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Beyond Resolution: Recent Progress and Industrial Challenges of Nanoimprint Lithography for Advanced Semiconductor Manufacturing
The relentless downscaling of semiconductor devices has pushed conventional lithography toward both economic and physical limits. Deep Ultraviolet (DUV) lithography with multi-patterning schemes causes exponential cost escalation, while Extreme Ultraviolet (EUV) lithography imposes prohibitive capital investment exceeding US$150 million per scanner and megawatt-scale power consumption. Within this landscape, nanoimprint lithography (NIL) has re-emerged as a compelling complementary patterning technology. Unlike projection-based methods, NIL achieves pattern definition through mechanical contact and material displacement. Therefore, rendering it bypasses optical diffraction limits and stochastic effects caused by photo-shot noise. This review provides a systematic evaluation of the transition of NIL from laboratory demonstration to semiconductor high-volume manufacturing (HVM), with emphasis on progress made during 2023-2025. We highlight the paradigm shift from resolution-centric studies to manufacturing-driven metrics including overlay accuracy, defect density, throughput, template lifetime, residual layer uniformity, and cost of ownership. We then examine recent advances across six key technology modules: tool architecture, overlay improvement, defect suppression, template ecosystems, resist engineering, and application-specific integration. We argue that NIL is positioned as a high-value strategic complement targeting specific layers in DRAM, 3D NAND, and advanced packaging, where sub-10 nm resolution, three-dimensional patterning capability, and order-of-magnitude lower cost-of-ownership provide decisive advantages. The review concludes by projecting potential trajectory of NIL through 2028, identifying template ecosystem maturation, artificial intelligence-driven process optimization, and hybrid mix-and-match lithography strategies as critical factor for adoption.
Copyright (c) 2026 Shuhao Si, Yuexu Si, Chen Cheng (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Analysis of Methods to Break Through the Free Spectral Range Limitation
The Free Spectral Range (FSR) is a key intrinsic parameter of interferometric and resonant photonic devices, and it has a trade-off relationship with the device sensitivity and dynamic range of measurement. This trade-off has become a critical bottleneck that limit to improve the performance of fiber optic sensing, integrated photonic filtering, and microcavity optoelectronic devices. Therefore, it has becomean an important research direction in photonic sensing and integrated photonics to break through the FSR limitation while maintaining both high sensitivity and large measurement range. This paper systematically reviews four major technical routes: demodulation algorithms, deep learning approaches, structural design, and material selection and properties. For each route, the implementation principles, research progress, and performance advantages of representative schemes are discussed. Finally, the current challenges are summarized, and the development trend toward multi-technology integration is envisioned.
Copyright (c) 2026 Hao Mi, Zhen Zhang, Ruiting Li, Faqian Liu, Mengchao Gao, Hongdi Wang, Qicheng Gai, Duo Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Two-Stage Data Mining and Randomized Controlled Trial Approach to Evaluate the Gouteng Xiakucao Formula for Essential Hypertension Comorbid with Anxiety Disorder of Liver-Yang Hyperactivity Pattern
Essential hypertension (EH) comorbid with anxiety disorder lacks optimal pharmacological options, and Traditional Chinese Medicine (TCM) formula construction has historically relied on classical theory rather than systematic data-driven evaluation. We addressed this gap through a two-stage workflow. Stage 1 was a retrospective data mining analysis of 114 EH–anxiety patients combining association rule, factor, and hierarchical cluster analysis of TCM prescriptions; it identified Liver-Yang Hyperactivity (LYH) as the predominant syndrome (52.63%) and Gouteng–Xiakucao as the highest-confidence herb pair (support 50.88%, confidence 94.83%), and anchored construction of the Gouteng Xiakucao Formula (GXF). Stage 2 randomized 128 patients with EH–anxiety comorbidity of LYH pattern 1:1 to valsartan plus GXF or valsartan plus oryzanol for 14 days. The GXF arm showed significantly greater post-treatment reductions than control in systolic blood pressure (134.03 ± 8.17 vs 137.18 ± 6.92 mmHg, P = 0.026), diastolic blood pressure (80.93 ± 3.88 vs 83.42 ± 4.31 mmHg, P = 0.001), Hamilton Anxiety Rating Scale score (11.63 ± 2.66 vs 12.76 ± 3.08, P = 0.036), Pittsburgh Sleep Quality Index (P = 0.015), and TCM syndrome score (P = 0.002); the SF-36 Physical Functioning, Social Functioning, and Mental Health subscales also improved more in the GXF arm, with no clinically meaningful adverse changes. This data-driven TCM formula derived from real-world prescription mining produced superior short-term outcomes compared with an oryzanol-based comparator in patients with EH–anxiety comorbidity of LYH pattern, illustrating a feasible paradigm for systematic TCM formula development and prospective validation.
