their material properties, carrier dynamics, and processing strategies to address persistent voltage deficits and outline a roadmap for future high-efficiency development.
This study utilizes density-functional theory and Monte Carlo simulations to demonstrate that while grain boundaries in bcc iron induce local antiferromagnetic coupling and are modulated by phosphorus segregation, realistic boundary densities only minimally reduce the material's global Curie temperature due to the dominance of bulk-like regions.
By combining deep potential molecular dynamics with high-fidelity electronic-structure data, this study resolves decades of uncertainty to precisely locate aluminum's liquid-vapor critical point at approximately 6531–6576 K, 0.637 g/cm³, and 1.6 kbar, establishing a transferable framework for predicting critical phenomena in metals under extreme conditions.
ReadMOF introduces a novel, structure-free machine learning framework that leverages pretrained language models to convert systematic MOF nomenclature into semantic embeddings, enabling accurate property prediction and chemical reasoning without relying on atomic coordinates or connectivity graphs.