Browse Articles

In Situ Synchronized SERS‐SEIRAS Unveils Cation‐Regulated Interfacial Water and Intermediates in the Oxygen Reduction Reaction

ABSTRACT Interfacial water plays a crucial yet poorly understood role in the alkaline oxygen reduction reaction (ORR) by modulating the adsorption of oxygen intermediates and mediating proton‐coupled electron transfer (PCET). However, the lack of techniques to dynamically correlate interfacial water with adsorbed intermediates makes it difficult to elucidate the mechanism governing the evolution of intermediate species. Here, we report a synchronized, site‐consistent SERS–SEIRAS platform that tracks interfacial water and surface‐adsorbed species (OOH ad and OH ad ) in real time during cation‐dependent ORR. Our results show that decreasing cation hydration energy induces the formation of an interfacial water layer with weak hydrogen bonding, low orientation constraints, and high dynamic flexibility, which diminishes its interactions with OOH ad and OH ad . This structure enhances water and oxygen transport and weakens OH ad solvation, thereby reducing OH ad coverage and accelerating the final PCET step. Our results reveal how cations reshape the interfacial hydrogen‐bond network to control ORR kinetics. More broadly, this work demonstrates the power of multi‐spectroscopic coupling for probing dynamic electrocatalytic interfaces and offers a strategy for improving catalyst performance via electrolyte and interface engineering.

A new dual-scale nearest neighbor statistical feature construction algorithm for imbalanced data oriented to Gaussian naive bayes classifiers

Abstract To address the performance degradation of Gaussian Naive Bayes (GNB) classifier on imbalanced datasets caused by sparse minority class features and severe class overlap, this paper proposes a new feature construction algorithm based on dynamic dual-scale nearest neighbor statistical ratio (NNDSR). The core of NNDSR is a dynamic dual-scale nearest neighbor mechanism, which is designed to accurately extract the local aggregation characteristics of samples and the inter-class boundary information. On this basis, new features are generated through cross-class and dual-scale statistical ratio operations. These features possess both strong discriminability and Gaussian distribution adaptability, which can significantly amplify class differences and effectively approximate the core assumptions of GNB. By optimizing the information expression of minority classes and enhancing class separability with these features, the algorithm avoids the information distortion problem of traditional sampling techniques and solves the mismatch between general feature enhancement algorithms and GNB’s core assumptions. Comparative experiments were conducted on 22 UCI datasets with varying scales, dimensions and imbalance ratios. Results show that NNDSR significantly outperforms the original data and 16 mainstream algorithms including sampling, feature enhancement and classifier-level optimization methods in core classification metrics such as AUC, G-mean and F-measure, with a notable improvement in the recognition accuracy of minority classes. Scalability tests further confirm its efficiency and stability on datasets with ten-thousand-level samples and within one hundred dimensions. This paper provides a robust new feature construction algorithm for GNB to handle imbalanced data, with strong practical application value.

Interfacial Proton Ordering Near the Electrode Surface Directs Carbonyl Electroreduction to Methylene

ABSTRACT Carbonyl‐to‐methylene deoxygenation is a fundamental transformation in organic synthesis, but conventional Clemmensen and Wolff–Kishner–Huang reductions require harsh acidic or basic conditions. Electrochemical reduction offers a milder alternative, yet commonly stops at the alcohol stage because the initially formed alcohol intermediate desorbs from the electrode before further C─O bond activation. Here, we report a ‐Gly interfacial catalytic system for aqueous electrochemical carbonyl‐to‐methylene conversion. In this system, the Pd‐rich electrode and glycine‐mediated interfacial regulation cooperate to retain alcohol intermediates at the electrified interface and promote their subsequent deoxygenation to methylene products. Time‐dependent reaction analysis supports a stepwise pathway involving initial carbonyl hydrogenation to an alcohol intermediate followed by further deoxygenation. Mechanistic and structural studies suggest that Pd sites supply surface H*, electron‐deficient Ni‐related sites generated through Ni─Pd coordination assist alcohol‐intermediate retention, and glycine regulates local proton availability and H* coverage in the interfacial region. This work highlights the ‐Gly system as an effective interfacial platform for directing carbonyl electroreduction beyond the alcohol endpoint under mild aqueous conditions.