Essential hypertension comorbid with anxiety
Copyright (c) 2026 Xinyu Wang, Xiangyu Su, Yu Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Research Progress in System Construction and Image Processing Algorithms of Optical Coherence Tomography (OCT)
Abstract:As a non-invasive, high-resolution tomographic imaging technique based on the principle of low-coherence interferometry, Optical Coherence Tomography (OCT) has established a pivotal position in biomedical clinical diagnosis, industrial non-destructive testing and other fields. The core trajectory of its technological evolution is manifested in the shift from the time domain to the Fourier domain. In particular, Spectral Domain OCT (SD-OCT) and Swept Source OCT (SS-OCT) have achieved an order-of-magnitude improvement in imaging speed and sensitivity by virtue of their parallel detection architecture. The current development of OCT technology has always revolved around two core dimensions: system hardware construction and image processing algorithms. At the hardware level, existing technologies have achieved continuous optimization of resolution and imaging depth, but have been unable to break through the intrinsic constraints of core performance under the classical optical framework; at the algorithm level, the technical system has completed the transformation from model-driven to data-driven, yet still faces the core bottleneck of translating laboratory performance into clinical practical value. This paper systematically reviews the research progress of OCT system construction and image processing algorithms, deeply analyzes the unsolved core bottlenecks in the two dimensions, clarifies the core path of future technological breakthroughs, and provides a reference for the basic research and clinical translation of OCT technology.
Copyright (c) 2026 Zhen Zhang, Hao Mi, Ruiting Li, Faqian Liu, Mengchao Gao, Duo Chen, Qicheng Gai, Hongdi Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Mini Review: Crystal growth from liquid and vapor phases
Crystalline materials at both the macro- and nanoscale have attracted growing interest due to their competitive advantages in cutting-edge applications. This literature review examines two representative crystal growth techniques—the heat exchange method and chemical vapor deposition—along with their prominent applications: high-quality bulk crystalline silicon and transition metal dichalcogenide (TMDs) crystalline nanofilms. The review provides an overview of the general mechanisms, the developmental trajectories of each method, their current status, and future prospects, with the aim of understanding the associated challenges, limitations, and emerging opportunities.
Copyright (c) 2026 Yujia wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Critical Review on Tensile Behavior and Johnson-CookConstitutive Model Calibration of Wire Arc Additive Manufactured Materials
Wire arc additive manufacturing (WAAM) has emerged as a key near-net-shape fabrication route for large-scale aerospace metallic components, offering deposition rates of 3-10 kg/h and material utilization exceeding 90%. However, the non-equilibrium microstructures formed under repeated WAAM thermal cycling — comprising coarse columnar grains, heterogeneous Laves phase, Widmanstatten lamellar alpha, and gradient in-situ aging precipitates — render the mechanical behavior of as-deposited materials fundamentally distinct from wrought counterparts. This disparity gives rise to a systematic 'parameter transplantation fallacy' in engineering simulations that rely on Johnson-Cook (JC) constitutive parameters calibrated from forged stock. The present review systematically examines 69 representative studies published between 2009 and 2026, covering three representative WAAM structural materials: Inconel 718, Ti-6Al-4V (TC4), and 18Ni300 maraging steel. A causal framework linking microstructure formation, tensile behavior, and JC calibration is established. Methodologically, this review proposes a new anisotropy classification taxonomy — texture-precipitate coupling type (IN718), grain boundary-texture type (TC4), and phase distribution-thermal history type (18Ni300) — and builds a cross-material three-tier error priority framework quantifying state-jump error, anisotropy error, and model calibration error. Key findings are: (i) classical JC models exhibit systematic prediction errors of 10-30% for WAAM materials at high strain rates (>1000 s-1) and elevated temperatures (>600 degrees C); (ii) for WAAM IN718, the state-jump error (~42%) far exceeds the anisotropy error (~14%) and model error (<2%), establishing as-deposited-specific parameter databases as the highest-priority research task; (iii) all five JC parameters remain entirely uncharacterized for WAAM as-deposited 18Ni300, representing the most critical knowledge gap in the field. A research roadmap with specific experimental matrices is proposed for near-, mid-, and long-term research directions.
Copyright (c) 2026 Xin Liu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Time-Frequency and Performance Analysis to Explore Neurocognitive Differences in Solving Redox Reaction Problems Across Academic Levels
Redox reactions represent a core yet challenging topic in high school chemistry, imposing significant cognitive demands on students. However, traditional behavioral measures are limited in their ability to uncover the cognitive processes underlying these learning difficulties. This study employed electroencephalography (EEG) alongside subjective cognitive load assessments to investigate the neurocognitive mechanisms differentiating high- and low-achieving students as they solved redox problems of varying difficulty. Thirty tenth-grade students participated in a mixed-design experiment. EEG signals and NASA-TLX subjective load ratings were recorded as participants solved simple and difficult redox problems. Time-frequency analysis was used to extract neural oscillatory features within the theta (4–8 Hz) and alpha (8–13 Hz) bands. Neurophysiological data revealed that low achievers exhibited stronger theta-band event-related synchronization (ERS), suggesting higher cognitive load during early working memory and control processes. In contrast, high achievers demonstrated stronger alpha-band event-related desynchronization (ERD), suggesting more efficient attentional allocation and information processing. These neural activation patterns differed significantly both between groups and across difficulty levels, a finding consistent with subjective load ratings from the NASA-TLX scale. This study provides neurocognitive evidence elucidating the sources of learning difficulties in chemistry. The findings offer implications for the design of targeted cognitive supports and differentiated instruction.
