Search Results (1,547)

The term bacterial biofilm refers to a complex community of microorganisms embedded within a self-produced matrix of extracellular polymeric substances. This structural organization creates an environment that, when present in an infectious context within a living organism, limits the effectiveness of conventional antibiotic therapy. Consequently, such conditions substantially promote the development of antibiotic resistance. The decline in the discovery of novel antibiotic agents, coupled with a concurrent increase in the prevalence of multidrug-resistant microorganisms, has intensified the search for alternative strategies to combat such infections. At the same time, advances in nanoscience have stimulated substantial research into the use of micro/nanorobots for the eradication of bacterial biofilms. These devices, engineered at the micro- to nanoscale, are capable of targeted intervention in otherwise inaccessible sites. However, the development of such “microscopic therapeutic agents” is still at an early stage. To date, the vast majority of available data has been derived from in vitro studies, while evidence regarding their feasibility, safety, and therapeutic effects in living organisms remains limited. This review discusses their antimicrobial mechanisms and critically evaluates the current evidence concerning their in vivo applications. Full article

Background: Micro- and nano-plastics (MNPs) are pervasive environmental contaminants that accumulate in various tissues, including the placenta. Experimental and clinical studies suggest potential cytotoxic, oxidative, and inflammatory effects that may lead to placental dysfunction and adverse obstetric outcomes. However, high-quality evidence on the clinical relevance of MNPs exposure during pregnancy remains scarce, underscoring the need for systematic evaluation of their impact on maternal and fetal health. Methods: The databases PubMed, ScienceDirect, CENTRAL, Embase, MDPI and Google Scholar were searched for studies published up to September 2025 investigating the relationship between MNPs and obstetric outcomes. Results: Twelve studies were included in this review, with half employing an observational design in human subjects and the other half using experimental studies in murine models. Although the available evidence is limited, there are studies reporting the association between MNPs exposure and premature birth, low birth weight, intrauterine growth restriction, and miscarriage. The most prevalent polymer detected was polyethylene, and the most commonly used MNPs detection techniques were Raman microspectroscopy, digital microscopy, Fourier Transform Infrared, and Pyrolysis gas chromatography-mass spectrometry. Conclusions: This systematic review summarizes current limited insights on the potential effects of MNPs on obstetric outcomes, highlighting possible associations with low gestational age, low birth weight, intrauterine growth restriction, and miscarriage. Findings do not allow causal inference due to heterogeneity in study design, exposure assessment, contamination control, and analytical methodologies. Full article

While aluminum (Al) continues to be a cornerstone for microelectronic interconnect technologies, its chronic tendency toward hillock growth and thermal instability necessitates a transition toward high-performance nanostructured material architectures. This research tackles these reliability bottlenecks by achieving a molecular-level integration of graphene nanoplatelets (GNPs) within Al matrices, a strategy designed to fortify structural resilience. Adopting a green chemistry approach, we synthesized Al-GNP (0.25 vol.%) composite thin films through Pulsed Laser Deposition (PLD) using precursors derived from recycled aluminum. A major obstacle—the formation of the deleterious Al₄C₃ intermetallic phase—was effectively suppressed by ensuring a homogeneous supramolecular dispersion via a specialized dual protocol (ultrasonication and magnetic stirring) during the powder metallurgy stage. Comprehensive physicochemical characterization, utilizing HR-TEM and XRD, verified the structural integrity of the multilayer GNPs (d-spacing = 4.6 Å). Furthermore, surface metrology analysis uncovered a radical shift in growth kinetics: whereas pure Al grew via a “spiky” Volmer-Weber mechanism (Sku = 31.17), the carbon-based inclusion stabilized the film evolution, tempering the kurtosis to Sku = 7.74. Analytical cross-sectional EDS confirmed both stoichiometric fidelity and the achievement of void-free Si/Pt/Al-GNP interfaces. These outcomes prove that a precise nanoscale tailoring of surface morphology via carbonaceous reinforcements significantly bolsters microstructural stamina. Consequently, these PLD-deposited composites emerge as sustainable, cutting-edge candidates for the next generation of microelectronic packaging and interfacial chemistry applications. Full article

