Search Results (649)

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu₂O, g-C₃N₄/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

The rapid advancement of micro/nano-electromechanical systems (MEMS/NEMS) and precision manufacturing has fundamentally challenged traditional friction theories at the nanoscale. Classical continuum models fail to capture energy dissipation mechanisms at the atomic level, which are influenced by interfacial phenomena such as electron transfer, charge redistribution, and energy level realignment. Density functional theory (DFT), renowned for its accurate description of ground-state properties in many-electron systems, has emerged as a key tool for uncovering quantized friction mechanisms. By quantifying potential energy surface (PES) fluctuations, the evolution of interfacial charge density, and dynamic electronic band structures, DFT establishes a universal correlation between frictional dissipation and electronic behavior, transcending the limitations of conventional models in explaining stick–slip motion, superlubricity, and non-Amonton effects. Research breakthroughs in the application of DFT include characterizing frictional chemical potentials, designing heterojunction-based superlubricity, elucidating strain/load modulation mechanisms, and resolving electronic energy dissipation pathways. However, these advances remain scattered across interdisciplinary studies. This article systematically summarizes methodological innovations and cutting-edge applications of DFT in computational tribology, with the aim of constructing a unified framework for carrying out the “electronic structure–energy dissipation–frictional response” predictions. It provides a state of the art of using DFT to help design high-performance lubricants and actively control interfacial friction. Full article

The growing demand for air connectivity, coupled with the forecasted increase in passengers by 2040, implies an exigency in the aviation sector to adopt sustainable approaches for net zero emission by 2050. Sustainable Aviation Fuel (SAF) is currently the most promising short-term solution; however, ensuring its overall sustainability depends on reducing the life cycle carbon footprints. A key challenge prevails in hydrogen usage as a reactant for the approved ASTM routes of SAF. The processing, conversion and refinement of feed entailing hydrodeoxygenation (HDO), decarboxylation, hydrogenation, isomerisation and hydrocracking requires substantial hydrogen input. This hydrogen is sourced either in situ or ex situ, with the supply chain encompassing renewables or non-renewables origins. Addressing this hydrogen usage and recognising the emission implications thereof has therefore become a novel research priority. Aside from the preferred adoption of renewable water electrolysis to generate hydrogen, other promising pathways encompass hydrothermal gasification, biomass gasification (with or without carbon capture) and biomethane with steam methane reforming (with or without carbon capture) owing to the lower greenhouse emissions, the convincing status of the technology readiness level and the lower acidification potential. Equally imperative are measures for reducing hydrogen demand in SAF pathways. Strategies involve identifying the appropriate catalyst (monometallic and bimetallic sulphide catalyst), increasing the catalyst life in the deoxygenation process, deploying low-cost iso-propanol (hydrogen donor), developing the aerobic fermentation of sugar to 1,4 dimethyl cyclooctane with the intermediate formation of isoprene and advancing aqueous phase reforming or single-stage hydro processing. Other supportive alternatives include implementing the catalytic and co-pyrolysis of waste oil with solid feedstocks and selecting highly saturated feedstock. Thus, future progress demands coordinated innovation and research endeavours to bolster the seamless integration of the cutting-edge hydrogen production processes with the SAF infrastructure. Rigorous techno-economic and life cycle assessments, alongside technological breakthroughs and biomass characterisation, are indispensable for ensuring scalability and sustainability. Full article

Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease characterized by variable clinical manifestations and a complex, often unpredictable disease trajectory, which hinders early diagnosis and personalized treatment. This review highlights recent breakthroughs in biomarker discovery, emphasizing the transformative impact of multi-omics technologies and deep profiling of the synovial microenvironment. Advances in genomics and transcriptomics have identified key genetic variants and expression signatures associated with disease susceptibility, progression, and therapeutic response. Complementary insights from proteomics and metabolomics have elucidated dynamic molecular patterns linked to inflammation and joint destruction. Concurrently, microbiome research has positioned gut microbiota as a compelling source of non-invasive biomarkers with both diagnostic and immunomodulatory relevance. The integration of these diverse data modalities through advanced bioinformatics platforms enables the construction of comprehensive biomarker panels, offering a multidimensional molecular portrait of RA. When coupled with synovial tissue profiling, these approaches facilitate the identification of spatially resolved biomarkers essential for localized disease assessment and precision therapeutics. These innovations are transforming RA care by enabling earlier detection, improved disease monitoring, and personalized treatment strategies that aim to optimize patient outcomes. Full article

