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To overcome the activity-stability trade-off of enzymes in organic solvents, we propose a "Tunnel dynamics guided" engineering strategy. MD simulations revealed that solvents induce a defensive compaction in lipases, creating pathological, narrow tunnels that obstruct catalysis in a dynamic perspective. By targeting critical tunnel-gating sites, we engineered mutants that effectively mitigated activity-stability trade-off, exhibiting 7.53- and 5.56-fold higher tolerance to 66.7% DMSO and 10% pyridine, respectively. Mechanistic analysis identified a newly formed D285-K290 salt bridge that rigidifies the tunnel entrance and shortens the substrate pathway by ∼40%, significantly alleviating mass transfer resistance. Validated via kilogram-scale sucrose laurate synthesis (96.5% conversion), this study establishes tunnel dynamics remodeling as a robust paradigm for designing next-generation industrial biocatalysts.
Reinforcement sensitivity theory (RST) most clearly relates to internalizing disorders. But a weak behavioural inhibition system (BIS as defined by RST) could underlie externalising, in general, and psychopathy in particular (Fowles, 1980). Conventional "rationally derived" RST scales (rRST) are not anchored in neurally defined RST systems (nRST). So, here, we use both rRST and nRST measures to assess psychopathy traits. We operationalised psychopathy via a four-factor model (affective | interpersonal | disinhibition | boldness). We operationalised rRST via the Heym, Ferguson & Lawrence (2008) updated version of Carver and White's (1994) BIS/BAS scales (BAS | BIS | FFFS). We operationalised nRST (goal inhibition system, GIS; goal repulsion system, GRS) via previously validated (Shadli et al., 2021) rhythmic power in the stop signal task (SST) and (goal approach system, GAS) via previously validated ERPs in the doors task. Initial bivariate correlations of psychopathy factors with rRST scales were as expected. We found no significant associations between psychopathy factors and nRST measures. A series of post hoc exploratory repeated measures ANOVAs guarded against non-linearity between psychopathy and nRST constructs. These found that: (1) Disinhibition traits might be explained (unexpectedly) by increased sensitivity in the GIS (i.e., conflict) and GRS (i.e., repulsion) and decreased sensitivity in the GAS (i.e., attraction). (2) Affective traits might be explained, as expected, by decreased sensitivity in the GIS and GRS. But an unexpected positive association was also found in the alpha frequency range for the GRS. So, nRST systems (particularly GIS) do not explain psychopathy. rRST scales were more aligned with expectations but were explained via their "rational" basis not RST per se. Unlike internalizing, nRST does not appear strongly related to externalising disorders in general and psychopathy in particular. rRST appears distinct from nRST.
Large language models (LLMs) are being studied as oncology decision-support tools but can produce inaccurate outputs. We compared LLM performance in gynecologic oncology across three knowledge-integration configurations differing in retrieval strategy and underlying model, using the modified Generative Performance Score (mGPS) as the primary outcome. Fifty de-identified gynecologic oncology cases were submitted (October-November 2025) to three LLMs: baseline GPT-5, an NCCN-anchored GPT-5 retrieval-augmented generation (RAG) configuration, and OpenEvidence (a literature-anchored clinical AI without NCCN access at that time). Three gynecologic oncologists independently scored outputs using the mGPS (range - 1 to +1; Guideline Concordance plus Hallucination Penalty). Wilcoxon signed-rank tests and mixed-effects ordered logistic regression were used. GPT-RAG produced the highest mGPS (0.83, SD 0.26), followed by OpenEvidence (0.70, SD 0.27) and baseline GPT-5 (0.65, SD 0.31). GPT-RAG exceeded baseline (W = 189.5, Z = -3.42, P < .001, r = 0.49) and OpenEvidence (W = 254.0, Z = -2.64, P = .008); OpenEvidence and baseline did not differ (P = .22). Mixed-effects modeling confirmed higher mGPS for GPT-RAG (OR 3.74; 95% CI, 1.57-8.90). Inter-rater agreement (ICC) was 0.49 for mGPS, 0.30 for Hallucination Penalty, and 0.70 for Readability and Rationality. NCCN-anchored RAG outperformed both baseline GPT-5 and a literature-anchored clinical AI without direct guideline access. OpenEvidence's subsequent NCCN integration (April 27, 2026) provides external validation of guideline anchoring's operational importance. Findings reflect benchmark performance, not clinical safety or improved patient outcomes.
