Browse Articles

Coherent transformation of metal halide perovskites

Market-adaptive techno-economic and business management optimization of a renewable poly-generation hub for power, water, and green hydrogen

PregMedNet: Multifaceted maternal medication impacts on neonatal complications

Abstract While medication use is common among pregnant women, medication safety remains insufficiently characterized because studies in pregnant women are challenging due to safety concerns. The recent digitization of healthcare databases and advances in computational methods have created new opportunities for large-scale, retrospective drug safety evaluations. Here, we present PregMedNet, a platform that characterizes multifaceted maternal medication associations on neonatal outcomes during pregnancy, covering more than 27,000 drug-disease pairs across 1,152 medications and 24 outcomes. These results encompass known and additional odds ratios (ORs), adjusted ORs, and drug-drug interactions, systematically analyzed using nationwide claims data and an advanced machine learning pipeline. Notably, one of the associations identified in this study is supported by in vivo experiments, increasing confidence in PregMedNet’s findings and highlighting the utility of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms underlying the associations are explored using a graph learning method, providing candidate pathways for future mechanistic investigations. We expect that PregMedNet will contribute to advancing maternal medication safety and improving neonatal outcomes by providing extensive, multifaceted drug safety information on this previously underrepresented population.

Effect of coil geometry on sensitivity distribution in field-free line magnetic particle imaging

Alignment switching in 3D-printed smectic liquid crystal elastomers

Abstract Extrusion-based additive manufacturing has emerged as a powerful platform for designing shape-morphing materials through controlled orientation. However, existing approaches primarily rely on a single mode of flow-induced alignment, limiting orientation programmability. Herein, we present a direct-ink-writing approach for smectic liquid crystal elastics that exploits two distinct alignment modes within a single ink. The smectic ink exhibits shear- and temperature-dependent orientation switching, enabling molecular alignment either perpendicular or parallel to the print direction. Combined rheological, X-ray, and molecular dynamics analyses reveal that this alignment inversion arises from the preservation or collapse of smectic layers under flow. This reversible switching encodes both contractile and elongational actuation within individual filaments, greatly expanding the design freedom of printed liquid crystal elastomers. We demonstrate 2D and 3D structures with diverse programmed shape transformations, highlighting the potential of this platform for adaptive soft actuators and architected functional materials.

A generalizable ensemble meta-learning framework for assessing 15-minute cities

Abstract Driven by the global challenges of climate change in cities, the 15-Minute City concept has gained prominence as a transformative strategy for fostering resilient and sustainable city environments. However, current approaches remain fragmented, lacking a comprehensive, multi-dimensional, and data-driven framework for systematically assessing such cities. To address this limitation, this study proposes an assessment model based on an ensemble meta-learning technique, aiming to enable a more robust, scalable, and comparable evaluation of city performance within the 15-Minute City framework. The ensemble meta-learning model was developed using eight different machine learning techniques and involves 21 cities across five continents for model training and evaluation. This ensemble meta-learning framework achieves an overall accuracy of 78.5%, with F1 scores of 79.1% for compliant areas and 77.8% for non-compliant areas. Performance levels that remain robust across diverse geographic and morphological contexts, with lower performance observed primarily in regions with limited open data coverage. The ensemble meta-learning framework was further validated and applied to three districts in Istanbul, showing consistent and competitive performance compared to recent literature. By combining cross-continental transferability with interpretable spatial outputs, the proposed framework provides a scalable, reproducible, and methodologically transparent approach to 15-Minute City assessment, offering a practical tool for evidence-based urban planning and policy development.

Development & assessment of polyherbal extracts for treating oral cancer by integrating phytochemistry, bioactivities, and network pharmacology

Abstract Oral carcinoma is a major global health concern and a leading cause of carcinoma-related mortality. This study evaluated a methanolic polyherbal extract for its phytochemical composition and biological efficacy, along with mechanistic insights via in silico network pharmacology. GC-MS and UPLC profiling confirmed diverse bioactive compounds, particularly polyphenols, with UV peaks at 272 nm. The extract showed strong antioxidant activity (IC₅₀ = 108 ± 0.17 µg/mL) and significant antimicrobial effects (inhibition zones: 6.00–21.33 mm). Cytotoxicity assays revealed anticancer activity against OECM-1 oral carcinoma cells (IC₅₀ = 18.86 µg/mL). The polyphenolic fraction suppressed pro-inflammatory cytokines (IL-6, IL-10, TNF-α, IL-1β, TGF-β) with IC₅₀ values of 3.02–6.15 µg/mL. Network pharmacology and molecular docking indicated multi-target interactions of key phytochemicals in pathways relevant to oral carcinoma progression. These findings suggest the polyherbal extract may be a complementary candidate in oral carcinoma management via antioxidant, anti-inflammatory, and antiproliferative effects.