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous spectrum of disorders that lead to progressive and irreversible vision loss. Gene therapy is the most promising emerging treatment for IRDs. While gene augmentation strategies have demonstrated clinical benefit and results within the first approved ocular gene therapy, their application is restricted by adeno-associated virus (AAV) packaging capacity and limited efficacy for dominant mutations. Recent breakthroughs in precision genome editing, particularly base editing (BE) and prime editing (PE), have provided alternatives capable of directly correcting pathogenic variants. BE enables targeted single-nucleotide conversions, whereas PE further allows for precise insertions and deletions, both circumventing the double-strand DNA cleavage or repair processes typically induced by conventional CRISPR–Cas editing systems, thereby offering advantages in post-mitotic retinal cells. Preclinical investigations across murine and non-human primate models have demonstrated the feasibility, molecular accuracy, and preliminary safety profiles of these platforms in targeting IRD-associated mutations. However, critical challenges remain before clinical application can be realized, including limited editing efficiency in photoreceptors, interspecies variability in therapeutic response, potential risks of off-target effects, and barriers in large-scale vector manufacturing. Moreover, the delivery of genome editors to the outer retina remains suboptimal, prompting intensive efforts in capsid engineering and the development of non-viral delivery systems. This review synthesizes the current progress in BE and PE optimization, highlights innovations in delivery platforms that encompass viral and emerging non-viral systems and summarizes the major barriers to clinical translation. We further discuss AI-driven strategies for the rational design of BE/PE systems, thereby outlining their future potential and perspectives in the treatment of IRDs. Full article

The River Chief System (RCS), an innovative policy for sustainable water governance in China, has diffused through parallel and hierarchical modes, exerting heterogeneous impacts on corporate green innovation—a key driver of sustainable development. Using a multi-period difference-in-differences (DID) design and data from A-share listed companies in Shanghai and Shenzhen (2005–2022), this study examines how these diffusion modes affect corporate green innovation, including its breakthrough and incremental forms. The study finds that (1) under the parallel diffusion mode, RCS does not significantly promote corporate green innovation overall and even exhibits an inhibitory effect in capital-intensive industries; (2) Under the hierarchical diffusion mode, the RCS significantly improves the level of corporate green innovation, with a notably stronger promoting effect on breakthrough innovation than incremental innovation; (3) The hierarchical diffusion mode promotes green innovation by alleviating corporate financing constraints and enhancing management’s green awareness; (4) Heterogeneity analysis further reveals clear regional and industrial disparities in policy effectiveness: hierarchical diffusion shows significant effects in eastern and western regions as well as in technology-intensive industries, but still exerts an inhibitory effect in the central region and labor-intensive industries. This study provides empirical evidence on the differential effects of environmental policy dissemination and offers insights for optimizing RCS implementation and promoting sustainable economic development. Full article

Rotary Steerable System (RSS) enhances directional drilling efficiency by over 300% via dynamic bit adjustment during string rotation. This study aims to systematically address these bottlenecks, quantify technical boundaries, and propose actionable breakthrough paths for China’s RSS technology in shale gas development. To address China’s shale gas RSS bottlenecks, this study proposes a “Material-Algorithm-System” tri-level strategy centered on an innovative “Tri-loop System.” Key innovations include (1) silicon nitride–tungsten carbide composite coatings to enhance thermal resilience, tested to withstand 220 °C, reducing thermal failure risk by 40% compared to conventional materials; (2) downhole reinforcement learning optimization; (3) a “Tri-loop System” integrating downhole intelligent control, wellbore-surface bidirectional communication, and cloud monitoring, shortening downhole command response latency from over 5 s to less than 1 s. In practical shale gas development scenarios—such as the Sichuan Basin’s deep coalbed methane wells and Shengli Oilfield’s tight reservoirs—this tri-level strategy has proven effective: the high-frequency electromagnetic wave radar increased thin coal seam drilling encounter rate by 23%, while the piezoelectric ceramic micro-actuators reduced tool failure rate by 35% in 175–200 °C environments. This approach targets raising China’s shale gas RSS application rate to 60%, supporting sustainable oil and gas exploration. Full article

To advance breakthroughs in core technologies and foster the growth of technology-based enterprises, China has introduced the Sci-Tech Finance Pilot Policy with the aim of promoting sci-tech enterprise development through optimized financial resource allocation. Based on a sample of technology-based firms listed on China’s SME Board and ChiNext Board from 2009 to 2023, this study empirically examines the relationships between the Sci-Tech Finance Pilot Policy, scale expansion, and technological innovation using a multi-period Difference-in-Differences (DID) model. The key findings reveal that, first, the Sci-Tech Finance Pilot Policy simultaneously promotes corporate scale expansion and technological innovation, generating both scale and innovation effects; second, it generates scale and innovation effects by optimizing financial resource allocation, while scale expansion further induces additional innovation effects. Third, heterogeneity analysis reveals that the innovation effect of the Sci-Tech Finance Pilot Policy is stronger, and the scale effect is weaker when the technology-based enterprise is privately owned, possesses a solid R&D foundation, or operates in a favorable external innovation environment. The findings of this study demonstrate that technology finance policy promotes high-quality development through the synergy between scale and innovation, providing policy implications for developing countries in implementing the United Nations Sustainable Development Goals. Full article

