Intensely applied strain from wrinkled architecture mitigates intrinsic limitations of 2D materials while enhancing their capabilities through spatially modulated electronic and catalytic properties. Here, this study introduces a deterministic wafer-scale fabrication strategy that enables densely distributed ultrafine wrinkled architectures in atomically thin molybdenum disulfide (MoS2) crystals, achieving tensile strains up to 3.29% over 50% of the scan area. The wrinkle structures are obtained by modulating the parameters of a wet transfer method, including transfer liquid media, thermal energy, and polystyrene (PS) solution concentrations. Collectively, these results demonstrate a practical route to wafer-scale fabrication of ultrafine wrinkle structures. The wrinkled MoS2 (w-MoS2) exhibits optimal multifunctional device performance in electronics and as a hydrogen evolution catalyst. In a hydrogen evolution reaction (HER), the lowest Tafel slope (52.3 mV dec-1) is observed, comparable to that of metallic TMDC catalysts. Furthermore, our w-MoS2 memory device displays an on/off ratio of 5 × 107 with a large memory window corresponding to 65% of the total gate voltage (VGS) sweep range. This simple and innovative morphology engineering offers a viable and reproducible pathway toward high-performance electronic and catalytic functionalities in highly strained 2D materials.

Two-dimensional (2D) semiconductors are poised to extend logic technology to the atomic-thickness limit, yet monolayer complementary metal-oxide-semiconductor (CMOS) has been largely hampered by polarity-dependent, thermionic-emission-dominated contacts that preclude a unified injection solution. Here, we introduce degenerately doped, crystalline SnSe2 as a universal source-side van der Waals (vdW) injector capable of enforcing all-tunneling carrier injection into both p- and n-type monolayer channels. The combination of a large electron affinity (∼5.1 eV), degenerate carrier density (>1019 cm-3), and atomically uniform vdW interfacial coupling enables polarity-tailored tunneling mechanisms while effectively suppressing interfacial gap states. In p-type WSe2, a type-III (broken gap) alignment drives efficient band-to-band tunneling, yielding a >1000-fold enhancement in drive current over conventional metal electrodes. In n-type MoS2, the SnSe2 injector forms a type-I heterojunction within the sub-depletion-width monolayer body, enabling a gate-tunable injection that evolves from field-controlled Fowler-Nordheim-like tunneling to thickness-limited tunneling, achieving an on/off ratio > 109 and a subthreshold swing below 70 mV dec-1. Integrating this single-material injector, we demonstrate a monolayer CMOS inverter with a maximum voltage gain of ∼340 at VDD = 2 V, establishing degenerate SnSe2 as a dual-polarity vdW injector and providing a platform for high-performance, low-power 2D integrated circuits.

The initial oxidation stage of high-entropy carbides (HECs) is a decisive yet poorly understood "black box" that governs their ultimate performance for ultra-high-temperature applications. Here, we unveil a powerful strategy-predesigning carbon vacancies-to fundamentally enhance HECs oxidation resistance. By integrating in situ x-ray diffraction (XRD) and in situ transmission electron microscopy coupled with electron energy loss spectroscopy (TEM-EELS), we provide the first direct, atomic-scale visualization of oxygen atoms preferentially occupying these predesigned vacancies during the incipient oxidation stage, leading to the dynamic formation of a metastable metal-carbon-oxygen (M-C-O) interfacial layer. Our combined experimental and theoretical analyses reveal a dual enhancement mechanism: kinetically, M-C-O suppresses both oxygen adsorption on the (111) surface and its subsequent inward diffusion; thermodynamically, they stabilize the HEC lattice at elevated temperatures, thereby increasing the energy barrier for M─C bond cleavage. This work not only deciphers the atomistic origin of enhanced oxidation resistance but also establishes a simple, general, and efficient design principle for next-generation ultra-high-temperature ceramics.

Developing two-dimensional photocatalysts with suitable band structures, efficient carrier transport, and favorable surface reaction activity is crucial for solar-driven water splitting. In this work, the photocatalytic performance of monolayer CdSnP2S6 was systematically investigated using first-principles calculations. The results show that CdSnP2S6 possesses good structural, thermal, dynamical, and mechanical stability. Electronic structure calculations reveal that monolayer CdSnP2S6 is an indirect semiconductor with suitable band-edge positions for overall water splitting. The pH-dependent band alignment further confirms that its conduction and valence band edges can straddle the water redox potentials over a broad pH range (0-14), indicating favorable thermodynamic feasibility under different reaction conditions. Optical absorption analysis shows that CdSnP2S6 exhibits strong light response in the visible-to-ultraviolet region, with a maximum absorption coefficient of about 5.20 × 105 cm-1. Surface reaction analysis suggests that S atoms serve as the main active sites for hydrogen evolution, and the hydrogen adsorption free energy can be significantly optimized under the photogenerated electric potential. Moreover, CdSnP2S6 exhibits a solar-to-hydrogen efficiency of 11.56%, exceeding the 10% benchmark for practical solar hydrogen production. These findings demonstrate that monolayer CdSnP2S6 is a promising two-dimensional photocatalyst for efficient overall water splitting.

