Browse Articles

Precatalyst Engineering Directs Reconstruction Into Coupled Defective Sites for Selective CO ₂ ‑to‑Formate Electroreduction

ABSTRACT Bismuth‐based materials are promising for formate production from CO 2 electroreduction, yet their rational design is hindered by an inability to control their dynamic reconstruction, which often leads to poorly defined active sites. Here, we propose a precatalyst engineering strategy wherein the crystallographic structure dictates the reconstruction pathway toward targeted active sites. Using bismuth oxyiodide as a model system, we demonstrate that layered BiOI transforms into metallic Bi favorable for the hydrogen evolution reaction. In contrast, the robust three‐dimensional framework of non‐stoichiometric Bi 5 O 7 I structurally preserves iodine, directing its reconstruction into a surface rich in coupled bismuth vacancies and iodine dopants. This uniquely defective configuration achieves a formate Faradaic efficiency of 96.8% at a high current density of 400 mA cm −2 , while demonstrating stable operation for over 100 h with negligible activity loss at 200 mA cm −2 . Combining in situ characterization with theoretical calculations, we elucidate the structure‐dependent evolution mechanism. This work establishes a design principle for bismuth oxyhalide precatalysts to program reconstruction pathways for efficient CO 2 electroreduction.

Enhancing rice productivity, grain quality and soil fertility through Pseudomonas mosselii biopriming

Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles

Abstract Untargeted mass spectrometry can detect thousands of molecules at once, potentially offering powerful insights into complex samples. However, the increasing scale of experimental datasets and spectral libraries limits our ability to extract and annotate structural information to allow for interpretation. Here, we present the software tool MS2LDA 2.0 that helps to address this gap by identifying recurring fragmentation patterns (Mass2Motifs) that can reflect shared chemical substructures. We introduce automated annotation support through Mass2Motif Annotation Guidance (MAG) that provides suggestions to interpret detected patterns. Our unsupervised pattern mining tool enables the study of much larger datasets with up to 14 times faster analysis than its predecessor. We demonstrate the utility of MS2LDA 2.0 and MAG in applications such as detecting pesticide-related substructures and exploring unknown fungal compounds. Together, these advances make it easier to uncover meaningful chemical patterns in complex data.

Market competition in banking and asset-liability maturity mismatch of non-financial firms: Evidence from Vietnam

This study examines the effect of bank competition on corporate investment-financing maturity mismatch, utilizing a panel dataset of 498 listed firms in Vietnam from 2008 to 2024. Bank competition is measured using both structural and non-structural indicators, allowing for a nuanced assessment of market dynamics. The findings reveal a robust positive association between bank competition and maturity mismatch, suggesting that intensified competition leads firms to increase their reliance on short-term debt relative to long-term investment needs. This relationship holds under multiple robustness checks, including alternative variable constructions, fixed effects specifications, crisis period exclusions, and instrumental variable approaches. Mechanism analyses indicate that bank competition affects firms’ debt maturity structures, increasing both the proportion and scale of short-term borrowing. Heterogeneity tests further show that this effect is stronger among firms with higher bank debt dependence, greater financial constraints, and higher borrowing costs, while it is weaker in capital-intensive sectors.

Mechanism‐Guided, Data‐Driven Discovery of a Dinuclear Gold Catalyst for Promoting Oxidative Addition

ABSTRACT Gold catalysis is frequently constrained by the limited accessibility of Au(I)/Au(III) redox pathways, particularly for the direct oxidative addition (OA) of aryl halides. Here, we present a mechanistically guided and machine learning‐accelerated strategy to design dinuclear gold complexes capable of facile OA with aryl iodides. Mechanistic DFT calculations reveal a favorable cationic Au(III)–Au(I) OA pathway localized at a single gold center within an electronically coupled bimetallic framework. Guided by this insight, 42 398 bidentate ligands have been screened using high‐throughput virtual screening, multiobjective Bayesian optimization and DFT refinement. This approach identifies pyridine–phosphine (di‐PN) ligands as privileged scaffolds, which can dramatically reduce the OA activation barrier and render the reaction exergonic. Interpretable machine learning and energy decomposition analyses elucidate that the enhanced reactivity arises from a synergy of geometric pre‐distortion, axial electronic polarization, and adaptive Au–Au interactions. A representative predicted dinuclear gold catalyst has been synthesized and experimentally validated in a model sulfonylation reaction of iodobenzene, supporting the practical relevance of the computationally identified di‐PN scaffold. This work establishes a mechanism‐guided, data‐driven workflow for evaluating ligand effects in dinuclear gold redox catalysis, with broader implications for multinuclear transition‐metal catalyst development.