Photodynamic therapy (PDT) faces severe clinical limitations due to tumor hypoxia and an immunosuppressive microenvironment. To address these challenges, we rationally designed a rhenium-based system, RGCS@PEG nanoparticles. This platform incorporates a Cu-doped mesoporous silica core as an efficient carrier for the simultaneous loading of a rationally engineered Re-Bodipy photosensitizer (Re3) and the nitric oxide (NO) donor S-nitrosoglutathione (GSNO). The entire assembly is further coated with reactive oxygen species (ROS)-responsive thioketal-linked polyethylene glycol, enabling targeted payload release in the tumor microenvironment. The molecular design of Re3 facilitates highly efficient superoxide radical (˙O2 -) generation via a nicotinamide adenine dinucleotide (NADH)-oxidation-driven photocatalytic cycle, initiating oxygen-independent type-I PDT. Concurrently, the GSNO-derived NO not only exerts direct cytotoxicity but also reacts with ˙O2 - to form highly toxic peroxynitrite (ONOO-), thereby triggering a self-amplifying reactive nitrogen species (RNS) storm even under hypoxia. This cascade effectively eradicates hypoxic tumors by inducing ferroptosis-dominated immunogenic cell death. Furthermore, the RNS storm directly downregulates the immune checkpoint protein CD24, alleviating immunosuppression. Collectively, this RNS-amplifying nanoplatform represents a strategy that moves beyond conventional PDT by synergistically integrating gas therapy and checkpoint downregulation to remodel the tumor microenvironment and amplify antitumor immunity.
Bismuth-based metal-organic frameworks (Bi-MOFs) are emerging as a distinctive class of functional porous materials that combine structural tunability, biocompatibility, and unique physicochemical characteristics of Bi(III). In contrast to conventional transition-metal-based MOFs, however, their assembly is strongly governed by the stereochemically active 6s2 lone pair, variable coordination environments, and the pronounced hydrolytic tendency of Bi(III), which together complicate the realization of predictable structures, permanent porosity, and operational stability. This review provides a systematic analysis of the structure-property relationships of Bi-MOFs, focusing on how Bi(III) coordination chemistry governs framework design, topology evolution, and stability. The influence of major ligand families, including carboxylates, phosphonates, and phenolates, is critically discussed, and the representative strategies for transforming Bi-MOFs into functional derivatives, such as porous carbons, oxides, sulfides, and phosphides, are summarized. Their applications in photocatalysis, electrocatalysis, energy storage, biomedicine, and environmental remediation are also highlighted, with particular attention to the relationship between precursor structure and functional performance. Key challenges, including green synthesis, rigorous evaluation of porosity and stability, and the development of multivariate and application-oriented systems, are further discussed. Rather than viewing Bi-MOFs as universal substitutes for established MOFs, this review identifies them as specialized functional platforms and provides a critical perspective for the rational development of next-generation Bi-MOF-based materials.