Copyright (c) 2026 Nian Wang, Junhan Zhang, Zaimiao Li, Chi Cheng, Jianqiang Ye (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Challenges and Emerging Solutions in Coal Analysis Technology Based on Laser-Induced Breakdown Spectroscopy
Laser-Induced Breakdown Spectroscopy (LIBS) is a novel type of atomic emission spectroscopy. It has the advantages of rapid detection and no sample pretreatment, and can simultaneously detect the characteristics of multiple components. Therefore, it has great application potential in the analysis of the coal industry. However, due to the intricate compositional characteristics of coal, significant spectral discrepancies exist among different measurement results, while notable spectral similarities are observed across distinct coal types. These phenomena pose challenges to the accuracy and reliability of coal analysis outcomes. The paper summarizes the key factors that influence the performance of LIBS in coal analysis, such as the redundancy of spectral features, matrix effects, self-absorption effects, or environmental factors in online detection. To address these issues, this paper introduces methods for improving classification accuracy and quantitative analysis precision, such as feature selection algorithms, machine learning models for classification, multifactor quantitative methods, spectral preprocessing techniques, and transfer learning approaches. It also presents novel techniques for reducing spectral fluctuations during online detection and enhancing the generalization capability of online models. Overall, the advancement of LIBS-based coal analysis technology relies on the innovative integration of spectroscopy, chemometrics, system integration, and other relevant aspects to meet the demands of high-precision and high-reliability industrial online analysis.
Laser-induced breakdown spectroscopy (LIBS)
Copyright (c) 2026 Guangtao Fu, Ruizhan Zhai, Rongzhou Zhang, Rongxin Ma, Chunling Dang, Bingxu Yang, Minzhe Liu, Kun Zhao, Yongjing Wu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Application of laser-induced breakdown spectroscopy for heavy metal detection in aqueous solutions
Water constitutes the essential basis for sustaining life. With the escalating contamination of global water resources, there exists a critical and immediate demand for a methodology enabling rapid, precise, and comprehensive elemental detection in aqueous solutions containing elevated concentrations of heavy metals. Traditional detection methods are plagued by limitations including intricate sample pretreatment procedures, prolonged detection cycles, and a heavy reliance on specialized laboratory conditions, thereby posing challenges for achieving micro-scale, in-situ, and dynamic distribution detection and analysis. Laser-Induced Breakdown Spectroscopy (LIBS) technology has emerged as one of the premier techniques for detecting heavy metals in aqueous solutions, owing to its distinctive advantages, including the elimination of complex sample preparation, simultaneous multi-element detection, rapid analysis, and the capability for remote in situ operation. However, the direct detection of aqueous solutions using LIBS suffers from issues such as liquid splashing, plasma quenching, inhomogeneous elemental distribution, and laser energy loss, resulting in insufficient detection sensitivity, accuracy, and stability. This paper systematically reviews the key technological advances of LIBS in the detection of heavy metals in aqueous solutions and comprehensively summarizes the principles, advantages, application performance, and representative research results of various technical routes from four core perspectives: sample pretreatment, detection system optimization, data processing and combined techniques. Meanwhile, the challenges faced by LIBS in the determination of heavy metals in aqueous solutions are concluded, and its future development trends toward miniaturization, intellectualization, and multi-technology integration are prospected. This review aims to provide a comprehensive reference for the further optimization and practical application of LIBS in the monitoring of heavy metal pollution in aquatic environments.
Copyright (c) 2026 Rongxin Ma, Huikun Tian, Xiangming Kong, Jianfei Li, Duo Chen, Bingxu Yang, Rongzhou Zhang, Guangtao Fu, Chunling Dang, Wenhao Zhang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Electrolyte Density Detection using Plasma-Modified FP Cavity Sensor for Lead-Acid Battery Health Management
In-situ density monitoring of lead-acid battery electrolytes faces challenges from bubble adhesion, turbidity, and high temperatures. This study presents a fiber optic Fabry-Perot (FP) sensor featuring an oxygen plasma-modified superhydrophilic probe encased in a 300-mesh nylon package. The superhydrophilic surface prevents bubble adhesion by promoting rapid liquid spreading, while the nylon mesh filters suspended impurities and mitigates hydrodynamic turbulence. Experimental results demonstrate excellent stability over 19 days at 45°C, with fluctuations ≤0.005 g/cm³. Under conditions of vigorous bubble evolution and impurity stirring, the composite design reduces measurement error from >0.05 g/cm³ to <0.01 g/cm³. This robust sensing scheme offers a reliable solution for the life-cycle health management of lead-acid batteries.