Background/Objectives: Pathogenic variants in GNAO1, encoding the inhibitory G protein subunit Gαo, cause severe neurodevelopmental disorders that remain largely refractory to pharmacological treatments. Gαo transduces inhibitory signals downstream of multiple G protein-coupled receptors (GPCRs) involved in motor control. Here, we used gene-edited Caenorhabditis elegans models carrying goa-1 variants, the ortholog of GNAO1, to investigate GPCR contributions to Gαo-dependent locomotor phenotypes. Methods: We combined pharmacological screening of dopamine- and cannabinoid-targeting ligands in goa-1 mutants with structural analysis of ligand-binding pocket conservation and genetic perturbation of receptor function using RNAi and knockout approaches. Results: Pharmacological modulation of GPCR signaling produced non-linear and context-dependent effects. Compounds predicted to further increase excitability may instead promote phenotypic improvement, consistent with compensatory network rebalancing. Structural analyses revealed substantial divergence in ligand-binding pocket conservation for several GPCR-ligand pairs, suggesting that altered binding affinity and selectivity may also contribute to the observed phenotypic outcome. Pharmacological experiments performed in GPCR-depleted mutants allowed for the correlation of structural findings with functional effects for selected receptor-ligand pairs. Finally, genetic reduction in GPCRs coupled to stimulatory G proteins ameliorated hyperactive locomotion in goa-1 mutants, whereas reduction in GPCRs coupled to inhibitory G proteins is largely insufficient to induce or exacerbate locomotor defects. Conclusions: Our findings identify excessive excitatory GPCR input as a key modulator of motor dysfunction in the context of impaired Gαo signaling. They also show that structural conservation is a necessary but not sufficient condition to predict functional responses. Overall, this study establishes C. elegans as a suitable platform to dissect GPCR-mediated signaling and highlights the value of integrating pharmacological and genetic approaches to guide target selection in GNAO1-related disorders. Full article

Drag-reducing coating technology is a core approach to enhancing the performance of ice and snow sports equipment. By regulating the interfacial characteristics between the equipment surface and the ice or snow medium, it significantly reduces frictional resistance during motion, thereby optimizing athletes’ speed performance and control precision. This paper aims to review the current research status and challenges in this technological field. The review first elaborates on the fundamental principles of applying drag-reducing coatings to key equipment such as skis, sleds, and ice skates, covering current mainstream coating material systems, key preparation processes, and comprehensive performance evaluation methods. Furthermore, integrating multidisciplinary advances in surface engineering, fluid dynamics, and materials science, this review specifically examines how these disciplines can be harnessed to address the unique tribological challenges of snow/ice interfaces. It focuses on cutting-edge research directions such as micro-nano-structured coatings driven by biomimetic design concepts and smart coatings with environmental responsiveness. By synthesizing existing research achievements and potential technological bottlenecks, this paper aims to provide a systematic, theoretical basis and innovative ideas for the future development of a new generation of high-performance, intelligent ice and snow sports equipment. Full article

Metal nanoparticles are widely used in fibrous membrane materials due to their excellent antibacterial properties. However, metal nanoparticle-loaded fibrous membranes often face the trade-off between antibacterial performance and filtration efficiency. To address this issue, silver nanoparticle-loaded dendritic fibrous membranes were prepared via electrospinning technology in this study, and the dual optimization of antibacterial and filtration performance was achieved by adjusting the silver loading amount and fiber morphology. The results showed that the prepared silver nanoparticle-loaded PVDF dendritic fibrous membrane exhibited an outstanding air filtration performance with a filtration efficiency of 99.87% for 0.3 µm particulate matter, a pressure drop of 87.4 Pa, and a quality factor (QF) of 0.076 Pa⁻¹. In addition, the membrane presented excellent antibacterial activity with inhibition rates of 99.9% and 99.8% against Escherichia coli and Staphylococcus aureus, respectively. This study provides a new insight into resolving the trade-off between air filtration and antibacterial performance of metal nanoparticle-loaded fibrous membranes and offers an important reference for applications in related fields. Full article

A conformal SiNx coating on carbon nanotubes (CNTs) was achieved by combining transient UV–ozone surface functionalization with a two-step atomic layer deposition (ALD) process. UV–ozone treatment gradually increased the defect density of CNTs, with the I_D/I_G ratio increasing from 0.05 for pristine CNTs to 0.25 after 7 min of exposure, while the overall fibrous CNT network remained intact. However, prolonged UV–ozone exposure beyond 10 min led to a sharp increase in the I_D/I_G ratio to 0.46, accompanied by structural degradation of the CNT membrane. Hydroxyl (-OH), epoxy (C-O-C), and carbonyl (C=O) groups were introduced by UV–ozone treatment and were partially removed during subsequent high-temperature processing. Accordingly, direct high-temperature ALD resulted in incomplete SiNx coverage of the CNTs, suggesting insufficient nucleation. A two-step ALD process, consisting of several cycles of low-temperature nucleation at 100 °C followed by high-temperature growth at 700 °C, enabled more conformal deposition of SiNx on CNTs. In addition, both annealing and ALD reduced the defect level toward that of pristine CNTs, supporting the transient nature of UV–ozone-induced functionalization. Full article

