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Extended Endocyclic Conjugation and <i>N,N</i> ‐Bidentate Chelation Alleviate the Capacity–Stability Trade‐Off in Organic Magnesium Battery Cathodes
ABSTRACT Rechargeable Mg batteries represent an appealing post‐lithium energy‐storage technology, yet their advancement is hampered by the scarcity of cathode materials combining high capacity, rapid kinetics, and long‐term cycling stability. In this study, we propose a molecular design strategy integrating extended endocyclic conjugation with polydentate Mg 2+ coordination. Using hexaazatriphenylene (HATN), a rigid planar macrocycle featuring extensive π‐conjugation and N , N ‐bidentate chelating sites, as the Mg‐storage active center, we constructed polymer cathodes through monothioether and dithioether linkages. Theoretical and experimental analyses reveal that the HATN unit enables high‐capacity, multi‐electron reversible Mg 2+ storage while maintaining structural stability via efficient charge buffering through strong electron delocalization, offering a notable advantage in a “capacity‒delocalization” evaluation framework. The thioether linkage suppresses dissolution and yields high surface area with hierarchical porosity, boosting interfacial kinetics and Mg 2+ transport. The resulting polymer cathode delivers a high capacity of 370 mAh g ‒1 at 0.1 A g ‒1 , superior rate capability (94 mAh g ‒1 at 5.0 A g ‒1 ), and exceptional cycling stability (95% capacity retention over 500 cycles at 1.0 A g ‒1 ). This work presents an innovative molecular‐level design strategy for high‐performance organic Mg‐battery cathodes, advances the mechanistic understanding of multivalent‐ion storage, and provides a new paradigm for rational electrode engineering for multivalent battery systems.
Enrichment of mutated DNA enables ultra-sensitive ctDNA detection in NSCLC using shallow targeted sequencing
A Molecular Trimming Strategy for Hypoxia‐Tolerant Photosensitizers With Enhanced cGAS‐STING Activation
ABSTRACT The development of effective photosensitizers for photo‐immunotherapy is highly desirable yet remains challenging, particularly given the prevailing reliance on π‐conjugation extension in conventional molecular design. Herein, we propose a counterintuitive “π‐bridge trimming” strategy to construct high‐performance Ir(III) complexes photosensitizers. Unlike the conventional π‐extension approach, the three‐ring fused TTz‐Ir outperforms its π‐extended five‐ring fused analog TBTz‐Ir in multiple aspects, including molar absorptivity, solubility, photocatalytic activity, and photocytotoxicity. Mechanistic studies revealed that the superior performance of TTz‐Ir stems from its longer triplet‐state lifetime, more efficient charge separation, and transport favoring type I reactive oxygen species (ROS) generation. Upon light irradiation, TTz‐Ir not only produces 1 O 2 via energy transfer, but also efficiently generates type I ROS such as O 2 •− , H 2 O 2 , and •OH, primarily through oxygen reduction reaction (ORR) and water oxidation reaction (WOR) pathways, ensuring robust photocytotoxicity even under hypoxia. These ROS induces mitochondrial and nuclear DNA damage, leading to activation of the cGAS‐STING pathway and robust antitumor immunity. When encapsulated into DSPE‐PEG 2000 ‐Biotin, TTz‐Ir NPs achieve effective tumor accumulation and significant tumor suppression in vivo. This work provides a novel molecular design paradigm and efficient metal complexes for photo‐immunotherapy.
FusionDiff: a dual-path diffusion-based framework for few-shot authenticity analysis of ceramic microstructures
Abstract The authenticity of ceramic components is closely tied to their microscopic structures, making automatic and accurate identification essential for quality control. However, this task is often constrained by the scarcity of labeled samples. This study investigates the potential of large-scale pretrained diffusion models as feature extractors, leveraging the rich visual priors embedded in their generative processes to provide a robust semantic foundation for small-sample learning. To address the limitations of the original U-Net in global representation modeling and the weak local-detail sensitivity of DeiT, we propose a dual-path fusion encoder, FusionDiff. Within a frozen Stable Diffusion V1.4 framework, CNN and adapter-enhanced DeiT paths operate in parallel and are deeply integrated via feature gating. Following a “self-supervised pretraining + supervised fine-tuning” paradigm, classification is performed using a Random Forest classifier. On our custom ceramic dataset, FusionDiff achieves a test accuracy of 99.07%, outperforming SD-CNN (97.44%), DeiT (96.30%), and ResNet50 (97.00%) under a unified self-supervised evaluation protocol. Even under extremely small-sample conditions ( $$n = 50$$ ), the model attains 90.7% validation accuracy, demonstrating competitive data efficiency and cross-domain generalization capability.
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.