Meniscus biomechanical properties are dependent on the microscale composition and distribution of structural proteins and other macromolecules. In this context, the local mechanical properties (material properties) of the meniscus largely depend on the chemical composition. However, to date, a direct link between the spatial biomechanical and biochemical footprint of the meniscus at the microscale is lacking. To this end, we show a multimodal imaging approach for fresh frozen tissue linking the biomechanical and biochemical readouts from atomic force microscopy (AFM) micro-indentation and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI), respectively. We consecutively applied both imaging modalities on the same tissue section, enabling direct correlative comparison across nano- to micrometer length scales. Using human meniscus cryosections, we provide spatial distribution information of collagen and other proteins in three zones of the meniscus section exhibiting different apparent elasticity. This methodology shows promise in providing a better understanding of the relationship between local meniscus composition and micromechanics. In addition, the technique has potential for improved detection of meniscus degeneration biomarkers and, in the future, linking those to clinical imaging readouts.

The use of the complementarity of the contrast between X-rays and neutrons for computed tomography is a well-established technique. X-rays are strongly attenuated by dense, high-atomic-number materials, while neutrons are particularly sensitive to light elements, such as hydrogen or lithium. Combining the information of both modalities into a two-dimensional histogram allows for the discrimination of material phases that would otherwise not be distinguishable with a single technique alone. Here we present DIANA (Dual-mode Imaging Analysis), an open-source Python package that implements a complete dual-modality CT simulation pipeline. The current version of the software builds a voxelised phantom, creates the radiographic projections for polychromatic X-rays and monochromatic neutrons, performs controlled artifact injection and reconstruction, and finally handles the visualisation of the 2D histogram and its analysis with both shape and quality metrics. To show the capabilities of DIANA, we demonstrate the application on a simple composite phantom and a clean, high resolution study of a realistic lithium-ion cell. The architecture of the package is extensible toward other modalities, such as polarised neutrons, phase-contrast X-rays or energy-resolved neutrons, among others. The package is hosted on Github under an MIT licence.

Seawater electrolyte-based zinc-air batteries (SZABs) offer advantages such as high theoretical energy density, safety, and environmental friendliness. However, their commercial application is limited by sluggish oxygen reduction reaction (ORR) kinetics and Cl- adsorption-induced corrosion of the catalysts in seawater. Herein, we propose the construction of a typical p-d orbital hybridization structure between p-block and d-block metal atoms, which enables the simultaneous enhancement of ORR kinetics and inhibition of Cl--induced poisoning of the active sites. First, Fe-based p-d orbital hybrid catalysts are screened via theoretical calculations, and FeSn dual single-atom catalysts (FeSn-DSACs) are identified as the optimal choice, attributed to their ideal adsorption free energy profiles for both *OH and Cl-. The resultant FeSn-DSACs demonstrate desirable ORR performance in an alkaline seawater. When assembled into SZABs, they deliver a peak power density of 255.6 mW cm-2 and exhibit stable operation for over 500 h. Theoretical calculations and in-situ characterizations suggest that p-d hybridization in the FeSn-DSACs weakens the strong adsorption energy of traditional Fe sites towards *OH intermediates, while the high electron cloud density of Sn sites in-situ constructs a locally negatively charged interface to inhibit the adsorption and corrosion of Cl-, collectively significantly enhancing the seawater ORR catalytic activity and stability.

Conventional periodontitis treatments are constrained by challenges such as antibiotic resistance and inadequate tissue regeneration. Herein, we rationally engineer a robust Fe/Mn bimetallic nanozyme (Fe/Mn-Bzyme) derived from a zeolitic imidazolate framework-8 precursor, featuring atomically asymmetric Fe-Mn dual-atom active sites with the Mn1-Fe1-O1-Mn1-N3 coordination environment meticulously embedded within a nitrogen-doped carbon matrix. This tailored structure endows the nanozyme with peroxidase-like (POD) activity, which is synergistically amplified by robust near-infrared (NIR) photothermal conversion. The NIR-driven localized heating dramatically accelerates catalytic kinetics, orchestrating a tunable burst of hydroxyl radical (•OH) generation from endogenous H2O2, which confers robust antibacterial action against Porphyromonas gingivalis (99.47% ± 0.23%) and methicillin-resistant Staphylococcus aureus (99.52% ± 0.27%). Additionally, Fe/Mn-Bzyme demonstrates proactive bioactivity, promoting osteogenic differentiation of pre-osteoblasts and fostering a pro-angiogenic microenvironment. In a rat periodontitis model, a topical Fe/Mn-Bzyme ointment effectively eliminated infections, suppressed pro-inflammatory cytokine expression, mitigated systemic inflammation, and inhibited osteoclast activity, thereby creating a pro‑regenerative microenvironment conducive to subsequent periodontal tissue healing. Furthermore, its robust catalytic activity enabled superior, enamel-preserving tooth whitening compared to conventional H2O2. This work presents a versatile nanozyme platform that integrates synergistic antibacterial, anti-inflammatory, and pro-regenerative functions, offering a powerful strategy for managing complex oral diseases. STATEMENT OF SIGNIFICANCE: The significance of this work lies in the rational design of an atomically dispersed bimetallic nanozyme that harnesses synergistic photothermal-catalytic effects for integrated oral disease management. The Fe/Mn-Bzyme outperforms conventional single-function therapies, offering a powerful and translatable strategy that simultaneously addresses infection, inflammation, tissue destruction, and aesthetic concerns.