Optical tweezers (OTs) serve as a core contactless manipulation tool at the micro- and nano-scale, with wide applications in physics, biology, colloid science and other fields. However, conventional single-beam gradient force OTs are limited by diffraction, optical damage, low throughput, and system complexity. To meet the demand for large-scale particle manipulation in complex environments, speckle optical tweezers (SOTs) based on random optical fields have emerged as a promising alternative to conventional OTs that transform random speckle patterns into a controllable manipulation resource. Since their formal establishment, SOTs have developed a solid theoretical foundation and diverse implementation platforms with key breakthroughs in micro- and nanoparticle manipulation. This paper systematically reviews the origin and development of SOTs, elaborates their core principles, summarizes the statistical properties of speckle fields, and introduces typical configurations based on random media, multimode fibers, and spatial light modulators. It also highlights the unique value of SOTs in micro- and nanoparticle manipulation, active particle dynamics, and cold atom physics, with advantages of high throughput, low cost, and environmental adaptability. Finally, future development trends are discussed, including intelligent regulation of optical fields, interdisciplinary applications, system miniaturization and multi-technology integration. This review provides a comprehensive reference for the theoretical development, system optimization, and practical application of SOTs in fields such as statistical physics, biomedicine, microfluidics, and quantum science. Full article

Background: Conventional therapeutic and diagnostic approaches, despite improving clinical outcomes, remain limited by poor bioavailability, inadequate targeting, suboptimal pharmacokinetics, and systemic toxicity, particularly in complex diseases. To overcome this, nanomedicine has emerged as a transformative strategy, employing engineered nanoparticles to enhance drug stability, controlled release, targeted delivery, and diagnostic performance, thereby enabling theranostic applications. This review evaluates major nanoparticle platforms in therapy and diagnosis, comparing their mechanisms, applications, and challenges while highlighting their potential to advance precision medicine and theranostic strategies. Method: For providing the context and evidence, relevant literatures were sourced from Google Scholar, PubMed, and ScienceDirect using targeted keywords including “drug delivery,” “diagnostics,” “nanoparticles,” “nanomedicine,” “nano drug delivery,” “nanotheranostics,” “targeted therapy,” “controlled drug release,” “solid lipid nanoparticles (SLNs),” “lipid nano carriers (LNCs),” and “inorganic nanoparticles.” Although no strict time limit was applied during the literature search, clinical trial data were collected and analyzed up to January 2026. Given that clinical trial registries are continuously updated, the included trials represent the status at the time of data retrieval. However, it is pertinent to note that the earliest relevant studies appeared in 1973. Conclusions: This review highlights nanoparticle fundamentals, major material classes, mechanisms of action, and applications in targeted therapy, imaging, and theranostics. It also addresses translational barriers related to safety, scalability, biological complexity, and regulatory compliance. Overcoming these challenges through standardized characterization and interdisciplinary collaboration is crucial for clinical adoption. Future efforts should focus on AI-driven design, computational tools, smart nanomedicines, and advanced biosensing technologies to integrate nanoparticle-enabled precision diagnostics and therapy into routine clinical practice. Full article

The continuous research progress in materials science has enabled the development of advanced nano-materials, including carbon nano-tubes, graphene and metal oxides with specialized properties, which can fundamentally affect the mechanical, thermal and tribological properties of conventional materials when used in the reinforcing phase [...] Full article

Liver cancer remains a significant global health burden, requiring the development of precise nucleic acid delivery systems. Lipid nanoparticles (LNPs) are leading candidates; however, their efficiency is governed by the pK_a of ionizable lipids, which dictates nanoparticle stability and endosomal escape. In this study, we employed a machine learning–driven quantitative structure–activity relationship framework to predict the pK_a of ionizable lipids derived from the DLin–KC2–DMA scaffold. Utilizing a dataset of 56 compounds, we compared Random Forest, Artificial Neural Network, and Extreme Gradient Boosting (XGB) models integrated with Permutation Importance (PI) for feature selection. The optimized PI–XGB model exhibited exceptional predictive accuracy (R² = 0.970, R²_CV = 0.901, RMSE_test = 0.115) and robust generalization confirmed via external validation (RMSE_ext. = 0.313). Mechanistic insights derived from SHapley Additive exPlanation analysis identified charge distribution, molecular topology, and polarity as critical determinants of lipid ionization. These results demonstrate the power of interpretable machine learning in elucidating molecular structure–property relationships, offering a robust computational strategy for the rational design of next–generation ionizable lipids to optimize LNP–mediated gene therapy for liver cancer. Full article