Monoclonal antibodies (mAbs) represent one of the most successful classes of biopharmaceuticals, with more than 100 approved for treating oncological, immunological, and infectious diseases. Antibody discovery and development have been driven by diverse methodologies. Classical strategies such as mouse hybridoma technology, phage display, transgenic mouse models, and single B cell isolation have enabled the generation of high-affinity therapeutic antibodies. Beyond binding affinity, recent innovations in combinatorial antibody libraries have facilitated the selection of functional antibodies within cellular environments, revealing their ability to act as agonists or antagonists and influence signal transduction pathways. These insights expand therapeutic applications by enabling modulation of complex cellular responses. Recent breakthroughs in artificial intelligence, involving antibody generation supported by rapidly growing antibody sequence and structure databases, are transforming computational protein design. This review highlights five major approaches (hybridoma technology, phage display, transgenic mouse models, and single B cell isolation, de novo antibody design) for antibody discovery and development. These approaches offer innovative strategies designed to accelerate the discovery process and enhance therapeutic outcomes for human diseases. Full article

Microbial hydrocarbon degradation mediated by natural/modified clay minerals represents an eco-friendly and economically viable remediation strategy for hydrocarbon contamination. However, its effects are not always positive as they depend on multiple factors, including clay mineral types, modification methods, microbial species, and hydrocarbon substrates. This review systematically synthesizes existing fragmented studies concerning the impacts of natural clay minerals, modified clay minerals (acid/alkali/thermal/organic/metal ion), and clay minerals containing composite materials on microbial hydrocarbon biodegradation. Based on current findings, future research should prioritize the following recommendations: (1) avoid using concentrated strong acids in acid activation; (2) exclude metal cations that induce strong adsorption (reducing hydrocarbon bioavailability) or trigger excessive interlayer hydrolysis (some trivalent cations) in metal cation modification; (3) eliminate biologically toxic agents during organic modification; and (4) expand understanding of alkali/thermally modified clay minerals and clay mineral-containing composite materials in this direction. Natural/modified clay mineral-mediated microbial degradation is a highly promising remediation technology for hydrocarbon contamination and poised to advance and achieve breakthroughs through continuous synthesis of knowledge and innovation. Full article

As chemical pollution and food safety risks in agriculture have increased due to global population growth and a food demand surge, the development of new environmentally friendly pesticide carriers is urgently needed to build a sustainable agricultural system. Owing to the excellent biocompatibility and controlled degradation of biomass materials and their specific interactions with active ingredients, biomass-based composites have unique advantages in the field of pesticide delivery. By regulating the carrier structure, the targeted controlled release of the pesticides can be achieved, leading to improvements in the chemical stability of the active substance and target absorption efficiency, and a significant reduction in environmental impact. This paper summarizes the innovative applications of biomass-based composites in agricultural scenarios, focusing on the breakthroughs in the three core areas of intelligent protection of seed coating, soil microcosm regulation, and foliar environment-responsive delivery. Through an in-depth analysis of the efficiency mechanism of composites on insecticides, antimicrobials, and herbicides, this review elucidates the scientific pathway of pesticide delivery through interfacial modification, slow-release kinetic modulation, and multilevel structural design, which will provide theoretical support and a practical paradigm for the development green agricultural technology. Full article

This review provides a critical overview of MXenes, an innovative class of 2D transition metal carbides, nitrides, and carbonitrides, emphasizing their synthesis, properties, and application potential. We systematically examine synthesis methods, contrasting top-down approaches with emerging green alternatives and bottom-up techniques, evaluating each in terms of scalability, cost, and environmental impact. This paper highlights MXenes’ unique characteristics, including high electrical conductivity, tunable surface chemistry, and structural versatility, which enable their use in energy storage, environmental remediation, biomedicine, and electromagnetic shielding. Key challenges such as oxidative instability, interfacial incompatibility, and hazardous etching processes are critically discussed. We identify future research priorities, including defect-engineered stabilization, AI-optimized manufacturing, and advanced integration protocols to bridge the gap between laboratory breakthroughs and industrial deployment. By integrating these insights, this review offers a roadmap for advancing MXenes from laboratory innovation to industrial application. Full article

Perovskite solar cells, as a rising star in third-generation photovoltaic technologies, have attracted extensive attention due to their high light absorption, tunable bandgap, and high power conversion efficiency, indicating substantial potential for future applications. Starting from the development history of perovskite solar cells, this review systematically comprehends the technological breakthroughs in the continuous improvement of power conversion efficiency since their invention, outlining the research status and technical bottlenecks. A detailed analysis is provided on the material characteristics and limitations of the lead-based perovskite systems. Critical obstacles towards commercialization are also identified, such as operational instability and the challenges associated with large-scale manufacturing. Finally, the potential role of machine learning in the discovery and design of new perovskite materials is highlighted, and future development directions have been outlined. Special focus is placed on the innovative applications of machine learning in material composition screening, material properties prediction, and process parameter optimization, with the aim of constructing a closed-loop research framework. The review aims to offer valuable insights and references for advancing the performance and industrial applications of perovskite solar cells. Full article

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article