Copyright (c) 2026 Ruiting Li, Faqian Liu, Mengchao Gao, Duo Chen, Hao Mi, Zhen Zhang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Surface Roughness Analysis of Low-cost Metal Material Extrusion Fabricated Parts and Prediction by Machine Learning Methods
Additive Manufacturing (AM), also known as 3D Printing (3DP), is a widely used layer-by-layer manufacturing process, it is evolving rapidly both in research and industry. Among all AM methods, Material Extrusion (ME) is one of the most popular techniques. Based on ME, another new AM method is developed, which is Low-cost Metal Material Extrusion (LCMME). In this newly developed process, pure metallic parts could be fabricated after sintering the metal infused additively manufactured parts. Both AM and sintering process parameters will have influence on the quality of the final pure metallic parts. In this research, several statistics methods were used to analyze the data gotten from the experiment. Then two Machine Learning (ML) algorithms were used to predict the Surface Roughness (SR) of the final specimens. Additionally, the results show that the neural network (NN) is more accurate than the support vector regression (SVR) on prediction.
Additive Manufacturing (AM), Surface Roughness (SR), Low-cost Metal Material Extrusion (LCMME), Machine Learning (ML)
Copyright (c) 2025 Zhicheng Zhang, Lu Shi, Hao Wang, shuai wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Global Research Landscape and Evidence Gaps in Diet-Gut Microbiota Science:A Bibliometric and Integrated Data Analysis
Abstract Background: Dietary pattern-gut microbiota research has expanded rapidly over the past decade, driven by growing recognition of the gut microbiota as a key biological mediator linking diet to health. While substantial evidence has accumulated, the overall structure, balance, and global distribution of this evidence particularly its alignment with nutritional needs and progression toward mechanistic understanding remain insufficiently examined. Methods: We conducted a multi-level bibliometric and data-integrated analysis of dietary pattern-gut microbiota research published between 2010 and 2025 using the Web of Science Core Collection. Bibliometric indicators were combined with external measures of nutritional burden and data infrastructure to assess research output, collaboration patterns, thematic evolution, evidence depth, and cross-country evidence inequality. A composite Evidence Inequality Index and a joint growth citation framework were used to identify structural imbalances and emerging research gaps. Results: Annual publication output increased markedly over time, with a transition from exploratory growth to rapid expansion after 2018. Research activity and collaboration networks were highly centralized, with output concentrated in a small number of high-income countries and institutions. Globally, research output showed weak alignment with nutritional burden, with countries facing high undernutrition frequently under-represented in the literature. Evidence depth varied substantially even among countries with similar publication volume, reflecting uneven progression toward mechanistic and data-intensive research. Thematic analyses revealed stable core research areas alongside rapidly expanding topics characterized by limited citation consolidation, indicating transitional stages of evidence development rather than mature research cores. Conclusions: Dietary pattern-gut microbiota research is structurally imbalanced in relation to nutritional need, evidence depth, and thematic maturity. The proposed framework provides a basis for prioritizing more equitable and impactful research directions.
Copyright (c) 2025 Ying Liu, Han Qiao, Guixiang Yao (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Sculpting Light in Three Dimensions: Advances in Longitudinal Control of Optical Vortex Beams
The precise longitudinal control of structured light, particularly the class of helical beams known as optical vortices, represents a significant and rapidly advancing frontier in modern photonics. By strategically and intricately modulating the core properties of light, including amplitude, phase, and polarization, in the transverse plane, their unique and complex spatial structures can be meticulously engineered to exhibit remarkable and highly useful behaviors as they propagate through space. This review is dedicated to systematically summarizing and contextualizing the pivotal progress achieved over the past five years within the expansive and dynamic field of the longitudinal engineering of vortex light fields. We categorize and analyze the field's most impactful advancements through the four key technological platforms, each with a unique profile of capabilities: metasurfaces, which offer unprecedented, subwavelength-resolution control over the wavefront; spiral phase plates, providing robust and high-fidelity static phase control; structured optical arrays, which masterfully utilize the principles of diffraction and interference; and liquid-crystal spatial light modulators (LC-SLMs), which grant unparalleled capabilities for dynamic, real-time beam shaping. For each of these distinct platforms, we delve deeply into their fundamental operating principles, examine their sophisticated and often computationally intensive design methodologies, and highlight their most salient and impactful applications. This is accompanied by a critical and balanced discussion of their respective advantages, such as efficiency, compactness, or tunability, and their inherent limitations. To conclude, we project our view forward, outlining the most promising future research directions, identifying the key technological and scientific challenges that must be overcome, and speculating on the potential for paradigm-shifting breakthroughs. This work aims to provide a comprehensive, detailed, and didactic reference for both the fundamental physics and the burgeoning applications of longitudinal optical control, thereby inspiring and catalyzing further innovation in fields such as multi-plane super-resolution imaging, coordinated multi-particle optical micromanipulation, ultra-high-capacity optical communications, robust quantum information science, and other emerging technological arenas.
