Search Results (6,843)

The detection of titanium dioxide (${\mathrm{TiO}}_2$) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical frequency combs (OFC) in dual-coupled microresonators. The OFC generation was modeled through the Lugiato-Lefever equation, while propagation in distilled water containing ${\mathrm{TiO}}_2$ was simulated using the finite element method (FEM). The water–${TiO}_2$ mixture was described with the Yamaguchi model in a 5 × 5 mesh to represent non-uniform concentrations. From the norm of the electric field at a probe, a database of 11 classes (0–100%) with controlled Gaussian noise was constructed. A Transformer-based classifier was trained and compared with 1D-CNN and SVM under Monte Carlo validation (100 random 70/30 splits). The Transformer achieved 99.84 ± 0.01% accuracy with an inference time of 0.793 ± 0.05 s, while the 1D-CNN reached 99.64 ± 0.09% and the SVM 84.73 ± 1.48%. A repeatability test with 200 iterations confirmed deterministic DKS trajectories. The results demonstrate that combining dual-coupled microresonators, FEM, and Transformer architectures enables precise and efficient detection of ${\mathrm{TiO}}_2$ nanoparticles in aqueous solutions. Full article

Accurate fruit detection and counting are fundamental requirements in the development of reliable computer vision applications for yield estimation. This work was conceived to provide farmers with a farmer-friendly approach for automatic grape bunch detection. This study exploits the free demo version of the Roboflow 3.0 platform to train five state-of-the-art computer vision models with RGB images of white and red grape bunches, acquired with a smartphone in the field, and compares their performance. The results were evaluated both quantitatively, in terms of precision, recall, and AP@50 calculated on the validation set, and qualitatively on the test set. The models that achieved the best performances, also in the presence of overlapping clusters, were Roboflow 3.0 Object Detection and YOLOv11, reaching precisions of 86.6% and 88%, respectively, for the detection of white bunches, and of 85.7% and 89.9% for red bunches. This study highlights the possibility of developing highly accurate computer vision models for table grape bunch detection using the Roboflow platform, offering an accessible and user-friendly tool for non-expert users, including farmers. Full article

Saccharomyces cerevisiae was cultured under the influence of static magnetic fields (SMFs) to assess their impact on the biosynthesis of silver nanoparticles (AgNPs). Cell-free media derived from SMF-exposed cultures facilitated the formation of AgNPs, with a significant reduction in nanoparticle size observed at an optimal field strength of 7 mT. AgNPs synthesized under SMF conditions exhibited smaller crystalline structures than those produced in control media, as evidenced by dynamic light scattering (DLS) and transmission electron microscopy (TEM) measurements. Over a 75-day period, SMF-exposed AgNPs demonstrated enhanced stability, as determined by DLS and polydispersity index (PDI) assessments. Further analysis through sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and Fourier transform infrared spectroscopy (FTIR) suggested the formation of a protein corona on the AgNPs in SMF-treated samples, which likely inhibits agglomeration and enhances long-term stability. These findings indicate that SMF-induced stress in S. cerevisiae triggers the secretion of specific proteins that contribute to the stabilization of AgNPs, providing a novel approach to controlling nanoparticle synthesis and stability through magnetic field exposure. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

The deterioration of civil infrastructure poses a significant threat to public safety, yet conventional manual inspections remain subjective, labor-intensive, and constrained by accessibility. To address these challenges, this paper presents a real-time robotic inspection system that integrates deep learning perception and autonomous navigation. The proposed framework employs a two-stage neural network: a U-Net for initial segmentation followed by a Pix2Pix conditional generative adversarial network (GAN) that utilizes adversarial residual learning to refine boundary accuracy and suppress false positives. When deployed on an Unmanned Ground Vehicle (UGV) equipped with an RGB-D camera and LiDAR, this framework enables simultaneous automated crack detection and collision-free autonomous navigation. Evaluated on the CrackSeg9k dataset, the two-stage model achieved a mean Intersection over Union (mIoU) of 73.9 ± 0.6% and an F1-score of 76.4 ± 0.3%. Beyond benchmark testing, the robotic system was further validated through simulation, laboratory experiments, and real-world campus hallway tests, successfully detecting micro-cracks as narrow as 0.3 mm. Collectively, these results demonstrate the system’s potential for robust, autonomous, and field-deployable infrastructure inspection. Full article