Advancing three-dimensional tendon imaging using laboratory X-ray phase contrast techniques and refined sample preparation

Abstract Tendinopathy is of great socio-economic importance, with high rates of prevalance in both athletic and non-athletic populations. Despite this, there remains limited understanding of the three-dimensional macro and microscopic anatomy and its significance in health, clinical and sub-clinical disease due to difficulties in gaining three-dimensional images of tissue volumes. Although histology is considered the gold standard for pre-clinical tendon imaging, the tissue is notoriously difficult to process and section, leading to a high incidence of artefacts. X-ray phase contrast imaging (XPCi) is becoming increasingly important with regards to three-dimensional imaging of biological tissues, and has shown promise in tendon imaging with synchrotron radiation, however laboratory based imaging and associated sample preparation protocols have yet to be validated. samples in this work, equine superficial digital flexor tendons were prepared using various combinations of PBS, 10% neutral buffered formalin, 70% and 100% ethanol and imaged using a laboratory based edge illumination XPCi system in a custom made 3D printed container. Consistent with other findings for tissue, contrast for tendon tissue was found to be maximised when dehydrated in ethanol, while the fixation medium has no notable affect on contrast.

Research on comprehensive drought index prediction model based on CNN-LSTM

Precise Regulation of Intrachannel Negative Charge Density in Metal‐Organic Frameworks for Efficient Alkali‐Ion Transport

ABSTRACT Charged nanochannels are critical for efficient cation transport in metal‐organic frameworks (MOFs); however, the relationship between intrachannel negative charge density and ionic conductivity remains poorly understood. Here, we report structurally analogous MOFs with nanochannels of precisely tunable negative charge density: neutral N–MOF, moderately charged M–MOF, and highly charged H–MOF. Our results show that intrachannel negative charge density regulates the electrostatic microenvironment and host‐guest interactions, thereby controlling ion‐pair dissociation, cation hopping, and the concentration of mobile charge carriers. Fixed negatively charged groups within the MOF nanochannels promote salt dissociation and provide hopping sites for ion migration. However, excessive charge density in H–MOF causes electrostatic anchoring that restricts Li + mobility, whereas the moderate charge density in M–MOF provides the optimal balance between ion dissociation and ion transport. Accordingly, ionic conductivity follows the order M–MOF > H–MOF > N–MOF for both Li + and Na + transport. M–MOF achieved ionic conductivities of 1.56 mS cm −1 for Li + and 1.38 mS cm −1 for Na + at 30°C, establishing precise intrachannel charge regulation as a design principle for next‐generation solid‐state electrolytes.

Genome near-haploidization in CDC73-wildtype parathyroid tumors

Limitations to air free cooling in data centers under rising heat and humidity

Assessing the effects of population aging on health financing structures: evidence from APEC countries using panel data

Fibroblast‐Mimetic Lignin Polymersomes for Logic‐Gated Synthesis of Mechanically Reconfigurable Bioskins

ABSTRACT Ageing is inevitable and accompanied by progressive loss of skin elasticity. Fibroblasts, embedded within the extracellular matrix, finely regulate skin mechanics via membrane‐bound ligands. Creating synthetic assemblies that mimic fibroblast function is appealing yet challenging. Here, we present a strategy that co‐assembles lignin with divinyl ligands to generate fibroblast‐mimicking polymersomes, enabling precise programming of bulk materials to emulate human skin across distinct physiological stages. Because lignin polymersomes are driven by relatively weak π–π stacking, hydrophobic ligands efficiently intercalate among aromatic rings, and their interfacial distribution can be tuned via molecular engineering. The polymersomes can be programmed in a Boolean logic‑gate manner (OR, AND, and NOT) to synthesize skin‐mimetic gels with tailored mechanical properties, analogous to fibroblast behavior. Furthermore, the platform enables on‑demand, high‑resolution 3D printing of complex bioskin architectures. This work provides a biomimetic paradigm for the synthesis and precise control over assembly from the molecular to the macroscopic scale.

KAN-PROSPECT: a Kolmogorov–Arnold Networks–integrated framework for predicting the effects and adverse reactions of natural products via transfer learning

Interfacial Electronic Modulation Redirects Anodic Radical Chemistry for Selective C─C Bond Cleavage in Electro‐Oxidative Lignin Depolymerization