Designing robust, fully synthetic receptors capable of selective protein detection remains critical for advanced clinical diagnostics. Molecularly imprinted polymers (MIPs) are a promising alternative to antibodies, but their application is often limited by empirical epitope selection and incomplete integration into functional assays. Here, we present an end-to-end platform combining data-driven epitope selection with polynorepinephrine-based molecular imprinting to produce fully synthetic protein receptors. The Python script "Epitope Selection Rational Approach" (ESRA), integrating physicochemical descriptor analysis and supervised machine learning, was developed and applied to human myoglobin (MYG) to identify effective imprintable peptide epitopes in the pre-analytical stage. Five selected epitopes were used to fabricate molecularly imprinted PNE nanofilms (MIPNE-NFs) and nanoparticles (MIPNE-NPs), targeting distinct regions of MYG. Systematic kinetic and affinity characterization by surface plasmon resonance (SPR) revealed pronounced epitope- and format-dependent recognition, underscoring the need for parallel evaluation of nanofilm and nanoparticle architectures. The optimal NF/NP combination, exploited as a capturing probe and a signal enhancer, respectively, was implemented in a fully antibody-free SPR sandwich assay. This configuration achieved sensitive detection over 4-125 ng mL-1, with a limit of detection of 0.9 ± 0.5 ng mL-1 in buffer and 0.70 ± 0.02 ng mL-1 in 1/200 diluted serum, covering the clinically relevant range for acute myocardial infarction, rhabdomyolysis, and muscle injury. This work establishes a generalizable workflow, from rational epitope selection to imprinting, kinetic validation, and assay integration, demonstrating that MIPNE can provide robust, antibody-free alternatives for sensitive protein detection in complex biological matrices.
Precise identification of agricultural machinery operation trajectories and efficient estimation of operation area are essential for cloud-platform-based machinery supervision and service settlement. However, raw GNSS trajectories collected from practical operation platforms are often affected by positioning drift, missing points, road-transfer trajectories, headland turns, and repeated or pseudo-missed operations, making it difficult for either purely rule-based trajectory screening or direct buffer-based area recovery to simultaneously achieve reliable recognition accuracy and engineering efficiency. To address this problem, this study proposes a staged trajectory identification and area estimation method for cloud-platform deployment. The method first standardizes raw trajectory sequences through attribute integrity checking, motion rationality filtering, speed cleaning, and temporal interpolation. Candidate operation trajectories are then organized using spatiotemporal neighborhood constraints to remove evidently non-operational long-distance transfer trajectories before image construction. A multi-channel trajectory image representation, in which speed, acceleration, and heading variation are encoded as feature channels, is further used as the input of an improved CBGAM U-Net semantic segmentation model for pixel-level refinement of field-operation trajectories. Finally, a cubic-spline-smoothed vector-buffer algorithm with width compensation and inward boundary correction is used to reconstruct operation coverage areas. Experimental results on the IAEMP platform dataset showed that the proposed method achieved an average trajectory recognition accuracy of 96.32%. In parcel-level area validation, the absolute area estimation errors of the tested field parcels were controlled within 3.00%, and the cloud-platform deployment test showed stable processing efficiency for practical operation records. In independent Real-Time Kinematic (RTK)-based field validation, the area accuracies reached 99.64% for wheat harvesting and 99.91% for rotary tillage. These results demonstrate that the proposed staged framework can improve trajectory identification reliability and area estimation consistency while maintaining practical deployability for agricultural machinery management platforms.
Green hydrogen production via direct seawater electrolysis represents a promising and sustainable pathway to reduce reliance on fossil fuels and mitigate carbon emissions, while simultaneously alleviating the pressure on limited freshwater resources. Covering 75% of the earth, seawater holds significant potential for large-scale hydrogen generation. However, its complex composition, particularly the high concentration of chloride ions (Cl-), impacts the performance and durability of electrocatalysts, leading to corrosion, degradation, and competing side reactions. To address these limitations, extensive research has focused recently on developing new strategies to enhance catalyst activity, selectivity, and long-term stability under harsh saline conditions. This review consolidates recent progress in the field, covering fundamental mechanisms, major operational challenges, and emerging approaches aimed at improving the efficiency of next-generation electrocatalysts for seawater electrolysis. Notable advancements include the modulation of the local reaction environment, the rational tuning of catalytic reaction mechanisms, and the regulation of interfacial pH. Additionally, innovative approaches such as light-assisted seawater electrolysis, along with the development of advanced passivation coatings, doped materials, and novel catalyst designs, are thoroughly examined. By presenting a comprehensive overview of these developments, this review aims to facilitate continued innovation in the pursuit of practical and scalable solutions for green hydrogen production from seawater. Finally, a thorough and critical discussion is presented in the last section of the review to assess the feasibility and scalability of these strategies.
