Search Results (4,381)

In the shift toward sustainable energy, there is a strong demand for efficient and durable energy storage solutions. Supercapacitors, in particular, are a promising technology, but they require high-performance materials that can be produced using simple, eco-friendly methods. This has led researchers to investigate new materials and composites that can deliver high energy and power densities, along with long-term stability. Herein, we report a green synthesis approach to create a composite material consisting of reduced graphene oxide and silver nanoparticles (rGO/Ag). The method uses ascorbic acid, a natural compound found in fruits and vegetables, as a non-toxic agent to simultaneously reduce graphene oxide and silver nitrate. To enhance electrochemical performance, the incorporation of silver nanoparticles into the rGO structures is optimized. In this study, different molar concentrations of silver nitrate (1.0, 0.10, and 0.01 M) are used to control silver nanoparticle loading during the synthesis and reduction process. A correlation between silver concentration, defect density in rGO, and the resulting capacitive behavior was assessed by systematically varying the silver molarity. The synthesized materials exhibited excellent performance as supercapacitor electrodes in a three-electrode configuration, with the rGO/Ag 1.0 M composite showing the best performance, reaching a maximum specific capacitance of 392 Fg⁻¹ at 5 mVs⁻¹. Furthermore, the performance of this optimized electrode material was investigated in a two-electrode configuration as a coin cell device, which demonstrates a maximum areal-specific capacitance of 22.63 mFcm⁻² and a gravimetric capacitance of 19.00 Fg⁻¹, which is within the range of commercially viable devices and a significant enhancement, outperforming low-level graphene-based devices. Full article

In the manufacturing of micro-scale components, geometric dimensional accuracy and product quality are critical factors that directly influence both production costs and efficiency. To meet the growing demands in this field, micro-extrusion technology has been developed and extensively applied, particularly in mass and bulk production. This technology is considered an optimal solution for improving dimensional accuracy, enhancing mechanical properties, increasing production efficiency, and reducing costs compared to traditional methods, while also aligning with the current trends of modern industrial development. This study investigates the influence of temperature and friction on forming force, formability, and product quality during the micro-extrusion process. A combined approach of simulation and experimentation was utilized to form cross-shaped coupling components using pure copper as the material. The results indicate a significant relationship between temperature, friction coefficient, and forming force. Furthermore, 550 °C is identified as the most suitable temperature for hot forming, providing a balance between force reduction and product quality. These insights enhance the predictability and control of the micro-extrusion process and contribute to reducing production defects. Ultimately, the findings support wider implementation of micro-extrusion in the manufacturing of high-accuracy small-scale parts and align with modern trends emphasizing miniaturization, automation, and cost efficiency. Full article

Background: Glaucoma is a multifactorial optic neuropathy and the leading cause of irreversible blindness worldwide. Vascular mechanisms, including impaired perfusion of the optic nerve head, are increasingly recognized as contributors to disease progression. Optical coherence tomography angiography (OCTA) enables non-invasive assessment of retinal and choroidal microvasculature, including peripapillary microvasculature dropout (MvD), which may serve as a marker of glaucomatous damage. Methods: A cross-sectional case–control study was conducted, including patients with primary open-angle glaucoma (OAG) and healthy controls. All participants underwent a comprehensive ophthalmic evaluation and OCTA imaging using the PLEX Elite 9000 system. Peripapillary vessel density (pVD), flow index (pFI), peripapillary choroidal thickness (PCT), β-zone parapapillary atrophy (β-PPA), and choroidal vascular indices were measured. MvD was defined as the complete absence of microvasculature within the β-PPA boundary. Statistical analyses included univariate and multivariate regression models to examine variables associated with PCT and to assess the association between MvD and visual field mean defect (MD), as well as other glaucoma characteristics. ROC curve analysis was performed to evaluate the ability of MvD to discriminate between different levels of visual field defects. Results: A total of 87 eyes (41 glaucomatous, 46 controls) were analyzed. Glaucoma patients exhibited significantly lower pVD, pFI, PCT, and choroidal vascular indices compared to the controls. MvD was detected in 10 glaucomatous eyes and was associated with a larger β-PPA area, smaller choroidal luminal and stromal areas, and worse mean deviation (MD) values. Multivariate regression showed that the number of ocular hypotensive treatments and StructureIndex variables were significantly associated with PCT (adjusted R² = 0.14). Logistic regression analysis identified MD, MD slope, and β-PPA area as variables significantly associated with the presence of MvD. ROC analysis showed that the presence of MvD had good discriminatory ability for visual field mean defects (MDs) (AUC = 0.77, 95% CI: 0.69–0.87; p = 0.005). Conclusions: Peripapillary MvD detected by OCTA is associated with reduced choroidal vascularity, increased β-PPA, and greater visual field deterioration in glaucoma patients. MvD may serve as a structural marker associated with functional deterioration in glaucoma patients. Full article