ABSTRACT Electro‐oxidative lignin depolymerization is considered a promising route to renewable aromatics; however, its selectivity is often limited by competition with oxygen evolution and uncontrolled overoxidation at the anode. A CuO/Cu 0.92 Co 2.08 O 4 hetero structured catalyst was developed, with which 88% conversion of 2‐phenoxy‐1‐phenylethanol was achieved, affording benzaldehyde and phenol in 53% and 27% yields, respectively. By means of time‐resolved analysis and intermediate‐feeding experiments, a tandem pathway involving benzylic oxidation to 2‐phenoxyacetophenone followed by C α ‐C β scission was identified. In situ Raman and FTIR spectroscopy, together with EPR, revealed that the Cu─Co interface suppresses the accumulation of OER‐type CoOOH species while promoting oxygen‐centered radical chemistry under reaction conditions. Through density functional theory, it was further shown that interfacial electronic modulation strengthens substrate adsorption and lowers the barrier for bond cleavage. The same mechanistic logic was extended from the model substrate to enzymatic hydrolysis lignin, for which characteristic interunit linkages are weakened while aromatic products are retained. These findings establish interfacial control of anodic radical chemistry as a strategy for selective lignin bond editing under electrochemical conditions.

Sporulation generates stress-dependent phenotypic variability in the heterothallic industrial Saccharomyces cerevisiae Ethanol Red

Beyond Fluorination: A Golden Criterion Guided by Chemical Coordination‐Informed Machine Learning for High‐Voltage Electrolyte Design

ABSTRACT Fluorine chemistry has garnered attention for extending operating voltage limits of electrolytes through robust interfacial passivation owing to fluorine's strong electronegativity. However, confronted with solvent/salt/additive multicomponent induced vast combinatorial space, conventional high‐voltage electrolyte recipe design has been confined to reliance on fluorine content adjustments, resulting in inevitable trade‐off between oxidation stability and ion transport kinetics. Herein, we develop a Chemical Coordination‐Informed Molarity feature parsing approach embedded into machine learning for training adapted models. By building the one‐to‐one mapping between components and chemical‐coordination atomic molarities of a given recipe, the trained gradient boosting regression achieves a prediction of oxidation potential with MAE below 0.36 V. Demonstrating 2808 experiment operational candidates based on a ternary‐solvent blend, we reveal the pronounced role of mono‐coordinated fluorine and double‐bonded oxygen molarity ratio (F1/O1) for breaking the oxidative stability limit, and define a golden design criterion for guiding O1‐involved recipes: F1(≥8.19)/O1(≥13.39)[0.55, 1.10]. Following this, we validate three experimentally reported low‐fluoride recipes and identify two promising ones exhibiting oxidation potentials around 6.3 V vs. Li + /Li along with high ion‐transport kinetics for further assessments. This work demonstrates customizable feature engineering in yielding intelligent materials design principles for reconciling multiple target performance that are usually mutually exclusive.

Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images

DS-ARO: a multi-strategy improved artificial rabbits optimization algorithm for global optimization and corporate bankruptcy prediction

A customized AI-based machine learning model for evaluating participants’ activities under a workshop setting

Adaptive queue management in healthcare using supervised Q-learning with time-varying reward and cost structures

Advancing biomedical data analytics using explainable neural network-based learning model for progressive neurodegenerative disorder diagnosis

Abstract Huntington’s disease (HD) is an inherited neurological disease caused by variations in the huntingtin (HTT) gene, which leads to neuronal degeneration. Conventionally, HD is affiliated with the gathering and misfolding of mutant HTT arising from an increased number of CAG triplets. Artificial Intelligence has emerged as an important tool in healthcare, supporting the monitoring, detection, and management of HD. Machine learning and deep learning methods are widely used for automated HD identification using neuroimaging, genetic, and clinical data. However, most DL models behave like a black box, making it difficult to interpret decision-making from clinical data, which reduces trust in medical applications. Therefore, this study presents an Explainable Neural Network-Driven Learning Model for Neurodegenerative Disorder Diagnosis (XNNLM-NDD). The primary objective of the proposed model is to examine clinical attributes and identify disease patterns efficiently for precise diagnosis. The model performs feature selection using a hybrid combination of minimum redundancy maximum relevance and ReliefF methods to select the most informative and non-redundant features from the dataset. For classification, the proposed approach employs a feature tokenizer-transformer model, which can capture complex feature interactions and improve classification accuracy on structured medical data. Furthermore, the model is optimized using the Cycle-Norm-Adam algorithm. For ensuring model transparency and interpretability, SHAP-based explainable artificial intelligence method is used to highlight the contribution of each feature towards the final prediction. The experimental evaluation is carried out on the Huntington Disease Dataset sourced from Kaggle. The results show that the proposed XNNLM-NDD approach accomplishes improved performance with an accuracy of 96.50% compared to existing techniques, indicating its efficiency in progressive neurodegenerative disorder diagnosis.

Prophage genomics of carbapenemase-producing Klebsiella pneumoniae from animal-derived food sources