We report here a robust multicomponent reaction (MCR) strategy for the synthesis of benzimidazole-linked covalent organic frameworks (COFs), overcoming the long-standing requirement for specialized ortho-functionalized precursors. By employing a copper-catalyzed C-H bond amination protocol with readily available non-ortho-functionalized amines, aldehydes, and azidotrimethylsilane (TMSN3), we successfully introduce the necessary nitrogen atom to construct the benzimidazole heterocycle in situ. This methodology not only circumvents synthetic constraints but also significantly expands the structural diversity of benzimidazole-linked COFs, as demonstrated by the successful synthesis of two highly crystalline frameworks, GBU-261 and GBU-262. When impregnated with phosphoric acid (PA), these COFs exhibit exceptional proton conductivity, reaching up to 4.41 × 10-2 S cm-1 at 95% relative humidity and 95 °C. The observed low activation energies indicate a Grotthuss-type proton transport mechanism, facilitated by the synergistic stabilization between the framework and the confined PA networks. This versatile approach paves the way for the rational design and exploration of heterocycle-linked functional materials.
White spots in dry-cured ham compromise product acceptability and cause economic losses. This study elucidated the accumulation mechanism by investigating proteolytic enzyme activities, protein degradation, metabolite profiles, oxidative modification and the composition of white spots in Jinhua ham with varying defect intensities. The spatial distribution frequency of white spots increased significantly with intensity, reaching 1.07 spots/cm2 in the high-intensity group. These samples exhibited higher activities of tyrosine aminopeptidase (TAP), phenylalanine aminopeptidase (PAP) and carboxypeptidase A, leading to extensive breakdown of structural proteins including myosin heavy chain, α-actinin, actin, troponin T, and a proteolysis index of 27.1%. 1H NMR spectroscopy identified 36 metabolites among these ham samples with amino acids constituting the dominant fraction. FT-IR and XRD analyses revealed that the white spot crystals were predominantly composed of co-precipitated tyrosine and phenylalanine. Furthermore, 2D 1H1H TOCSY NMR detected characteristic cross-peaks of oxidized tyrosine derivatives and a marked increase in dityrosine signal intensity within the spots. These results suggested that the accumulation of white spots was a complex process driven by the close coordination between proteolysis-induced amino acid supersaturation and crystallization, and the oxidative cross-linking of tyrosine. These findings provided a crucial theoretical basis for the white spot defect control of traditional Chinese ham by optimizing specific processing parameters to rationally modulate protease activities and oxidative extent in the future.
Glioblastoma (GBM) remains resistant to therapy due to cellular heterogeneity and adaptive stress responses, yet the role of tumor-intrinsic Toll-like receptor 4 (TLR4) signaling in this process remains unresolved. This review addresses a central inconsistency in the field by defining tumor-intrinsic TLR4 as a context-dependent signaling rheostat that generates distinct biological outcomes rather than a uniform tumor-promoting pathway. Across experimental systems, TLR4 signaling produces divergent effects that range from mesenchymal transition, invasion, and adaptive survival under chronic or therapy-associated conditions, to differentiation, apoptosis, and increased treatment sensitivity under specific cellular and temporal contexts. These opposing outputs are not contradictory but arise from defined determinants, including ligand environment, signaling dynamics, tumor cell state, and metabolic conditions. This framework explains previously discordant findings and establishes that the functional role of tumor-intrinsic TLR4 cannot be inferred from receptor activation alone. Instead, its impact is conditional and state-dependent. This perspective defines a clear experimental and translational priority: to identify the contexts in which tumor-intrinsic TLR4 signaling sustains tumor persistence versus exposes therapeutic vulnerability, thereby enabling rational and stratified intervention strategies in GBM.