Experimental study on load-bearing failure characteristics and repeated seepage resistance performance of immobilized microbial cement

Applying Artificial Intelligence and machine learning in precision nutrition

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises

Velocity zones (e.g., 1.0–0.75 m·s -1 ) are commonly aligned with terminology such as “starting strength”, ‘speed-strength’, ‘strength-speed’, ‘accelerative strength’, or ‘absolute strength’. However, the load-velocity profiles of most exercises do not align with these discrete bands. The aims of this study were to 1) develop load-velocity profiles of seven weightlifting derivatives; and 2) create exercise-specific velocity zones that can be used to guide training prescription. Fourteen (6 males and 8 females) weightlifting athletes undertook six testing sessions that required maximal strength testing on occasions one and two, and the development of load-velocity profiles for the power snatch, hang power clean, snatch pull, hang clean pull, hang power snatch, clean pull, and hang snatch pull on testing occasions three to six. During each testing occasion, peak velocity was assessed. Linear mixed models with effect size ±95% confidence limits (CL) were used to detect changes across profiles and estimate exercise specific velocity zones. While all load-velocity profiles had a clear reduction in velocity as load was increased, each exercise was found to have substantially different velocity zones when compared to previous recommendations. Of note, all ‘absolute strength’ zones (i.e., > 80% one repetition maximum) from the weightlifting derivatives were found to be greater than 1.3 m·s -1 which is commonly used as the threshold for ‘starting strength’. These findings demonstrate that, if these terms are to be used, exercise-specific load-velocity profiles should be developed. Furthermore, these findings provide practitioners with exercise-specific zones that can be used to enhance training prescription and target specific strength qualities.

Discovery of a Robust Single‐Atom Ruthenium Emission Control Catalyst

ABSTRACT Herein, we discover a robust emission control catalyst featuring Ru single‐atom sites even undergoing thermal oxidative aging at 850°C in a 10%H 2 O/10%O 2 /N 2 mixture gas stream, expecting to substitute the traditional Rh catalysts by cutting ∼73% of the total cost. The stability challenges in elevated‐temperature oxidizing environments were overcome via constructing the isolated Ru atoms anchored by square‐planar coordination with four lattice oxygen in ceria and suppressing Ru atom migration toward the single Ru─O x ─Ce catalytic centers by introducing the Zr‐rich materials, which are conducive to inhibiting the formation of undesirable N 2 O by‐products during catalytic NO reduction. More importantly, the challenge of low‐temperature C─H bond activation in short‐chain alkanes on the isolated single‐atom sites was broken through by constructing the CeZrO–Ru SA –CeO 2 three‐phase interfaces to promote the H‐spillover. The low‐cost Ru SA –CeO 2 /CZ single‐atom catalyst exhibited much better stability, lower selectivity of N 2 O and NH 3 by‐products, and higher activity for CO conversion under oxygen‐lean conditions compared to commercial Rh catalysts for automotive emission control applications. This work opens new avenues for developing the new generation of low‐cost, robust emission control catalysts in the future.

Biocompatible Interface for Organic Electrochemical Transistors Enables Bioadhesion and Over‐Swelling Suppression

ABSTRACT Bioelectronic interfaces necessitate devices that not only align mechanically with biological tissues but also adhere effectively to wet surfaces and maintain electrochemical stability over extended periods. However, glycolated conjugated polymer (g‐CP) channels in organic electrochemical transistors (OECTs) face challenges due to mechanical incompatibility and swelling‐induced degradation. We propose a versatile interfacial design employing a conformal hydrogel coating that transforms various p‐type and n‐type g‐CPs into bioelectronic interfaces that can harmonize with tissue and maintain adhesion. This innovative coating ensures seamless interaction between the device and biological tissues, while concurrently mitigating channel swelling. As a result, the coated OECTs exhibit a figure‐of‐merit µ C* roughly double that of uncoated OECTs and show enhanced stability over 1800 operational cycles. When utilized within a flexible, complementary circuit, these coated OECTs deliver a substantial voltage gain of 210 V V −1 and consume an exceptionally low power of 20 nW. Validated through in vivo electrocorticographic recordings, the platform achieves a signal‐to‐noise ratio of 28 dB—significantly surpassing that of conventional electrodes—highlighting its potential for high‐fidelity neural interfacing. This study elegantly combines the mechanics of soft hydrogels with the superior performance of organic electronics through deliberate interface engineering, providing a comprehensive strategy for cutting‐edge biointegration.