Lithium is of major importance for many areas of technology, especially batteries, and is therefore relevant to both the industrial and private sectors. High-performance, ideally inline-compatible analytics are important for economical and environmentally friendly lithium extraction. Nuclear Magnetic Resonance is an established analytical method that has already been used in numerous inline applications. For this study on ⁷Li NMR in flow, a cryogen-free magnet with a variable magnetic field was used, whereby a field strength of 4.7 T was set for the measurements for compatibility reasons. The influences of flow velocity, repetition time, and lithium concentration were investigated in spin echo measurements. This allows for defining limitations and potential fields of application for the measurement setup. In addition, the possibilities of internal pre-polarization were investigated. The results show that the method and setup are well suited for inline flow measurements on ⁷Li and have great potential for expanding the range of applications. Full article

The increasing global demand for clean hydrogen necessitates production methods that minimize greenhouse gas emissions while being scalable and economically viable. Hydrogen has a very high gravimetric energy density of about 142 MJ/kg, which makes it a very promising energy carrier for many uses, such as transportation, industrial processes, and fuel cells. Methane pyrolysis has emerged as an attractive low-carbon alternative, decomposing methane (CH₄) into hydrogen and solid carbon while circumventing direct CO₂ emissions. Still, the process is very endothermic and has always depended on fossil-fuel heat sources, which limits its ability to run without releasing any carbon. This review examines the integration of geothermal energy and methane pyrolysis as a sustainable heat source, with a focus on Enhanced Geothermal Systems (EGS) and Closed-Loop Geothermal (CLG) technologies. Geothermal heat is a stable, carbon-free source of heat that can be used to preheat methane and start reactions. This makes energy use more efficient and lowers operating costs. Also, using flared natural gas from remote oil and gas fields can turn methane that would otherwise be thrown away into useful hydrogen and solid carbon. This review brings together the most recent progress in pyrolysis reactors, catalysts, carbon management, geothermal–thermochemical coupling, and techno-economic feasibility. The conversation centers on major problems and future research paths, with a focus on the potential of geothermal-assisted methane pyrolysis as a viable way to make hydrogen without adding to the carbon footprint. Full article

DNA recombination is a fundamental biological process that encodes genetic information for organism development and function. In this study, we construct left-symmetric algebras arising from DNA insertion operations. That is, we define a modified insertion operation by weighting the simplified insertion. It generalizes the left-symmetric algebra constructed from the simplified DNA insertion operation. We prove that the algebra $\mathbb{F}\left(R\right)$ (over a field $\mathbb{F}$ of characteristic 0, with R being an infinite free semigroup generated by DNA nucleotides $\{A,G,C,T\}$) forms a left-symmetric algebra if and only if the function f satisfies a certain multiplicative condition for all positive integers m, n, and p. A key example of such a function is $f(m,n)=exp\left\{g\right(m,n\left)\right\}$, where $g(m,n)=k·mn,$ and k is a fixed positive number, which effectively models length-dependent DNA insertion dynamics. This work contributes an algebraic framework that may be useful for quantitative modeling of DNA recombination processes. Full article

Metal–organic framework (MOF)-based surface-enhanced Raman scattering (SERS) sensors have emerged as a versatile platform for high-sensitivity and selective detection in agricultural, environmental, and biomedical applications. By integrating plasmonic nanostructures with tunable MOF architectures, these hybrid systems combine ultrahigh signal enhancement with molecular recognition, analyte preconcentration, and controlled hotspot distribution. This review provides a comprehensive overview of the fundamental principles underpinning MOF–SERS performance, including EM and chemical enhancement mechanisms, and highlights strategies for substrate design, such as metal–MOF composites, plasmon-free frameworks, ligand functionalization, and hierarchical or core–shell architectures. We further examine their applications in environmental monitoring, pesticide and contaminant detection, pathogen identification, biomarker analysis, and theranostics, emphasizing real-sample performance, molecular selectivity, and emerging integration with portable Raman devices and AI-assisted data analysis. Despite notable advances, challenges remain in reproducibility, quantitative reliability, matrix interference, scalability, and biocompatibility. Future developments are likely to focus on rational MOF design, sustainable fabrication, intelligent spectral interpretation, and multifunctional integration to enable robust, field-deployable sensors. Overall, MOF-based SERS platforms represent a promising next-generation analytical tool poised to bridge laboratory innovation and practical, real-world applications. Full article