Predicting habitat suitability of weed species Lysimachia arvensis (L.) U. Manns & Anderb. in wheat cropping systems using machine learning approach

Psychoactive substance use and salivary periodontal pathogens among pregnant women in rural Rwanda

Professional burnout among dietitians and the perceived role of artificial intelligence tools

Amharic neural coreference resolution with multi-head attention and named entity recognition

Observed changes in accumulated dangerous heat during Northern Hemisphere summers

Reassessment of eddy viscosity turbulence models as alternatives to Reynolds stress models for gas cyclone separator CFD simulations

Abstract Various Reynolds stress turbulence model (RSM) formulations have been widely employed in computational fluid dynamics (CFD) simulations of gas cyclone separators for more than two decades, primarily because they do not rely on the Boussinesq hypothesis (isotropic eddy-viscosity assumption) and can better represent anisotropic turbulence and strongly swirling flows, which simpler eddy-viscosity turbulence models (EVMs) often struggle to capture accurately. But are RSMs truly the only Reynolds-averaged Navier-Stokes (RANS) turbulence models capable of simulating the mean flow in gas cyclone separators? Does their continued popularity result from their actual performance or from modeling bias arising from the Boussinesq (isotropic eddy-viscosity) assumption and the limitations in the discretization approaches (including grid generation and numerical schemes) adopted in previous CFD studies of EVMs? To address these questions, this work compares predictions from several EVMs (Spalart-Allmaras, standard k-epsilon, renormalization group k-epsilon, realizable k-epsilon, standard k-omega, and shear-stress transport k-omega) with and without curvature correction, against those from linear and quadratic pressure-strain RSMs and reference experimental data for a 0.29-m-diameter Stairmand gas cyclone. The results show that appropriate discretization approach enhances the capability of some non-modified EVMs to predict mean velocities in weak-curvature-effect circumstance (e.g., inside the cyclone barrel). Curvature correction improves the mean flow prediction accuracy of EVMs. In addition, caution is advised when applying RANS-predicted root-mean-square velocity fluctuations to describe cyclone flow phenomena. Finally, this study demonstrates that, for the study conditions, the realizable k-epsilon model with curvature correction can provide accuracy comparable to linear and quadratic pressure-strain RSMs in predicting the mean flow in gas cyclone separators while requiring shorter computational time, thereby challenging the long-held preference that RSMs are the only suitable RANS turbulence models for such applications.

Performance evaluation of corroded reinforced concrete columns strengthened with FRCM under cyclic loading and subsequent corrosion rate

Ionic liquid-grafted PVDF membrane incorporated with g-C3N4-Fe3O4 for the removal of cationic dye and improved anti-bacterial activity

Long-Term Survival of Hypoimmune Allogeneic Islets without Immunosuppression

Lewis Acid/Base Au–Mn Pairs in Metal Nanoclusters for Enhanced Photocatalytic C(sp ² )–H Arylation

ABSTRACT Integrating polar bimetallic active sites on the metal nanocatalysts for advanced cross‐coupling reactions holds great promise, yet is challenging. Here, we report the successful synthesis of Lewis acid/base bimetallic nanocluster Au 20 Mn 2 and demonstrate its catalytic potential in visible‐light‐driven direct C(sp 2 )–H arylation of aryl halides. The cluster comprises two Au 11 incomplete icosahedra fused into an Au 20 kernel, which features four uncoordinated gold atoms in a rhombic pattern and is protected by two bridged Mn(CO) 5 moieties. Experimental and computational studies disclose that the Mn sites function as electron donors, facilitating polarized Au(δ + )−Mn(δ − ) bonds with pronounced [Mn(CO) 5 ] − →Au 20 σ donation interactions. The Lewis acid/base pair bestows Au 20 Mn 2 with exceptional photocatalytic performance in C(sp 2 )–H arylation of various substituted (hetero)aryl bromides and chlorides with electron‐rich‐to‐deficient arenes under mild conditions. This protocol boasts high functional group tolerance, supports gram‐scale synthesis, and is applicable to both the late‐stage functionalization of complex molecules and the targeted synthesis of bioactive products. Mechanism analyses uncover that the collaboration between the gold‐involved single electron transfer activation of aryl halides and Mn π‐base activation of arenes accounts for the remarkable efficiency of this system.