Hybrid attention-RNN and HMM framework for reliable high-speed arterial vs. urban road classification under degraded GPS conditions

Embryo quality control via lineage-specific aneuploid cell elimination in embryos and stem cell-derived embryo models

A triple-band terahertz metamaterial perfect absorber for biomedical applications and biomarker detection

This study presents a triple-band perfect metamaterial absorber with a compact microstructured design, achieving near-unity absorption rates of 99.92%, 99.97%, and 99.58% at three distinct terahertz resonance frequencies of 0.925 THz, 1.71 THz, and 2.7675 THz, respectively, for advanced biomedical biosensing applications. The work represents a theoretical and numerical proof-of-concept investigation based on full-wave electromagnetic simulations and refractive-index-assisted biosensing analysis, demonstrating the feasibility of high-sensitivity terahertz detection of biological analytes. The proposed absorber consists of concentric copper ring resonators combined with a central 7-shaped snowflake pattern, developed on an FR-4 dielectric substrate and backed by a continuous copper ground plane. Numerical simulations using the FDTD method were combined with a genetic algorithm approach, enabling the design of high-performance terahertz metamaterials, yielding three sharp, high-Q resonances with near-perfect absorption and ultra-narrow linewidths characteristics. The proposed sensor also demonstrates excellent capabilities including breast cancer cell identification with peak sensitivity of 10,142.86 GHz/RIU, infectious agent recognition, and polarization insensitivity over 0–90°, alongside enhanced absorption tolerance that supports clinical deployment. To evaluate biosensing capability, refractive-index-assisted numerical modeling was performed using literature-reported dielectric properties of biological analytes, including cancer cells, viruses, glucose concentrations, blood components, intracellular materials, and biological tissues. The proposed sensor achieves a maximum sensitivity of 529 GHz/RIU at the third resonance mode with strong linear resonance shifts and high spectral selectivity. Owing to its multi-band terahertz sensor facilitates and strong confinement of electric and magnetic fields at the resonant frequencies enhances the device’s sensitivity to minimal changes in the surrounding medium, allowing for precise, label-free detection of biological analytes including viral variants, malaria pathogens, glucose concentrations, hemoglobin elements, classification of diabetes severity and can also differentiate in anemia categorization across diverse biomedical applications.

Anthraquinone‐Engineered Thiophene‐Based Nanotube‐Like Porous Aromatic Framework as a Robust and Efficient Bifunctional Photocatalyst for C─H Cyanation of Tertiary Amines and Hydrogen Peroxide Generation

ABSTRACT The development of stable organic semiconductor photocatalysts—those capable of withstanding harsh conditions while maintaining high activity—remains a significant challenge. To this end, this study proposes an anthraquinone engineering strategy that aims to simultaneously enhance both structural stability and catalytic efficiency of organic semiconductors for photocatalysis. Specifically, 1,3,5‐tri(thiophen‐2‐yl)benzene (TTB)‐based porous aromatic frameworks (PAFs) (TTB‐PAFs) are optimized by linking anthraquinone fragments via carbon‐carbon bond formation. The unique electron distribution, abundant active sites, nanotube‐like micromorphology, and robust carbon‐carbon bonding character of the optimized TTB‐PAF jointly facilitate the charge separation/transfer, mass transportation, and stability during photocatalysis. Remarkably, these result in the optimal C─H cyanation of tertiary amines over the PAF‐396 photocatalyst, achieving excellent yields (up to 99%), good substrate adaptability (18 examples), and good recyclability (10 cycles), thereby surpassing the performance of reported porous organic semiconductor materials under similar conditions. Furthermore, PAF‐396 also achieves efficient photosynthesis of hydrogen peroxide (H 2 O 2 ) with a high synthesis rate of 5154 µmol g −1 h −1 from air and water without a sacrificial reagent under blue LED lamp irradiation.