This paper presents UM3D, an end-to-end unsupervised domain adaptation framework for monocular 3D object detection. Monocular 3D object detection is appealing due to its low cost, yet it suffers from limited depth cues and poor cross-domain generalization when labeled data are scarce. Existing Pseudo-LiDAR methods require supervised training and propagate depth estimation errors to downstream detection, while current unsupervised domain adaptation (UDA) approaches exploit only a single modality and lack effective pseudo-label quality control. UM3D addresses these limitations through two key designs: (1) a quality-aware pseudo-label generation strategy with object-level random scaling and a memory bank refinement mechanism; and (2) an end-to-end differentiable pipeline that integrates multimodal fusion of image and Pseudo-LiDAR features with a multi-network consistency loss, which jointly optimizes depth estimation and 3D detection via backpropagation. Notably, the entire pipeline requires only a single monocular camera at inference; the Pseudo-LiDAR representation is generated internally from the same image, and thus the multimodal fusion integrates image and Pseudo-LiDAR features without requiring additional sensors. Extensive experiments across KITTI, nuScenes, Waymo, and Lyft demonstrate that UM3D generally outperforms existing UDA methods. In particular, a 19.30% relative ${\mathrm{AP}}_{\mathrm{BEV}}$ improvement is achieved under easy conditions through end-to-end joint training compared to independent depth estimation, and up to 76.81% of the domain gap is closed on the WOD → KITTI benchmark. Full article

Camera-based 3D occupancy prediction commonly relies on bird’s-eye-view (BEV) representations, yet two limitations remain: optimization instability when inserting new modules into pre-trained BEV encoders, and height-agnostic BEV-to-voxel lifting that fails to preserve elevation-aware scene structure. We propose GSH-Occ (Gradient-Shielded and Height-Aware BEV Occupancy Network), a framework that tackles both issues through two complementary mechanisms. Gradient-Shielded Residual Dual Attention (GS-RDA) introduces a zero-initialized residual gate that preserves the identity mapping at initialization, allowing new attention modules to be grafted onto pre-trained encoders without disturbing learned features. Height-Aware Adaptive Lift (HAL) replaces naive channel replication with per-voxel adaptive fusion of BEV features and learnable height embeddings, followed by 3D convolutional refinement to capture vertical structure. On the Occ3D-nuScenes validation benchmark, GSH-Occ achieves 46.92 mIoU, outperforming FlashOcc by $+3.40$ mIoU. Ablation studies confirm that GS-RDA and HAL target distinct failure modes and yield complementary improvements. Full article

A miniaturized (70 × 7.7 cm) multi-parameter synchronous observation system was developed for in situ ocean turbulence measurement, integrating micro-electromechanical system (MEMS)-based two-dimensional (2D) turbulence, pressure, temperature, conductivity, and attitude sensors. Field tests conducted at a depth of 1800 m in the northern South China Sea validated the system’s accuracy through comparisons with standard CTD (Conductivity, Temperature, and Depth) sensors, dual-probe consistency analysis, and Nasmyth spectrum fitting. The system precisely captured thermoclines, internal waves, and turbulent shear fluctuations at a depth of approximately 125 m, revealing enhanced turbulence near the thermocline due to intensified shear effects. With high spatiotemporal synchronization and reliability, the system provides an effective solution for studying multiscale ocean turbulence and associated dynamic processes. Full article

Densely packed semiconducting carbon nanotube (CNT) arrays with well-controlled morphology are highly desirable for high-performance CNT-based electronics. Although dielectrophoresis (DEP) enables precise, efficient, and site-selective assembly, increasing array density often destabilizes process regulation and aggravates nanotube bundling because of the dynamic interplay among assembly conditions. Here, we introduce the effective deposition region (EDR) to reformulate DEP assembly into a framework that links DEP conditions and final arrays through an interpretable CNT deposition dynamic based on the effective DEP capture. Within this framework, experiments and modeling indicate a self-regulating, negative-feedback mechanism in which conductive CNT bridging reduces the gap voltage, contracts the EDR, and weakens sustained CNT-capture capability, thereby driving the assembly toward self-termination. By synergistically optimizing the applied voltage, electrode configuration, and CNT dispersion concentration to regulate EDR contraction, we obtained dense, bundle-suppressed CNT arrays with the number of nanotubes per unit width of approximately 140 tubes µm⁻¹. The formation of small bundles implies that further combination of EDR-regulated assembly with additional inter-tube interactions is required to realize dense, monolayer CNT arrays. This work provides a coherent mechanistic framework for understanding feedback-regulated DEP assembly and enables a practical approach for optimizing both densification and morphology control in CNT array assembly. Full article