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Room temperature green synthesis and time resolved kinetics of formation of xanthan gum stabilised silver nanoparticles with catalytic and antibacterial potential
Abstract Well-defined and stable, spherical silver nanoparticles (AgNPs) were synthesized at room temperature by a green approach using xanthan gum (XG) as a reducing and stabilizing agent. The effect of various concentrations of silver nitrate on the formation kinetics of AgNPs at 23 °C and pH 10 was studied. The nanoparticles formation with XG as a reducing agent followed a first-order reaction kinetics. The denaturation of XG from a helix to a random polymer coil was achieved at 23 °C and pH 10. Renaturation of XG was not observed at room temperature, and the nanoparticles were stable against aggregation during prolonged storage of more than 8 months. The formation of AgNPs was studied using UV-Vis absorption spectroscopy, and a strong surface plasmon resonance peak centred around 407–413 nm confirmed the presence of nanoparticles. The optical bandgap of the nanoparticles was estimated to be in the range of 2.46 to 2.55 eV. Transmission electron microscope (TEM) images showed spherical and non-agglomerated nanoparticles of 10–22 nm size range. The Fourier-transform infra-red spectroscopy (FTIR) revealed the presence of an organic layer (due to XG) on the surface of the nanoparticles. The nanoparticles were highly effective in the degradation of a model organic pollutant, 2-nitrophenol, exhibiting 80% degradation within 10 min, with a pseudo-first-order rate constant of 0.211 min − 1 . The AgNPs showed size-dependent favourable antibacterial potential against Staphylococcus aureus and Salmonella typhimurium .
Direct Synthesis of Amide-Linked COFs via Ester-Amine Exchange Reaction
Targeting dendritic cell-specific TNFR2 improves skin and joint inflammation in a murine model of psoriatic arthritis
A Unified Picture of Radical Anion Photoredox Chemistry
Associations between dynapenic abdominal obesity and diabetes in middle aged and older Chinese
Stepwise approach to alzheimer’s disease diagnosis in primary care using cognitive screening, risk factors, neuroimaging and plasma biomarkers
Grain Boundary Oxygen Improving the Acidic Oxygen Evolution Reaction of Zn-RuO<sub>2</sub>@ZnO
Advanced deep learning modeling to enhance detection of defective photovoltaic cells in electroluminescence images
Abstract This paper discusses a deep learning approach for detecting defects in photovoltaic (PV) modules using electroluminescence (EL) images. The method addresses key challenges in two practical areas: Creating high-quality EL images to overcome imbalance issues in existing datasets. This is accomplished by employing generative adversarial network (GAN) properties to generate new images. Enhancing training efficiency and performance through a one-cycle policy with optimized learning rate settings, designed to overcome hardware limitations. The research highlights that while automatic defect classification in PV modules is gaining attention as an alternative to visual/manual inspection, the process remains challenging due to the inhomogeneous nature of cell cracks and complex backgrounds in crystalline solar cells. A comparison was made between popular deep learning models (Densenet169, Densenet201, Resnet101, Resnet152, Senet154, Vgg16, and Vgg19) to assess the effectiveness of our approaches on multiple variants of our dataset. We also observe a shift in the phenomenon of moving the threshold in regression estimates because of employing a policy that uses a dynamic threshold instead of a standard threshold (0.5). We have employed two different categorizations that use binary numbers; the first employs four classes (0%, 33%, 67%, and 100%), while the second employs eight classes that are identical to four classes. However, each class has two varieties (monocrystalline and polycrystalline) and a boundary beyond which results will be obtained. Based on the performance results, it was found that the pre-trained Resnet152 model achieved the highest classification accuracy (90.13% for Datasets) of all approaches. Additionally, we have demonstrated that approaches that utilize over-sampling have the greatest performance. These findings emphasize the strength and innovation of our approach, combining advanced data augmentation, adaptive thresholding, and optimized learning strategies. The proposed system not only achieved a peak classification accuracy of 90.13% using ResNet152 but also demonstrated high robustness, reduced training time, and superior generalization across defect types and cell categories. This positions our framework as a scalable and deployment-ready solution for real-world photovoltaic quality inspection systems.
Ultrabright 1650 nm-Emitting Biodegradable Organic Luminophores for NIR-IIbc Fluorescence Imaging
The effects of a concerning older adult abilities health education program to promote appropriate decision among acute myocardial infarction patients
Chirality-Assisted Self-Assembly of Low-Symmetry Noncovalent Capsules with Quantitative Diastereoisomeric Selection
Development, physicochemical, and sensory analysis of moringa oleifera l. powder added buffalo milk yoghurt with pharmacological potential
A Chemo-Enzymatic Platform for Furanolide Synthesis and Functional Exploration
Regional biodiversity monitoring reveals severe population decline of the Atlantic horseshoe crab (Limulus polyphemus) in Long Island Sound, USA
Enhancing Carbon Dioxide Reduction Performance on Copper via Surface Reconstruction Induced by Spontaneous Diazonium Salt Grafting
Deep learning-based automatic facial symmetry scoring in peripheral facial palsy
Abstract Unilateral peripheral facial palsy (PFP) results in facial asymmetry and functional impairment, reducing quality of life. Accurate, objective assessment is vital for monitoring and rehabilitation. This study presents an automated method utilizes standardized 2D photographs to visualize facial dynamics using heatmaps and calculates an objective symmetry score, quantifying movement symmetry. Retrospective analysis included 405 facial datasets from 198 PFP patients. Images were processed using a deep learning-based facial landmark detection and an affine alignment algorithm. Heatmaps were generated from grayscale difference images, and symmetry scores calculated by comparing mirrored facial halves within a defined mask. Stennert movement scores were correlated with symmetry scores using Spearman’s rank correlation. The method was applied successfully to all datasets, with symmetry scores ranging from 0 to 0.99 (mean 0.85 ± 0.12), varying by expression level. Heatmaps highlighted asymmetries matching clinical findings. In 85% of cases, Stennert trends aligned with symmetry scores; 9% showed stable Stennert scores but changing symmetry scores, suggesting higher sensitivity. Significant negative correlations (r = − 0.32 to − 0.66, p < 0.0001) confirmed greater clinical severity corresponds to lower symmetry scores. In conclusion, the automated method provides an objective, reliable, and accessible tool for assessing facial symmetry in PFP, thereby improving clinical evaluation and facilitating precise rehabilitation monitoring.