Copyright (c) 2025 Shirong Liu, Li Ma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Developmental Patterns of Artificial Intelligence Research in Geriatric Diseases: A Bibliometric Analysis of Growth and Evidence Depth
The rapid adoption of artificial intelligence (AI) in geriatric medicine has led to substantial growth in related research, yet the overall structure and developmental patterns of this field remain incompletely understood. In this study, we conducted a comprehensive bibliometric analysis of AI-based research in geriatric diseases published between 2006 and 2025. Publications were retrieved from the Web of Science Core Collection and analyzed across multiple dimensions, including annual publication trends, geographic and institutional contributions, collaboration networks, thematic structure and evolution, alignment between research activity and aging-related disease burden, and the relationship between research growth and citation-based evidence depth. The results demonstrate a clear stage-dependent development pattern, characterized by rapid expansion in publication output in recent years accompanied by declining average citation impact. Research activity is highly concentrated among a limited number of countries, institutions, and authors, forming a centralized collaboration structure. Thematic analyses reveal a gradual shift from early neuroimaging focused and disease-centered studies toward more method-driven and application-oriented research, with Alzheimer’s disease and machine learning remaining dominant organizing cores. However, many rapidly growing topics exhibit limited citation depth, indicating exploratory or transitional stages of development rather than consolidated research cores. In addition, the geographic distribution of research output shows marked heterogeneity and does not consistently align with global patterns of aging-related disease burden. Overall, this bibliometric study highlights substantial structural heterogeneity within geriatric disease-AI research, reflecting uneven thematic maturity, concentrated knowledge production, and variable evidence consolidation. These findings provide a quantitative perspective on the current developmental stage of the field and may inform more balanced and integrative future research efforts.
Copyright (c) 2025 Bin Yu, Xue Zhang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Lightweight-based Intrusion Event Recognition Method for Distributed Fiber Optic Vibration Sensing
When Distributed Fiber Optic Vibration Sensing (DVS) systems are used for perimeter security intrusion detection in complex industrial environments, existing recognition models suffer from insufficient adaptability, high computational overhead, and high latency. This paper proposes the MNV4-LDA end-to-end recognition method, which integrates the lightweight feature extraction capability of MobileNet-V4 (MNV4) with the advantages of supervised dimensionality reduction of Linear Discriminant Analysis (LDA), constructing a framework from feature enhancement to dimensionality reduction and then to classification. The experiment targets three types of events: background noise, mechanical operations, and human walking. Tests based on 6000 groups of datasets show that the number of parameters of this method is reduced from 632.22M to 2.38M, with a reduction rate exceeding 99.6%. The recognition accuracy reaches 98.28%, 4.67% higher than that of MNV4, and the single-sample inference time on the CPU is 127.0ms. Moreover, no denoising preprocessing is required. This method effectively solves the edge deployment challenges of traditional models, balances efficiency and stability, and provides a practical solution for the edge application of DVS systems in perimeter security.
Distributed Fiber Optic Vibration Sensing
Copyright (c) 2025 Zhaohai Li, Zhenzhen Zhang, Jiaxing Tang, Sheng Huang, Wenan Zhao, Chen Wang, Ying Shang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Optimization of heating wire arrangement in MOCVD on quadratic regression orthogonal rotational combination design
AlGaN and AlN, III-nitride semiconductors distinguished by wide bandgaps, strong polarization, and exceptional chemical stability, hold substantial potential for applications in high power and high frequency electronics, high temperature sensors, and optoelectronic devices. These materials thus serve as a cornerstone for next generation technologies. Metal organic chemical vapor deposition (MOCVD) is the preeminent technique for synthesizing high quality AlGaN and AlN films, with vertical showerhead reactors being widely employed due to their efficiency and stability. This study focuses on optimizing the arrangement of heating wires within an MOCVD reaction chamber. Using a quadratic regression orthogonal rotation combination design, we investigated the effects of heating wire width and spacing on temperature distribution within the reaction cavity. By univariate analysis and quadratic regression modeling, we identified the optimal combination of heating wire width and spacing to achieve uniform and stable temperature distribution. Our results show that the heating wire gap has the most significant impact on the average temperature of the mixing chamber, followed by the heating wire width and their interaction effects. The optimized heating wire arrangement markedly enhances the average temperature of the reaction chamber, thereby providing an improved thermal environment for the growth of high quality AlN films. This research offers both theoretical insights and practical guidance for the design and optimization of MOCVD reactors.
Copyright (c) 2025 Chao Li, Hui Li, Fan Zhang, Kailei Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Review of Gold Nanoparticle Used in Biological Application: Bacterial Infection, Cancer Treatment, and Cytotoxicity Study
Gold nanoparticles have been studied extensively for various medical applications due to their strong capability for cancer treatment without drugs and their equally strong, but newly identified, potential in the anti-bacterial field. Due to their ease of synthesis, chemical stability, activation by light, and controllable toxicity, gold nanomaterials have attracted enormous interest as novel biomaterials, especially for cancer patients who have an increased rate of infection due to a compromised immune system. This review discussed how gold nanoparticle used in the anti-bacterial and anti-cancer fields, as well as their prospect in further medical research to combine such multi-properties into one kind nanoparticle. Given the adverse impacts of the pandemic spanning 2020 to 2022, a considerable number of research projects were either interrupted or prematurely terminated. As a direct consequence, this review solely encompasses the research progress attained prior to the onset of the pandemic. Notably, the core unresolved issues within this research domain—with specific focus on topics centered around gold nanoparticles—continue to be the subject of ongoing investigation to the present day.