We report combined scanning probe microscopy and electrical measurements to investigate local electronic transport in reduced graphene oxide (rGO) devices. We demonstrate that quantum transport in these materials can be significantly tuned by the electrostatic potential applied with a conducting atomic force microscope (AFM) tip. Scanning gate microscopy (SGM) reveals a clear p-type response in which local gating modulates the source–drain current, while scanning impedance microscopy (SIM) indicates corresponding shifts of the Fermi level under different gating conditions. The observed transport behavior arises from the combined effects of AFM tip-induced Fermi-level shifts and defect-mediated scattering. These results show that resonant scattering associated with impurities or structural defects plays a central role and highlight the strong influence of local electrostatic potentials on rGO conduction. Consistent with this electrostatic control, the device also exhibits chemical gating and sensing: during exposure to electron-withdrawing molecules (acetone), the source–drain current increases reversibly and returns to baseline upon purging with air. Repeated cycles over 15 min show reproducible amplitudes and recovery. Using a simple transport model, we estimate an increase of about $40\%$ in carrier density during exposure, consistent with p-type doping by electron-accepting analytes. These findings link nanoscale electrostatic control to macroscopic sensing performance, advancing the understanding of charge transport in rGO and underscoring its promise for nanoscale electronics, flexible chemical sensors, and tunable optoelectronic devices. Full article

Tool edge preparation is the process aimed at eliminating edge defects and optimizing the micro-geometric parameters of cutting tools. Drag finishing, the primary engineering method, subjects tools to planetary motion (simultaneous revolution and rotation) within abrasive media to remove burrs and micro-chips, thereby improving cutting performance and extending tool life. A discrete element method (DEM) model of drag finishing edge preparation was developed to investigate the effects of processing time, tool rotational speed, and rotation direction on abrasive-mediated tool wear behavior. The model was validated through milling cutter edge preparation experiments. Simulation results show that increasing the processing time causes fluctuating changes in average abrasive velocity and contact forces, while cumulative energy and tool wear increase progressively. Elevating tool rotational speed increases average abrasive velocity, contact forces, cumulative energy, and tool wear. Rotation direction significantly impacts tool wear: after 2 s of clockwise (CW) rotation, wear reached 1.45 × 10⁻⁸ mm; after 1 s of CW followed by 1 s of counterclockwise (CCW) rotation, wear was 1.25 × 10⁻⁸ mm; and after 2 s of CCW rotation, wear decreased to 1.02 × 10⁻⁸ mm. Experiments, designed based on simulation trends, confirm that edge radius increases with time and tool rotational speed. After 30 min of processing at 60, 90, and 120 rpm, average edge radius increased to 22.5 μm, 28 μm, and 30 μm, respectively. CW rotation increased the edge shape factor K, while CCW rotation decreased it. The close agreement between experimental and simulation results confirms the model’s effectiveness in predicting the impact of edge preparation parameters on tool geometry. Rotational speed control optimizes edge preparation efficiency, the predominant tangential cumulative energy reveals abrasive wear as the primary material removal mechanism, and rotation direction modulates the shape factor K, enabling symmetric edge preparation. Full article