Energy‐Efficient Electrocatalytic Semi‐Hydrogenation of Alkynols by Synergistic Ni‐Fe Pairs and Ni Clusters on N‐Doped Carbon

ABSTRACT Electrocatalytic semi‐hydrogenation (ECSH) of alkynes using water as a hydrogen source is expected to provide a revolutionary solution for upgrading the traditional hydrogenation process. An ingenious design of the electrocatalyst is required to break the tradeoff between activity, selectivity, and Faradaic efficiency (FE). Herein, a non‐noble metal catalytic system, containing well‐defined Ni‐Fe atom pairs and Ni clusters on N‐doped carbon, is constructed by a two‐step annealing method for energy‐efficient ECSH of alkynols. The optimized catalyst with collaborative Ni‐Fe pairs and Ni clusters effectively suppresses hydrogen evolution reaction (HER) competition and C═C over‐hydrogenation, and simultaneously accomplishes three critical objectives at ultra‐low applied potential (−0.125 V vs. RHE): nearly 100% conversion, 100% selectivity, and high FE of up to 98% (for 2 h). Joint experiments and theoretical calculations demonstrate that adjacent Ni‐Fe pairs electronically tune the neighboring Ni clusters, and the resulting synergy enables complementary functions of the two sites: Ni‐Fe pairs accelerate H 2 O dissociation, whereas Ni clusters regulate alkynol/alkenol adsorption for selective semi‐hydrogenation. The excellent stability, wide substrate universality, ultrahigh TOF, and low energy consumption of this low‐cost catalyst distinguish it from noble‐metal‐based systems with poor FE, offering a promising strategy for designing efficient polymorphic component catalysts.

Vertical facial pattern associations with craniocervical posture and cervical curvature appear attenuated in a contemporary digital-era cohort: a historical control analysis

Microseismic monitoring with the quake neural operator

Abstract Accurate monitoring of small-scale seismic events is essential for seismological studies. Traditional techniques rely on manual or automated seismic phase picking, which often leads to inaccuracies in microseismic monitoring due to unclear phase onsets. Here we introduce the Quake Neural Operator (QNO), a deep learning algorithm that builds earthquake catalogs directly from continuous data without explicit phase picking. As a multi-task operator, QNO utilizes classification and regression to detect and locate events across arbitrary seismic network geometries. We show that QNO successfully characterizes events where state-of-the-art phase picking fails. Applying QNO to the Geysers geothermal field, we identify nearly an order of magnitude more seismic detections than reported in routine catalogs. These results are validated through comparison with the Phase Neural Operator and visual inspection. QNO holds the potential to reveal undetected seismic activity, enhancing our understanding of subsurface processes critical to both natural phenomena and industry applications.

Biochemical characterizations of leaves and fruits in Crataegus monogyna Jacq., C. pontica K.Koch, C. microphylla K.Koch, and C. pentagyna Waldst. & Kit. ex Willd

The genus Crataegus comprises a diverse group of species with significant medicinal and nutritional value. This study aimed to characterize the biochemical composition of Crataegus monogyna Jacq., C. pontica K.Koch, C. microphylla K.Koch , and C. pentagyna Waldst. & Kit. ex Willd fruits and leaves by evaluating their phenolic profiles, antioxidant capacities, and metabolic interactions. The study also assessed biochemical variations among the analyzed samples and the impact of different ecological conditions on biochemical traits. Descriptive statistical analysis revealed substantial variability in phenolic contents among species. The highest coefficients of variation were observed in ferulic acid (200.00%), flavonol (141.40%), and epicatechin (122.75%). Correlation matrix analysis (CMA) demonstrated strong positive relationships between fruit total phenol and fruit total flavonoid ( r  = 0.97*) and between fruit hydroxycinnamic acid and fruit ortho-diphenol ( r  = 1.00**), suggesting possible co-accumulation patterns among these metabolites within the analyzed dataset. Exploratory multiple regression analysis (MRA) indicated statistical associations between antioxidant capacity and several phenolic-related variables, including hydroxycinnamic acid, ortho-diphenol, and cinnamic acid within the analyzed dataset. Principal component analysis (PCA) revealed that the first three principal components (PCs) collectively explained 100.00% of the total variance, with PC1 accounting for 71.00%, PC2 for 19.91%, and PC3 for 9.09% of the variation. PC1 was primarily driven by hydroxycinnamic acid, total flavonoid, and total phenol, indicating their important contribution to the observed biochemical separation among the analyzed samples. PCA grouped C. monogyna and C. pontica closely together, whereas C. pentagyna exhibited a distinct profile, particularly in total phenol, total flavonoid, and hydroxycinnamic acid accumulation. Heat map analysis (HMA) classified the species and biochemical variables into distinct clusters, with ‘ C. pentagyna ’ exhibiting a unique metabolic profile, particularly in total phenol, total flavonoid, and hydroxycinnamic acid accumulation. These findings suggest potential biochemical associations between fruit and leaf phenolics, emphasizing the impact of genetic and ecological factors on phenolic metabolism in Crataegus species. The observed associations may provide preliminary information for future breeding-oriented studies aimed at enhancing bioactive compound content for functional food and medicinal applications. Further research integrating transcriptomic and enzymatic analyses is necessary to elucidate the regulatory mechanisms underlying phenolic biosynthesis and environmental adaptability.