Copyright (c) 2025 Yujia Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Gut Trimethylamine Cycle: Microbial Ecology, Quantitative Metabolic Flux, and Therapeutic Targeting
Trimethylamine (TMA) and its hepatic oxidation product, trimethylamine N-oxide (TMAO), form a pivotal gut–liver metabolic axis linking the intestinal microbiota with host cardiometabolic health. Elevated plasma TMAO levels have been consistently associated with atherosclerosis, chronic kidney disease, diabetes, and other metabolic disorders. This review systematically summarizes current advances in understanding the microbial ecology and biochemical pathways underlying the gut TMA cycle. We highlight three major microbial routes for TMA generation—cutC/D-mediated choline cleavage, cntA/B-dependent carnitine oxidation, and BbuA-associated γ-butyrobetaine reduction - as well as methanogenic and acetogenic pathways responsible for TMA utilization and detoxification. Integrating multi-omics data with metabolic-flux modeling provides quantitative insight into the gut–liver TMA network, revealing key rate-limiting nodes and interindividual variability driven by diet and microbial composition. Emerging intervention strategies include chemical inhibition of cutC/D, cntA/B activity, dietary modulation, probiotic or engineered consortia enhancing TMA clearance, and bacteriophage-based precision targeting of TMA-producing taxa. Finally, we propose developing a gut TMA index and mapping producer–degrader interaction networks as frameworks for personalized risk evaluation and therapeutic design. Collectively, quantitative and translational studies of the TMA cycle are expected to establish new paradigms for microbiome-driven prevention and treatment of cardiometabolic diseases.
Copyright (c) 2025 Yangyang Zhao, Xin Xing (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Preparation and Mechanism Study of Biochar based Mortar Composite Materials
This study investigated the mechanical properties and hydration mechanism of cement-based composites incorporating rice husk biochar (RB) and corn straw biochar (CB) under standard curing conditions of 28 days. The compressive strength, flexural strength, and total calcium hydroxide generated during hydration of the cement-based composites were studied using simultaneous thermal analysis (TG-DTG), X-ray diffraction (XRD), mercury intrusion porosimetry (MIP), scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), and attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR). The results showed that when the incorporation of RB and CB was 4%, both the compressive and flexural strengths were significantly improved. The incorporation of RB and CB did not generate new hydration products or change the total amount of calcium hydroxide generated during hydration. On the contrary, refining the internal pore structure of the cement significantly improved the mechanical properties of the cement-based materials. These findings indicate that biochar materials prepared from agricultural and forestry waste represent a valuable new development strategy for improving the performance of cement-based materials.
Copyright (c) 2025 Ning Song, Guangqi Hu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Development and Validation of a Predictive Model for Glycemic Response to Dorzagliatin in Type 2 Diabetes: A Retrospective Real-World Study
Patient response to dorzagliatin, a novel glucokinase activator for type 2 diabetes mellitus (T2DM), exhibits significant heterogeneity in clinical practice, posing a challenge for precision medicine. This study aimed to develop and validate a predictive model for identifying optimal responders prior to treatment initiation. We conducted a retrospective cohort study involving 300 T2DM patients hospitalized between April 2024 and April 2025. Patients were categorized into a responder group (n=146, fasting/postprandial glucose reduction >0.5 mmol/L) and a non-responder group (n=154, reduction <0.3 mmol/L). Multivariable logistic regression and random forest machine learning algorithms were employed. Key predictors identified included diabetes duration, HOMA-β index, and use of GLP-1 receptor agonists and metformin. The logistic regression model demonstrated high predictive accuracy (87.0%), while the random forest model achieved an AUC of 0.99 in the test set. A comprehensive model integrating the most significant variables yielded a superior AUC of 0.900. This clinically applicable prediction model facilitates the pre-treatment identification of patients most likely to benefit from dorzagliatin, thereby advancing personalized T2DM management.
Copyright (c) 2025 Yue Liu, Jia Liu, Zhongqing Yan, Ying Chen, Liping Han (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Dual-Ring Based Surface Plasmon Polaritons MIM Waveguide for High-Sensitivity Air Sensor
Metal-insulator-metal (MIM) waveguides supporting surface plasmon polaritons (SPPs) offer a promising platform for refractive index sensing in air. However, limited sensitivity remains a major constraint for practical air-sensing applications. To obtain a high-sensitivity sensor, we propose a SPPs MIM waveguide sensor incorporating a tangent dual-ring resonator integrated with gain structure and waveguide strips. The proposed structure, fabricated on a silver substrate with air as the dielectric core, is systematically investigated using finite-difference time-domain (FDTD) simulations. By optimizing the structural parameters of the sensor model, the resonator exhibits continuous shifts in resonant wavelength as the refractive index varies from 1.00 to 1.05. A maximum sensitivity of 3540 nm/RIU is achieved. The findings demonstrate the potential of the designed sensor for highly accurate air quality monitoring, supporting the development of more effective environmental conservation strategies.