Gallbladder cancer (GBC) and other biliary tract cancers (BTCs) are aggressive hepatobiliary malignancies with limited pharmacological treatment options and a poor prognosis. Even with platinum-gemcitabine regimens and selected targeted or immunotherapies, most patients experience only transient benefits, highlighting the need for new mechanistic approaches. Natural products, ranging from purified phytochemicals to complex traditional formulations, act on several key processes involved in gallbladder carcinogenesis, including chronic inflammation, epithelial-mesenchymal transition, apoptosis, and DNA damage responses. These multi-target effects are difficult to capture using reductionist approaches that focus on single receptors or pathways. Network pharmacology and molecular docking have therefore emerged as valuable in silico tools for understanding this complexity. By integrating compound-target predictions, protein-protein interaction networks, and pathway enrichment analyses with structural models of ligand-target binding, these approaches can generate pharmacological hypotheses, prioritize targets for validation, and suggest rational combinations with standard systemic therapies. However, their application to gallbladder and biliary tract cancers remains limited and methodologically heterogeneous. In this narrative review, we summarize current preclinical evidence, outline standard workflows, critically examine common pitfalls, and propose a best-practice workflow and reporting checklist. We also discuss how in silico methods can be integrated into experimental and clinical pharmacology to support the mechanism-driven development of natural product-based therapies in hepatobiliary oncology.
Improving the rational use of antibiotics is critical to limiting the development and spread of antimicrobial resistance. To assess the prevalence and types of antibiotic use and the association with patient characteristics in 2 teaching hospitals in Tunisia. Using a modified WHO point prevalence survey protocol, we collected data on the prevalence of antibiotics use and types and classes of antibiotic used from the medical records of 1028 patients hospitalized at the medical, surgical and intensive care units of 2 teaching hospitals in Sfax Governorate, Tunisia. We analysed the data using SPSS version 26. P ≤ 0.05 was considered statistically significant. The prevalence of antibiotic use was 26.8%, with an average of 1.7 antibiotics used per patient. Of the 466 antibiotic types used, 156 (35.4%) belonged to the access group and 277 (59.4%) to the watch group. Antibiotic use was highest (39.8%) among children aged <5 years and males had 30% higher chances of receiving antibiotics. Antibiotic use was higher among patients in high-risk wards than those in non-high-risk wards and among patients in intensive care units than in nonintensive care units. We found that 79.2% of the antibiotics were used empirically; watch antibiotics were used empirically in 78.7% of the patients. The frequent use of watch group antibiotics for empirical treatment and the significantly higher antibiotic use among children and the elderly indicate the need to re-assess prescription practices in these and other hospitals, enhance prescription education, and develop standard guidelines for optimising antibiotic use in Tunisia.
Although the design of bilayer interconnects in solid oxide fuel cells (SOFCs) has attracted attention, the regulation mechanism of the bilayer interconnect system on the electrical transport properties remains unclear. In this study, La0.3Sr0.7TiO3 (LST) and La0.8Sr0.2MnO3 (LSM) were used as bilayer interconnect materials. The interfacial oxygen partial pressure was tuned by systematically adjusting the LST/LSM thickness ratio, and the conduction mechanism of the bilayer interconnect was interpreted. LSM acted as an oxygen barrier. A thickness of about 5 µm appeared to be necessary to effectively block oxygen. The thickness of LSM influenced the baseline level of the interfacial oxygen partial pressure, while the thickness of LST protected the Ti3+ charge carriers by regulating the oxygen ion transport distance, thereby determining the actual level of conductivity. The results suggest that increasing LSM thickness or decreasing LST thickness reduced the interfacial oxygen partial pressure. At a fixed total thickness, a relatively high conductivity can be obtained when the LSM layer was slightly thinner than the LST layer. The regulation mechanism of bilayer interconnect thickness on electrical conductivity is as follows: the thicknesses of LSM and LST jointly affect the interfacial oxygen partial pressure, which in turn modulates the O2- content and Ti3+ carrier concentration within the LST layer and ultimately influences the electrical conductivity of the bilayer interconnect. The optimized bilayer interconnect was applied to a 5-cell flat-tubular segmented-in-series SOFC (FT-SIS-SOFC), which contributed to an approximately 40% increase in output power density compared to the non-optimized counterpart. This study may contribute to understanding the thickness ratio-conductivity relationship in LST/LSM bilayer interconnects and offers a potential theoretical basis and practical guidance for the rational design of high-performance SOFC interconnects.