A Self‐Monitoring Single‐Atom Copper Nanocapsule for Cascade‐Responsive Tumor Cuproptosis Therapy

ABSTRACT Cuproptosis represents a potent anticancer mechanism, yet its translation is hampered by the scarcity of tumor‐selective and efficient copper ion modulators. Herein, we develop a single‐atom copper‐based smart nanocapsule (SNC) that enables cascade‐responsive copper ion release within the tumor microenvironment (TME) for synergistic cuproptosis therapy and self‐monitoring. The SNCs are constructed by anchoring isolated copper atoms within a gold cluster/zeolite framework via the Cu‐imidazole/carboxyl coordination, guaranteeing atomic dispersion and high biostability. Upon encountering the weakly acidic and glutathione‐rich TME, the SNCs undergo sequential ligand protonation and competitive coordination, which synergistically triggers framework disassembly and controlled copper ion release. The intracellularly accumulated copper ions robustly activate the cuproptosis pathway and concurrently facilitate a specific fluorescence recovery of gold clusters for self‐reporting therapeutic monitoring. Both in vitro and in vivo assessments demonstrate high‐efficacy tumor therapy and imaging capabilities without eliciting systemic toxicity. This work pioneers a versatile cuproptosis‐induction platform with broad potentials for advancing diverse biomedical applications.

Nearly 100% Valorization in Seconds: Transforming PET‐Metal Oxide Mixtures Into M ₁ O _x Clusters on rGO and Premium Syngas/Aromatics Products

ABSTRACT The efficient recycling of polyethylene terephthalate (PET) is often hindered by challenges such as low‐value products, limited processing capacity, prolonged reaction times, and incomplete carbon conversion. Here, we develop a large‐scale, ultrafast Joule‐assisted reformation strategy that achieves nearly 100% valorization of physical mixtures containing 10 g of PET waste and metal oxides within seconds. This process converts more than 30% of the carbon into single‐metal‐atom oxide clusters supported on reduced graphene oxide (M 1 O x /rGO) nanosheets, while transforming the remaining carbon into high‐value syngas and aromatic compounds. When applied to a physical mixture of PET waste and commercial ZnO, the gaseous products consist mainly of syngas (88.91 mmol CO and 41.85 mmol H 2 ), the aromatic fraction contains 22.84 mmol benzene, and the solid product is Zn 1 O 4 /rGO nanosheets. Synchrotron‐radiation X‐ray absorption fine structure and X‐ray emission spectroscopy analyses confirm a four‐coordinate oxygen environment around the Zn center in the as‐synthesized Zn 1 O 4 /rGO nanosheets. The PET reformation pathway was monitored using quasi‐in situ Fourier transform infrared spectroscopy and quasi‐in situ gas chromatography/mass spectrometry, and ab initio molecular dynamics simulations revealed a fragmentation–annulation mechanism. The resulting Zn 1 O 4 /rGO nanosheets exhibit excellent electrocatalytic performance for syngas production via CO 2 reduction at an industrial‐level current density of 400 mA cm −2 . This work establishes a new paradigm for the near‐complete valorization of PET waste into high‐value products.