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well as the uniqueness of each printed component. Building upon our prior work in developing 3D-printable self-sensing cementitious materials by incorporating graphite powder and carbon microfibers into a cementitious matrix to enhance its piezoresistive properties, this study aims at enabling condition assessment of cementitious 3DP by integrating the self-sensing materials as sensing nodes within conventional components. Three different 3D-printed strip patterns, consisting of one, two, and three strip lines that mimic the pattern used in fabricating foil strain gauges were investigated as conductive electrode designs to impart strain sensing capabilities, and characterized from a series of quasi-static and dynamic tests. Results demonstrate that the three-strip design yielded the highest sensitivity (${\lambda }_{\mathrm{stat}}$ of 669, ${\lambda }_{\mathrm{dyn}}$ of 630), whereas the two-strip design produced the highest signal quality (SNR_stat = 9.5 dB, SNR_dyn = 10.8 dB). These findings confirm the feasibility of integrating 3D-printed self-sensing cementitious materials through hybrid manufacturing, enabling monitoring of print quality, detection of load path changes, and identification of potential defects. Full article

Inducible CRISPR interference (CRISPRi) systems were established in Lactococcus lactis using both plasmid and chromosomal approaches. Expression of nuclease-deficient Cas9 (dCas9) from Streptococcus pyogenes was placed under the control of the nisin-inducible promoter P_nisA, while sgRNAs were transcribed from the constitutive P_usp45 promoter. To monitor expression, dCas9 was fused with superfolder GFP. Plasmid-based constructs successfully repressed a luciferase reporter gene and silenced the gene of the major autolysin, AcmA, leading to the expected morphological phenotype. However, plasmid systems showed leaky expression, producing mutant phenotypes even without induction. Chromosomal integration of dCas9 reduced its expression level by approximately 20-fold compared with plasmid-based expression, thereby preventing leaky activity and ensuring tight regulation. This chromosome-based (cbCRISPRi) platform enabled controlled repression of the essential gene ybeY, which resulted in severe growth defects. Restoration of wild-type phenotypes was achieved by introducing a synonymous codon substitution in the sgRNA target region. Transcriptome analysis of ybeY-silenced cells revealed downregulation of ribosomal protein genes and widespread effects on membrane-associated proteins, ATP synthase subunits, and various transporters. These inducible CRISPRi platforms provide robust and tunable tools for functional genomics in L. lactis, particularly for studying essential genes that cannot be deleted. Full article

Shilajit, a natural herbo-mineral compound with potent antioxidant, anti-inflammatory, and osteogenic properties, has been traditionally used to promote tissue repair. However, limited experimental data exist on its localized application in bone regeneration. This study aimed to evaluate the combined effect of Shilajit and bovine-derived xenograft on bone healing in a rat tibial defect model. Twenty-eight male Sprague–Dawley rats were randomly assigned to four groups (n = 7): Control (defect left to heal spontaneously), Graft-only, Graft + Shilajit 150 mg/kg, and Graft + Shilajit 250 mg/kg. Standardized 3 mm tibial defects were created and filled with xenograft in all groups except the Control. Shilajit was administered intraperitoneally on days 0–3 postoperatively. After 4 weeks, serum total oxidant status (TOS), total antioxidant status (TAS), and TNF-α levels were measured. Tibial specimens underwent histopathological, histomorphometric, and TNF-α immunohistochemical analysis. High-dose Shilajit significantly increased TAS and reduced TOS compared with the Control and Graft-only groups (p < 0.001). Median TNF-α concentrations decreased in a dose-dependent manner, with the lowest values in the high-dose group (15.7 [14.3–17.1] pg/mL, p < 0.001). Histomorphometry revealed the highest new bone area percentage (78.1% [74.9–81.2]) and lowest fibrous tissue content (9.8% [8.1–11.6]) in the high-dose group. Immunohistochemistry confirmed marked suppression of TNF-α expression in Shilajit-treated groups, particularly at high doses. The combination of Shilajit and bovine-derived xenograft significantly enhanced bone regeneration in a dose-dependent manner, likely through antioxidative, anti-inflammatory, and osteogenic mechanisms. These findings suggest that Shilajit may serve as a promising adjunct in bone grafting procedures. Full article