Operando Electrochemical Phase Engineering Overcomes Activity–Stability Trade‐Off in Perovskite Electrocatalysts

ABSTRACT Electrocatalysts for diverse electrochemical devices commonly suffer from a trade‐off between activity and stability, as highly active surface features are intrinsically prone to degradation under operating conditions. Here, we develop an operando electrochemical phase engineering (ECPE) strategy that enables rapid and reversible regeneration of active sites during operation. Using a highly active yet unstable perovskite cathode, Sr 2 Fe 1.5 Mo 0.5 O 6‒δ , in solid oxide electrolysis cells, we show that cathodic polarization induces reversible phase transitions between perovskite and Ruddlesden‐Popper structures. This dynamic phase switching redistributes exsolved Fe nanoparticles and segregates inactive Sr‐containing phases, restoring catalytic activity within minutes. Operando x‐ray diffraction and electron microscopy reveal the nanoscale redox and structural evolution underlying this deactivation‐regeneration process. As a result, a cathode that initially deactivates within hours sustains efficient CO 2 electroreduction for over 2100 h with near‐unity Faradaic efficiency. Operando ECPE thus provides a promising framework for breaking the activity–stability trade‐off in electrochemical energy conversion.

Unlocking Interfacial Binder Chemistry for Efficient Li ⁺ Desolvation in 3C‐Rate Lithium Metal Pouch Cells

ABSTRACT Lithium‐ion transport dynamics at the cathode electrolyte interface play a crucial role in determining the performance of lithium batteries. However, most studies of ion‐transport dynamics have focused on electrolyte‐governed solvation structures and cathode electrolyte interphase (CEI) chemistry, while largely overlooking the complex interactions at the actual cathode surface. Herein, we unveil that interfacial binder chemistry plays a pivotal role in regulating Li + desolvation dynamics and interfacial solvation configuration. Specifically, strongly polar functional groups (e.g., ─COOH and ─C≡N) engage in stronger interactions with Li + , facilitating rapid ion transport and reducing solvent molecule retention at the interface. This enhanced transport dynamics mitigates interphase degradation and preserves the cathode surface structure, thereby enabling stable cycling and high‐rate performance of the LiNi 0.92 Co 0.04 Mn 0.04 O 2 (Ni92) cathode. At the pouch‐cell level, Ni92‐PAN||Li cells deliver 505.1 Wh kg −1 at 4.9 Ah and 506.2 Wh kg −1 at 10.3 Ah, with stable cycling over 140 and 80 cycles, respectively. Moreover, a 5.8 Ah Ni92‐PAN||Li pouch cell exhibits energy densities ranging from 523.8 to 405.7 Wh kg −1 at discharge rates of 0.2 to 3 C. These findings identify interfacial binder chemistry as a key regulator of ion transport at the cathode electrolyte interface and underscore its importance in high‐performance lithium batteries.

Proteome‐Wide Target Identification Using Reactive Metallo‐Scaffolds ( <i>r</i> ‐mS): A Platform for Metallodrug Discovery

ABSTRACT Metal complexes offer unique opportunities as scaffolds in chemical biology and drug discovery, with tunable geometries, modular coordination environments, and structural features not readily accessible with organic molecules. Here, we introduce reactive metallo‐scaffolds ( r ‐mS ) as a class of metal complexes designed to map ligandable cysteines across the mammalian proteome. These covalent warhead‐bearing metal complexes use the metal centre and ligand architecture to modulate cysteine engagement, with cysteine labelling occurring through the electrophilic chloroacetamide warhead. Using chemoproteomics, we profiled a focused r ‐mS series in HEK293T lysate, identifying novel cysteine ligandability and demonstrating how metal identity, arene substituents and overall molecular topography govern cysteine reactivity and proteome‐wide targeting. Among the series screened, r ‐mS‐2 emerged as the most productive scaffold, which engaged cysteine 119 within the functionally relevant SAM‐binding domain of PRMT1. This interaction was validated by intact protein LC‐MS and was determined to functionally inhibit the activity of PRMT1. Structural modelling and docking provided insights into the molecular basis of binding, which implied π‐stacking and electrostatic complementarity in driving covalent engagement. Together, these results position reactive metallo‐scaffolds ( r ‐mS ) as a versatile platform for proteome‐wide covalent ligand discovery and the rational development of next‐generation metallodrugs.