The physicochemical properties of highly diluted homeopathic preparations remain insufficiently characterized. This study investigated particulate features of Kali carbonicum (K2CO3) at 50-millesimal potencies (LM4-LM7, ~1:50,000 dilutions per step) and explored plasma proteomic changes in a placebo-controlled N-of-1 trial. Scanning electron microscopy showed larger particle size in Kali carbonicum (67.3 nm) than in the lactose control (47.5 nm) at LM4 in a descriptive comparison. Dynamic light scattering showed no significant differences in size, polydispersity, or zeta potential among Kali carbonicum, lactose control, and solvent blank, accounting for vial-level clustering. Atomic force microscopy showed more compact dendritic assemblies in Kali than in lactose controls, suggesting trituration influences self-organization. Raman spectroscopy of LM7 detected carbonate-associated bands absent in controls. Plasma proteomics identified six FDR-significant proteins during Kali exposure, including increased S100A9, with exploratory enrichment for inflammation, cytoskeletal, and motility terms. These findings are exploratory and do not imply causality.

Solar-driven photocatalytic CO2 reduction converts greenhouse gas into carbonaceous fuels and chemicals, offering a green route to address fossil energy shortages and advance carbon neutrality. However, the field faces core challenges of narrow visible-light response, low quantum efficiency, and poor product selectivity. The fundamental bottleneck lies in ambiguous multi-interface coupled reaction mechanisms. Conventional offline static characterization only captures pre- and post-reaction snapshots, failing to establish correlations among catalyst structure, process behavior, and catalytic performance, thus impeding rational catalyst design. Accurate dynamic information across the entire reaction chain is critical, including atomic-scale active-site evolution, cross-interface photogenerated carrier transport, and molecular-level reactant adsorption/activation along with key intermediate dynamics. Achieving this requires in-situ/operando characterization. This review systematically summarizes recent in-situ techniques applied to photocatalytic CO2 reduction, clarifying their principles, detection limits, and applicable scenarios. Their application value is highlighted across three dimensions: (1) real-time tracking of catalyst dynamic structural evolution (e.g., phase reconstruction, defect dynamics, valence transitions) under operating conditions; (2) quantitative elucidation of photogenerated carrier separation, migration, and recombination, identifying factors governing interfacial charge transfer; (3) accurate identification of adsorption configurations and transformation pathways of intermediates (*COOH, *CO, *CHO), providing direct experimental evidence for mechanistic deduction. Finally, common limitations of current in-situ techniques are outlined, and future directions, including multi-technique combinations and integration with theoretical calculations, are proposed.

Chlorella vulgaris, a microalga that has emerged as promising for heavy metal bioremediation, is studied for its metal removal efficiency, metal recovery, and post-treatment biomass regeneration under scaled-up cultivation conditions. The microalga was exposed to a mixture of heavy metals (Cd, Zn, Cu, Pb, and Ni) at concentrations of 1, 5, and 10 ppm during cultivation in a 30 L pilot-scale photobioreactor (PBR) with micro-bubble aeration. Following acid-mediated metal recovery, the microalgal biomass was re-cultivated to evaluate its ability to regenerate under post-treatment conditions. Metal removal and recovery efficiencies were quantified via atomic absorption spectroscopy (AAS), while population, morphology, and physiological responses were monitored using optical microscopy, UV-Vis spectrophotometry and Fourier transform infrared (FTIR) spectroscopy. Metal removal efficiencies were metal-, time-, and concentration-dependent, exceeding 90% for Cu, Cd, and Pb, at 1 ppm on Day 3, and lowest for Ni (13-45%). Metal recovery from microalgal biomass through acidification demonstrated moderate to high desorption efficiencies, ranging from 40% to 90%, with metal- and concentration-specific dependencies. Regarding the microalga's response to regeneration, the 1 ppm culture exhibited the highest biomass recovery even from Day 3 of regeneration, as supported by cell morphology, growth rate, and pigment content. This integrated pilot-scale approach demonstrates the feasibility of combining biosorption, metal recovery, and biomass regeneration within a single system, supporting the development of circular and sustainable microalgal-based bioremediation strategies.