The frequency characteristics of the switching-mode power supply (SMPS) control loop under all operating conditions are crucial for performance evaluation and defect detection. Traditional methods, relyingon experiments under preset conditions, struggle to achieve comprehensive evaluation. This study proposes a frequency characteristic fitting method for all operating conditions based on FT-WOA-MLP. A discrete-point dataset covering all conditions of an LLC SMPS was obtained using the small-signal perturbation method, including input voltage, output current, injection frequency, and corresponding amplitude- and phase-frequency characteristics. The multilayer perceptron (MLP) model was trained on the training set covering all operating conditions, with the whale optimization algorithm (WOA) used to optimize the learning rate, and fine tuning (FT) applied to further enhance accuracy. Independent test set validation showed that, for amplitude-frequency characteristics, the mean absolute error (MAE) was 2.0995, the mean absolute percentage error (MAPE) was 0.0974, the root mean square error (RMSE) was 4.0474, and the coefficient of determination (R²) reached 0.92; for phase-frequency characteristics, the MAE was 3.502, the MAPE was 0.0956, the RMSE was 10.5192, and the R² reached 0.94. The method accurately fits frequency characteristics under all conditions, supporting defect identification and performance optimization. Full article

Surface defect detection serves as one of the crucial auxiliary components in the quality control of varistors, and it faces real challenges such as the scarcity of defect samples, high labelling cost, and insufficient a priori knowledge, which makes unsupervised deep learning-based detection methods attract attention. However, existing unsupervised models have problems such as inaccurate defect localisation and a low recognition rate of subtle defects in the detection results. To solve the above problems, an unsupervised detection method (Var-MNDR) is proposed to reconstruct the normal distribution of surface defects of varistors under mask constraints. Firstly, on the basis of colour space as well as morphology, an image preprocessing method is proposed to extract the main body image of the varistor, and a mask-constrained main body pseudo-anomaly generation strategy is adopted so that the model focuses on the texture distribution of the main body region of the image, reduces the model’s focus on the background region, and improves the defect localisation capability of the model. Secondly, Kolmogorov–Arnold Networks (KANs) are combined with the U-Network (U-Net) to construct a segmentation sub-network, and the Gaussian radial basis function is introduced as the learnable activation function of the KAN to improve the model’s ability to express the image features, so as to realise more accurate defect detection. Finally, by comparing the four unsupervised defect detection methods, the experimental results prove the superiority and generalisation of the proposed method. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

This study presents a viscosity-controlled epoxy infiltration strategy to mitigate common production defects, such as interlayer bond weaknesses, step gaps, and surface roughness, in 3D-printed polypropylene lattice and tube structures. To address these issues, epoxy resin infiltration was applied at four distinct viscosity levels. The infiltration process, facilitated by ultrasonic assistance, improved epoxy penetration into the internal structure. The results indicate that this method effectively reduced micro-voids and surface irregularities. Variations in epoxy viscosity significantly influenced the final internal porosity and the thickness of the epoxy film formed on the surface. These structural changes directly affected the energy absorption (EA) and specific energy absorption (SEA) of the specimens. While performance was enhanced across all viscosity levels, the medium-viscosity specimens (L-V2 and L-V3), with a mass uptake of ~37%, yielded the most favorable outcome, achieving high SEA (0.84 J/g) and EA (252 J) values. This improvement was mainly attributed to the epoxy filling internal voids and defects. Mechanical test results were further supported by SEM observations and validated through statistical correlation analyses. This work constitutes one of the first comprehensive studies to employ epoxy infiltration for defect mitigation in 3D-printed polypropylene structures. The proposed method offers a promising pathway to enhance the performance of lightweight, impact-resistant 3D-printed structures for advanced engineering applications. Full article