Background Carbapenem-resistant Klebsiella pneumoniae (CRKP) represents a major global health threat. Rising colistin resistance in CRKP further restricts therapeutic options. With no newer antibiotics in the pipeline, the combination of ceftazidime-avibactam (CZA) and aztreonam (ATM) has emerged as a rational strategy to overcome complex β-lactamase-mediated resistance. Aim The aim of this study was to determine the in vitro susceptibility and synergy rates of CZA + ATM among colistin-resistant CRKP isolates. Methods A prospective laboratory study including 17 colistin-resistant CRKP isolates was conducted. Synergy testing was performed using the disk elution method in cation-adjusted Mueller-Hinton broth (CA-MHB), as per the latest CLSI guidelines. Results Notably, 12 of 17 (70.6%) isolates demonstrated synergistic susceptibility to CZA + ATM. Three isolates (17.6%) were susceptible to CZA alone, and the remaining two (11.8%) were susceptible to ATM alone. Conclusion CZA + ATM demonstrates remarkable in vitro efficacy against colistin-resistant CRKP and is a notable carbapenem- and colistin-sparing treatment alternative.
Zearalenone (ZEN) lactonase is a commercially promising enzyme that catalyzes the conversion of zearalenone into less toxic metabolites and has been expressed in Pichia pastoris. However, differences in culture media between shake-flask and fermenter systems hinder efficient production. Using a data-driven approach combined with rational analysis, an optimized medium (FM4CSP) was developed for both systems. ZEN lactonase activity reached 25.87 ± 0.52 U/mL in shake flasks with FM4CSP medium, whereas it was negligible in the conventional FM22 medium. To elucidate the underlying mechanisms, comparative transcriptomic and nitrogen composition analyses were conducted. The results revealed that organic nitrogen sources enhance heterologous protein expression by alleviating energy metabolic stress under oxygen-limited conditions. Large peptides serve as core active components, acting as slow-release nitrogen sources that maintain stable amino acid availability. The balanced peptide profile in complex nitrogen sources triggered metabolic reprogramming, including downregulation of reducing equivalent-generating pathways to prevent NADH accumulation and upregulation of oxidative phosphorylation to match energy supply with oxygen availability. The effectiveness of FM4CSP was further validated in a 30 L fermenter, where ZEN lactonase activity reached 327.56 ± 1.78 U/mL, representing a 1.59-fold increase compared with the conventional FM22 medium. This study developed a novel universal medium (FM4CSP) for both shake-flask and fermenter systems using a cost-effective corn steep powder (CSP) as a slow-release nitrogen source. This medium effectively bridges the compatibility gap between both systems and provides a scalable strategy for heterologous protein production in P. pastoris.
Monitoring national antibiotic consumption is essential to strengthen antimicrobial stewardship and efforts to combat antimicrobial resistance. To assess annual antibiotic consumption in Tunisia from 2016 to 2023. Using SPSS version 27, Spearman correlation, the WHO defined daily dose per 1000 inhabitants per day and the WHO access, watch and reserve classification, we analysed national antibiotic consumption data in Tunisia for 2016 to 2023. Overall antibiotic consumption increased from 31.6 defined daily dose per 1000 inhabitants per day in 2016 to 32.4 in 2023, with lower values for 2019-2022 (P = 0.096). Private sector consumption consistently exceeded public sector levels and access group antibiotics were the most consumed (78% in 2016, 65% in 2021 and 75% in 2023). Beta-lactam penicillins was most consumed (exceeding 60% in most years), followed by macrolides, lincosamides and streptogramins (≈10% in most years), while oral antibiotics represented 98% of total consumption. The predominance of generics indicates the need for vigilant antimicrobial stewardship to prevent costdriven overuse and mitigate resistance. There is a need to strengthen national antibiotic use surveillance, including in private sector facilities, and promote a more rational use of antibiotics.