Background/Objectives: Autologous dentin matrix (ADM) has been suggested as a biologically plausible biomaterial for alveolar bone regeneration after tooth extraction. However, clinical evidence regarding its biological activity and early healing outcomes is limited. This exploratory, randomized controlled pilot study aimed to descriptively assess early alveolar healing patterns and bone morphogenetic protein 4 (BMP4) expression following tooth extraction using ADM compared with other grafting approaches. Methods: Patients requiring tooth extraction were allocated to one of four groups: ADM, xenograft, ADM combined with platelet-rich fibrin, and a graft-free control group. Histological and immunohistochemical analyses were performed four months after extraction to descriptively assess cellular features of healing and BMP4 expression. The trial was registered at the Brazilian Registry of Clinical Trials (ReBEC; RBR-24mdgrf) and conducted under prior ethics committee approval. Results: BMP4 expression was detected in 67.9% of the analyzed histological fields, predominantly localized in osteocytic, osteoblastic, and medullary areas. Although descriptive differences in BMP4-positive fields were observed among the groups, no statistically significant differences were identified between the groups. Histological evaluation revealed an active cellular environment across all treatment modalities, consistent with early post-extraction healing. No adverse events related to surgical procedures or grafting materials were reported during the study period. Conclusions: Within the limitations of this pilot randomized clinical trial, ADM exhibited consistent biological behavior during early post-extraction alveolar healing. The observed BMP4 expression likely reflects a general physiological healing response rather than a material-specific effect. This finding supports the biological plausibility of dentin-derived grafts as osteoconductive biomaterials. These findings are hypothesis-generating, and larger, adequately powered randomized clinical trials with standardized molecular and histological assessments are required to determine their clinical relevance. Full article

Cables in cable-supported bridges are critical structural components with exceptional tensile capacity, and their assessment is essential for the safety of both the cables themselves and the entire bridge. Microwave radar, a non-contact and efficient measurement technique, has emerged as a promising tool for bridge cable evaluation. This study demonstrates the deployment of microwave radar on bridge decks to efficiently measure the displacements of multiple cables, enabling coverage of all cables while effectively eliminating low-frequency components caused by deck deformation and radar motion using the LOWESS method. The measured cable displacements can be directly used to characterize vibrations, particularly for detecting vortex-induced vibrations (VIVs), without the need for numerical integration of accelerations. Furthermore, microwave radar is applied to free-decay testing for cable damping evaluation, providing an improved signal-to-noise ratio and eliminating the need for sensors installed via elevated platforms, thereby enhancing the reliability of damping assessments. The effectiveness of these approaches is validated through field testing on two cable-stayed bridges. Full article

With the rapid advancement of science and technology, robotics is evolving towards more profound and extensive applications. Nevertheless, the inherent limitations of traditional industrial “caged” robots have significantly impeded the full utilization of their capabilities. Consequently, breaking free from these constraints and realizing human–robot collaboration has emerged as a new developmental trend in the robotics field. The collision-response mechanism, as a crucial safeguard for human–robot collaboration safety, has become a pivotal issue in enhancing the performance of human–robot interaction. To address this, an adaptive admittance control collision-response algorithm is proposed in this paper, grounded in the principle of admittance control. A collision simulation model of the AUBO-i5 collaborative robot is constructed. The effectiveness of the proposed algorithm is verified through simulation experiments focusing on both the end-effector collision and body collision of the robot, and by comparing it with existing admittance control algorithms. Furthermore, a collision-response experimental platform is established based on the AUBO-i5 collaborative robot. Experimental studies on end-effector and body collisions are conducted, providing practical validation of the reliability and utility of the proposed adaptive admittance control collision-response algorithm. Full article