Topological magnetic textures—including skyrmions, Bloch points, and chiral bobbers—exhibit extraordinary properties with significant potential for advanced information technologies. However, achieving precise control over specific topological states requires an understanding of their formation mechanisms and stabilization criteria in nanoscale materials. Our work addresses this challenge by investigating how tailored interactions in ferromagnetic multilayers govern the emergence of specific topological configurations. In this study, we investigate topological magnetic structures in ferromagnetic multilayers, focusing on the interplay between magnetic anisotropy, the Dzyaloshinskii–Moriya interaction, and interlayer exchange coupling. We demonstrate how these interactions govern the formation and stability of diverse 3D topological configurations, including Bloch-point-like structures, conical skyrmions, chiral bobbers, and skyrmion tubes. Optimal conditions for stabilizing specific defect types have been identified and phase diagrams have been constructed as a function of material parameters. These findings provide insights into the controlled design of magnetic textures for advanced spintronic applications. Full article

The confluence of computer vision and natural language processing has yielded powerful vision language models (VLMs) capable of multimodal understanding. We applied state-of-the-art VLMs for quality monitoring of the shoe assembly industry. By leveraging the ability of VLMs to jointly process visual and textual data, we developed a system for automated defect detection and contextualized feedback generation to enhance the efficiency and consistency of quality assurance processes. We conducted an empirical evaluation by evaluating the effectiveness of the developed VLM system in identifying standard procedures for assembly, using the video data from a shoe assembly line. The experimental results validated the potential of the VLM system in detecting the quality of footwear assembly, highlighting the feasibility of future practical deployment in industrial quality control scenarios. Full article

Background: Post-traumatic osteomyelitis (PTO) of the lower extremity is among the most demanding problems in orthoplastic reconstructive surgery. It typically follows open fractures, failed osteosynthesis, or implant infection. Effective management requires coordinated infection control, stable skeletal fixation, and timely vascularized soft-tissue coverage. Methods: We conducted a retrospective case series of 20 consecutive patients with PTO of the lower limb treated between 2021 and 2024 at a tertiary orthoplastic center. All patients underwent radical debridement, culture-directed intravenous antibiotic administration, and soft-tissue reconstruction using local muscle, fasciocutaneous, or free flaps; vascularized bone flaps were used to select nonunion cases. The primary outcomes were flap survival, complications, infection resolution, and limb salvage. Exploratory analyses included descriptive subgroup summaries by flap category. Results: Among 20 patients (15 men, 5 women; mean age 53.6 years), reconstructions included reverse/pedicled sural flaps (n = 9), hemisoleus muscle flaps (n = 7), medial gastrocnemius muscle flaps (n = 2), peroneus brevis muscle flaps (n = 2), and free flaps (n = 6), which comprised anterolateral thigh (ALT), medial femoral condyle (MFC) osteoperiosteal, deep circumflex iliac artery (DCIA) osteocutaneous, and radial forearm free flaps (RFFFs). Single-flap reconstructions were performed in 13 cases, whereas multistage/multiflap strategies were used in 7. Overall flap survival was 90%. Major flap complications comprised partial necrosis in two reverse sural flaps and one complete loss of a reverse sural flap; two patients had minor wound dehiscence. Infection resolved in 18/20 patients (90%; 95% CI ≈ 0.70–0.97). One patient requested below-knee amputation due to persistent nonunion associated with a pathological fracture. At a mean 10-month follow-up, all limb-salvaged patients were ambulatory. Conclusions: Effective reconstruction of PTO is improved by using a patient-specific algorithm that considers the defect location, vascular status, and host comorbidities. Local muscle and fasciocutaneous flaps remain dependable for most defects, with free or vascularized bone flaps reserved for composite or recalcitrant cases. Early referral to high-volume centers, radical debridement, and orthoplastic collaboration are critical for optimizing limb salvage. Our findings should be interpreted in light of the study’s retrospective design and small sample size. Full article