Copyright (c) 2025 Fei Zhao, Ting Cui, Wenxian He, Miaomiao Chen, Huili Pei, Meng Zhang, Li Ma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Development of a Measurement Instrument for High School Students’ Chemical Science Capital
Chemical science capital refers to a collection of resources that create use or exchange value for individuals or groups, supporting and enhancing chemical achievements and engagement in chemistry. Following the general procedures for developing science education measurement instruments and drawing on existing research on science capital, this study constructed an evaluation framework for chemical science capital. The framework comprises four primary dimensions—material capital, social capital, institutional capital, and cultural capital—along with nine secondary dimensions. Based on this framework, a measurement instrument for high school students' chemical science capital was developed. Guided by item response theory (IRT) and the fundamental assumptions of the Rasch model, this instrument underwent pilot testing, quality validation, and revisions, resulting in a robust measurement tool for evaluating high school students' chemical science capital.
Copyright (c) 2025 Chuan Li, Yu Guo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Since its launch in March 2025, X-Disciplinarity has embraced a bold vision, dedicated to promoting interdisciplinary research to address complex global challenges. In just three issues, the journal has received over 100 submissions and published 25 high-quality articles across a wide range of fields, including science, engineering, medicine, and education. These articles have garnered over 500,000 visits and numerous citations, earning it widespread global recognition. To support the continued expansion of its content, the journal has restructured its editorial structure. A new Editorial Director Board, comprised of three distinguished Editors-in-Chief, guides strategic direction and maintains scholarly rigor. A strengthened multi-level review process—including initial screening, expert evaluation, peer review, and final approval by at least two members of the Editorial Director Board—ensures the journal's quality and integrity. The journal also emphasizes community building, incorporating doctoral students as observers and interns into the review process to cultivate the next generation of scholars. X-Disciplinarity is committed to openness and innovation, inviting researchers from around the world to contribute to a collaborative intellectual platform dedicated to transformative, interdisciplinary dialogue.
Incorporating Modern Social Media into the Classroom to Enhance Students' Conceptual Understanding in Science Education: A Quasi- Experimental Study
The digital age has significantly transformed information dissemination, positioning modern social media as a key platform for student engagement in educational contexts. Despite its advantages, the diverse, informal, and interdisciplinary nature of modern social media content presents cognitive challenges for students. This study proposes an approach to integrating modern social media as a pedagogical tool by utilizing its contemporary features to develop a comprehensive instructional Strategyfor teaching scientific concepts. The model aims to guide students in critically examining diverse modern social media perspectives through a sociological lens. It facilitates the acquisition of solid scientific concepts, enhancement of critical thinking skills, and accurate interpretation of modern social media narratives via independent inquiry, evaluation of scientific evidence, and perspective reframing. The study employed a quasi-experimental design in four middle school classes, comprising an intervention group (N=44) and a control group (N=52). Results revealed a significant positive effect of the model on students' conceptual understanding and reasoning skills.
Copyright (c) 2025 Zengze LIU, Yuqing Hou, Youye Ding, Huiling Xu, Sudong Pan, Baoxin Huang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Comparison of Visual Representations in Junior Secondary School Geography Textbooks on Continental Drift and Plate Tectonics
This research examines 39 visual representations from junior secondary school geography textbooks in China, the US, and the UK, themed “Continental Drift and Plate Tectonics”. This study employs content analysis and the revised Graphical Analysis Protocol (GAP) to evaluate graphics and their integration with text. Two indicators are considered while assessing graphics: Systematicity and Graphic form. The integration of graphics with text is evaluated based on five indicators: Contiguity, Indexical reference, Captions, Semantic relations, and Level of connection. The findings indicate that the geographic schematic map is the primary graphic form utilized in all textbooks. Graphics often exhibit a low degree of systematicity, and most visual representations depict representational semantic relations. PEP and MH demonstrate significant parallels regarding contiguity and indexical reference, with graphics physically associated with related text, and the text includes references to the graphics. Only OUP employs engaging captions.
Copyright (c) 2025 X-Disciplinarity https://creativecommons.org/licenses/by-nc-nd/4.0
Gut Microbiota and Thyroid Diseases: A Comprehensive Review of Mechanisms and Clinical Implications
The interplay between gut microbiota and thyroid diseases has emerged as a cutting-edge topic in endocrinology and microbiome research. Accumulating evidence indicates that gut microbes not only influence thyroid hormone synthesis and metabolism through metabolites such as short-chain fatty acids and bile acids, but also play critical roles in immune homeostasis, thereby contributing to the pathogenesis and progression of Graves' disease, Hashimoto's thyroiditis, thyroid dysfunction, and thyroid cancer. Epidemiological and clinical studies suggest that specific microbial signatures are associated with disease risk, subtype classification, therapeutic response, and recurrence, highlighting their potential as diagnostic and prognostic biomarkers. Emerging interventions, including probiotics, dietary modulation, and fecal microbiota transplantation, have shown promising effects in animal models and preliminary clinical studies, offering new opportunities for personalized management. Nevertheless, current research faces notable challenges: causal relationships remain unclear, disease heterogeneity and population differences compromise reproducibility, and biases inherent to low-biomass samples and sequencing methodologies limit evidence strength. Future investigations should focus on standardized protocols, cross-cohort validation, multi-omics integration, and mechanistic trials to elucidate the biological basis of the “gut–thyroid axis” and accelerate its translation into clinical diagnostics, risk stratification, and therapeutic interventions.