In this study, long-term variation in atmospheric 7Be activity concentrations in Japan was investigated through long-term continuous measurements spanning 1987-2020. A dataset was constructed by integrating sequential observations from Tsukuba in eastern Japan and Dazaifu in western Japan, providing multidecadal (over three decades) data of near-surface 7Be activity concentrations. Measured 7Be activity concentrations ranged from not detected to 18 mBq m-3, with a mean monthly value of 4.6 mBq m-3. Distinct seasonal patterns were observed. In Tsukuba, 7Be activity concentrations exhibited a maximum in spring and a secondary peak in autumn, with lower levels in summer and winter. In contrast, a pronounced decrease occurred only in summer in Dazaifu. Annual 7Be activity concentrations gradually increased, with notable peaks in 1995, 2009, and 2019. These variations corresponded well with increases in cosmic-ray intensity and decreases in sunspot number. This indicates that long-term variation in atmospheric 7Be activity concentrations is primarily controlled by production rate variations associated with the solar modulation of galactic cosmic rays. The multidecadal continuous dataset provides fundamental information on the behavior of atmospheric cosmogenic radionuclides in Japan and offers a valuable basis for evaluating future variations in solar activity and atmospheric transport processes.

This study develops an Explosive-release Particle Tracking Model (EPTM) and couples it with the Worldwide version of System for Prediction of Environmental Emergency Dose Information (WSPEEDI) to explicitly represent the explosion-induced initial condition in atmospheric dispersion simulation. The developed model is designed to resolve the particle dynamics immediately after an explosive release and to provide a physically based initial condition for subsequent atmospheric transport simulations. To demonstrate the capability and impact of EPTM, numerical experiments were conducted under the idealized meteorological field. The results show that the impact of explosion-induced initial motion on concentration and surface deposition is substantial near the explosion point but diminishes with transport distance. Immediately after explosive release, the explosive case produces a rapid vertical redistribution of radioactive materials. This vertical redistribution increases concentration near the surface and increases surface deposition near the release point compared with the non-explosive case. As a result, air concentration maxima and surface deposition patterns near the explosion point are controlled by the explosion-driven initial conditions represented by EPTM. In contrast, in the downwind area, differences in air concentration and surface deposition between the explosive and non-explosive cases become small because horizontal advection and turbulent diffusion govern the spatial distribution of the concentration and deposition, reducing the relative influence of the initial explosion as the plume evolves. Consequently, increasing explosive energy amplifies the contrast of air concentration and surface deposition near the explosion point while exerting limited influence on downwind concentration and deposition distributions.

Graph-theoretic analysis provides a rigorous mathematical framework for investigating molecular architecture; however most existing studies primarily rely on conventional topological indices and centrality measures that characterize connectivity without explicitly quantifying structural control, redundancy, vulnerability or resilience. This study develops a unified graph-theoretic framework for resilience-oriented analysis of cyclic peptide molecular graphs through a collection of novel connectivity-based descriptors. Oxytocin is selected as the principal case study because of its well-defined cyclic architecture and conserved disulfide bridge, four additional cyclic peptides: Vasopressin, Desmopressin, Octreotide and Somatostatin are analyzed to validate the robustness, discriminative capability and general applicability of the proposed methodology. Hydrogen-suppressed molecular graphs were constructed from experimentally established molecular structures. In addition to classical graph-theoretic measures including degree, betweenness, closeness, eigenvector centralities and network efficiency, the proposed framework introduces the Enhanced Structural Control Index (ESCI), Bond Criticality Index (BCI), Weighted Bond Criticality Index (WBCI), Disulfide Structural Redundancy Index (DSRI), Atom Vulnerability Spectrum (AVS) and Molecular Resilience Ratio (MRR). Structural robustness was further investigated by comparing targeted perturbations performed through sequential removal of the five highest-ranked AVS atoms with random vertex deletions. The oxytocin molecular graph contains 69 vertices, 71 edges and a cyclomatic number of three revealing strong dependence on a limited set of articulation points and bridge edges. AVS exhibits a strong correlation with betweenness centrality (Pearson correlation >0.91 across all investigated peptides) while providing complementary efficiency-based vulnerability information beyond conventional centrality measures. Comparative validation across five cyclic peptide molecular graphs demonstrates that the proposed descriptors consistently distinguish structural control, bond criticality, redundancy, vulnerability and resilience. The proposed framework establishes a mathematically interpretable, computationally efficient and broadly applicable methodology for graph-theoretic analysis of cyclic peptide molecular graphs with potential extensions to larger peptide systems and related biomolecular networks.