Escherichia coli (E. coli) is a well-established indicator of faecal pollution and a potent pathogen linked to numerous gastrointestinal and systemic illnesses. Ensuring public safety requires rapid and sensitive detection methods capable of real-time, on-site deployment. Many conventional techniques are either laborious, time-intensive, costly, or require complex infrastructure, limiting their applicability in field settings. Raman spectroscopy offers label-free molecular fingerprinting; however, its inherently weak scattering signals restrict its effectiveness as a standalone technique. Surface-Enhanced Raman Spectroscopy (SERS) overcomes this limitation by exploiting plasmonic enhancement from nanostructured metallic substrates—most commonly gold, silver, copper, and aluminium. Despite the commercial availability of SERS-active substrates, challenges remain in achieving high reproducibility, long-term stability, and true field applicability, necessitating the development of integrated lab-on-chip platforms and portable, handheld Raman devices. This review critically examines recent advances in SERS-based E. coli detection across water and perishable food products with particular emphasis on the evolution of SERS substrate design, the incorporation of biosensing elements, and the integration of electrochemical and microfluidic systems. By contrasting conventional SERS approaches with next-generation biosensing strategies, this paper outlines pathways toward robust, real-time pathogen detection technologies suitable for both laboratory and field applications. Full article

Human carboxylesterases (CES) are enzymes that play a central role in the metabolism and biotransformation of diverse endogenous substances and xenobiotics. The two most relevant isoforms, CES1 and CES2, are crucial in clinical pharmacotherapy as they catalyze the hydrolysis of numerous approved drugs and prodrugs. Elucidating the structural basis of CES isoform substrate specificity is essential not only for understanding and anticipating the biological fate of administered drugs, but also for designing prodrugs with optimized site-specific bioactivation. Additionally, this knowledge is also important for the design of biomedically useful molecules such as subtype-targeted CES inhibitors and fluorescent probes. In this context, both experimental and computational methodologies have been used to explore the mechanistic and thermodynamic properties of CES-mediated catalysis. Experimental designs commonly employ recombinant CES or human tissue microsomes as enzyme sources, utilizing quantification methods such as spectrophotometry (UV and fluorescence) and mass spectrometry. Computational approaches fall into two categories: (1) modeling substrate: CES recognition and affinity (molecular docking, molecular dynamics simulation, and free-energy binding calculations), and (2) modeling substrate: CES reaction coordinates (hybrid QM/MM simulations). While experimental and theoretical approaches are highly synergistic in studying the catalytic properties of CES subtypes, they represent distinct technical and scientific fields. This review aims to provide an integrated discussion of the key concepts and the interplay between the most commonly used wet-lab and dry-lab strategies for investigating CES catalytic activity. We hope this report will serve as a concise resource for researchers exploring CES isoform specificity, enabling them to effectively utilize both experimental and computational methods. Full article

This study presents a comprehensive digital workflow for the archaeological investigation and heritage enhancement of the Coëby megalithic necropolis (Brittany, France). Dating to the Middle Neolithic, between the 4th and 3rd millennia BC, this chronology is established through stratigraphy, material culture, and radiocarbon dating. Focusing on cairns TRED 8 and TRED 9, which are two excavation units, we combined field archaeology, photogrammetry, and topographic data with open-source 3D geometric modeling to reconstruct the monuments’ original volumes and test construction hypotheses. The methodology leveraged the free software Blender (version 3.0.1) and its Bagapie extension for the procedural simulation of lithic block distribution within the tumular masses, ensuring both metric accuracy and realistic texturing. Beyond static reconstruction, the research explores innovative dynamic and narrative visualization techniques. We employed the FILM model for smooth video interpolation of the construction sequences and utilized the Wan 2.1 AI model to generate immersive video scenes of Neolithic life based on archaeologically informed prompts. The entire process, from data acquisition to final visualization, was conducted using free and open-source tools, guaranteeing full methodological reproducibility and alignment with open science principles. Our results include detailed 3D reconstructions that elucidate the complex architectural sequences of the cairns, as well as dynamic visualizations that enhance the understanding of their construction logic. This study demonstrates the analytical potential of open-source 3D modelling and AI-based visualisation for megalithic archaeology. Full article