Longitudinal Manipulation of Optical Lattices Based on Six-Aperture Spiral Array
Optical Lattices are evolving from an information carrier to a programmable medium in which topology, structure and propagation are dynamically coupled. Here we investigated longitudinal manipulation of optical lattices generated by a six-aperture spiral array whose azimuthal phase is imprinted with a topological charge l. By varying l from 1 to 7 in simulations, we sculpted the longitudinal lattices into Hexagonal, Kagome and Honeycomb structures whose transverse phase profiles are uniquely encoded by topological phase configurations. The results demonstrated that the phase profiles execute a longitudinal pulsation, while all higher-order vortices converge to a stable zero state after a geometry-dependent length z. During the longitudinal variations, the measured phase-lattice exhibits the correspondences of 6π-Honeycomb, ±4π-Kagome, and ±2π-Hexagon, enabling predictive design of reconfigurable photonic crystals and topologically protected registers. Our results establish the six-aperture spiral as a compact diffractive engine for coherent lattice reconfiguration and open a route to longitudinal light-matter interfaces.
Mapping the Intersections of Physical Activity and Gut Microbiota: A Global Bibliometric Analysis of Research Patterns, Gaps, and Emerging Trends (2010–2025)
The interplay between physical activity and gut microbiota has attracted growing attention across disciplines ranging from microbiology to behavioral science. While studies increasingly suggest that exercise can modulate gut microbial diversity and function, the broader landscape of this research field—its development trajectory, collaboration patterns, and thematic evolution—remains unclear. In this study, we conducted a comprehensive bibliometric analysis of publications from 2010 to 2025, aiming to clarify the field’s structural characteristics and identify key knowledge gaps. Our findings reveal a rapidly expanding body of work, with publication surges after 2017 and concentrated outputs from a limited number of countries, institutions, and journals. Network analysis uncovered regionally imbalanced collaborations, while keyword mapping showed a transition from compositional profiling to mechanistic exploration, yet limited coverage of diverse exercise types, populations, and cultural contexts. These results underscore the need for broader, more inclusive research frameworks to guide future empirical and translational efforts in this evolving domain
Nanocrystal and Quasi-2D Perovskites Gain Media: From ASE to Laser
Metal halide perovskites have emerged as revolutionary optical gain media, challenging conventional semiconductor lasers with their combination of solution processability, tunable emission, and exceptional optoelectronic properties. This review systematically explores recent advances in perovskite-based amplified spontaneous emission (ASE) and lasing technologies, focusing on low-dimensional systems—nanocrystal (NC) and quasi-two-dimensional (quasi-2D) perovskites. We first elucidate the fundamental photophysical principles governing ASE in these materials, contrasting the quantum confinement-dominated gain in NCs with the exciton funneling mechanism in quasi-2D structures. This is followed by a critical analysis of materials engineering strategies, highlighting how ligand/solvent/additive engineering, phase control, and plasmon coupling work together to suppress nonradiative losses to achieve sub-μJ/cm² ASE thresholds. This paper explores breakthroughs in optically pumped lasers (e.g., room-temperature continuous-wave operation, microcavity integration), as well as ongoing challenges facing electrically pumped devices, including charge injection imbalance and thermal degradation. The review further points to emerging opportunities in scalable synthesis, hybrid-size design, and applications such as on-chip photonics and bioimaging. By correlating molecular-scale defect engineering with macroscopic photonic performance, this study provides a roadmap for developing stable and efficient perovskite coherent light sources, bridging fundamental research with practical applications.
A Mini Review of Nano-Structured Metal for Cancer Treatment
Nanostructured metal materials have attracted much attention in cancer treatment field because of their special properties, like large surface area, tunable shape and size, and good physical and chemical stability. Many kinds of nanostructured metal can be used in different cancer therapy methods, such as photothermal therapy, radiotherapy, Reactive oxygen species (ROS) generation, and drug delivery. With the development of controllable synthesis methods, progressively morphologies such as rod, shell, cage, and branched type can be made, which benefit to strong response in light, magnetic field, or radial-frequency (RF), and applied in specific targeted lesion area. These metal nanomaterials also can be used for combined treatment and diagnosis. In this mini review, we introduce recent progress of nanostructured metal in cancer therapy, and also talk about their working mechanism, structure design, and possible problems for future clinical applications.