Thyroid cancer exhibits substantial heterogeneity in its tumor immune microenvironment (TIME), which critically shapes disease progression and therapeutic responsiveness. While most differentiated thyroid cancers (DTCs) remain indolent, a subset evolves into radioiodine-refractory disease or progresses to poorly differentiated (PDTC) and anaplastic thyroid carcinoma (ATC), characterized by aggressive behavior and limited treatment options. Emerging evidence suggests that this transition is accompanied by dynamic immune reprogramming rather than static immune evasion. In this review, we propose a stepwise model of immune evolution in thyroid cancer, spanning from autoimmune-driven inflammation in chronic lymphocytic thyroiditis (CLT) to immune-exhausted states in advanced tumors. We systematically characterize immune cell composition, functional states, and regulatory networks across disease stages, highlighting key shifts in antigen presentation, T-cell functionality, and myeloid cell polarization. Building on this framework, we integrate tumor immune phenotypes ("hot", "altered", and "cold") with actionable biomarkers, including PD-L1 expression, tumor mutational burden, IFN-γ signatures, M2 macrophage-related signature, and tertiary lymphoid structures. We further map these immune contexts to rational therapeutic strategies, encompassing immune checkpoint blockade, combination regimens with tyrosine kinase inhibitors or radiotherapy, and emerging approaches such as innate immune activation and adoptive cell therapies. By linking immune evolution with therapeutic vulnerabilities, this review provides a translationally relevant framework for precision immunotherapy in thyroid cancer and highlights future directions for overcoming resistance in advanced disease.
Hard carbon has emerged as a highly promising anode material for sodium-ion batteries. However, rational regulation of its microstructure to achieve synergistic enhancement in electrochemical performance remains a critical challenge. In this work, we demonstrate a scalable co-pyrolysis strategy that integrates bamboo biomass with low-density polyethylene (LDPE) to regulate free-radical evolution and interactions during pyrolysis, thereby enabling the tailored formation of pseudo-graphitic domains and closed-pore architectures in biomass-derived hard carbon. Through systematic structural, spectroscopic, and electrochemical analyses, including in situ Raman spectroscopy and the galvanostatic intermittent titration technique (GITT), we establish multiscale correlations among preparation parameters, structural evolution, sodium-ion storage behavior, and overall electrochemical performance. The optimized hard carbon delivers a reversible capacity of 367.4 mAh g-1, with an initial Coulombic efficiency of 89.6%, and retains 92.5% of its capacity after 500 cycles at 0.1 A g-1. This work provides a novel precursor-modulation strategy to facilitate the practical commercialization of hard carbon for high-performance sodium-ion batteries.
Enveloped viruses, owing to the intrinsic sensitivity of their lipid envelopes to physical perturbations, have attracted increasing attention in the field of antiviral surface engineering. Compared with conventional chemical-based strategies, nanostructured surfaces can inactivate viruses through interfacial physical interactions without the need for continuous external stimuli. This review summarizes the primary mechanisms underlying nanostructure-induced viral inactivation, including mechanical piercing, stretching-induced rupture, and array-induced synergistic effects. Among these, membrane stretching and localized stress concentration arising from multivalent contact are identified as the dominant mechanisms. Key factors influencing antiviral performance, such as structural spacing, tip geometry, and virus size, are further discussed. In addition, common fabrication approaches, including etching, template-assisted replication, and plasma-based techniques, are reviewed. The contributions of surface composition to antiviral performance are also highlighted, with emphasis on the synergistic effects between nanostructured features and functional materials. Finally, current challenges related to mechanistic understanding, structure-activity relationships, and practical implementation are highlighted, providing insights for the rational design of antiviral surfaces.