An eco-friendly and efficient route for synthesizing reduced graphene oxide (rGO) using Mimosa pudica leaf extract as a green reducing and stabilizing agent is reported. To address the limited understanding of structure–property relationships in green-synthesized rGO, a systematic study is performed. Analysis of graphene oxide (GO) and rGO using XRD, FTIR, Raman, and XPS confirms effective deoxygenation and restoration of sp2 domains. The spin-coated thin films of Mimosa pudica leaf extract reduced graphene oxide (MP-rGO) exhibit increased conductivity with a sheet resistance (Rs) of 373 ± 18 kΩsq−1 and high optical transmittance of 93.61
This study explores the MnO doping effects on mechanical, non-isothermal crystallization kinetics and luminescence properties of 30SiO2-50B2O3-(20-x)Li2O-xMnO (where, x = 4, 6, 8, and 10 mol%) glasses produced by melting and quenching method. Makishima-Mackenzie (MMR) and Rocherulle (RM) models are used to study the mechanical properties of the prepared glass samples. The FTIR spectra confirm the presence of different structural units of borate and silicate in the prepared glass samples. The Kissinger and Augis-Bennett models are used to study the activation energies (Eg, Er, and Ec) of the prepared glasses. The substitution of Li2O with MnO is responsible for weakening the glass network, and, consequently, glass with the highest doping of MnO (LM-10, 10 mol%) exhibits lowest network rigidity. Glass with the lowest doping of MnO (LM-4, 4 mol%) exhibits the highest activation energy (Eg) for crystallization. While glass with the highest doping of MnO (LM-10, 10 mol%) exhibits the lowest activation energy (Eg) of crystallization. The fragility index shows a decreasing trend with MnO doping and heating rates, while thermal stability shows an increasing trend with increasing MnO doping. The value of correlated colour temperature lies between 1692 and 1712 K and confirms that the MnO-doped borosilicate glasses predominantly emit in the bluish-pink region, highlighting their potential utility in photonics applications.
The automation of road damage detection and classification is vital for enhancing road safety and enabling cost-effective, proactive infrastructure maintenance. This study introduces an advanced transfer learning-based framework designed to achieve precise and efficient road damage classification, validated on the RDD 2022 dataset, which spans six geographically diverse regions—India, Japan, Czech Republic, Norway, the U.S, and China. To ensure both specialization and generalization, the framework employs a two-phase fine-tuning strategy. A region-specific model is trained on country-wise datasets to capture localized damage patterns with high precision, while a generalized model leverages a cross-regional dataset encompassing diverse damage classes from all six countries, enhancing adaptability and ensuring robust performance across varying road conditions. To extract high-dimensional, discriminative representations of road damage images, the framework integrates twelve state-of-the-art deep learning architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, ResNet152, InceptionV3, DenseNet121, EfficientNetB0, and transformer-based models such as Vision Transformer and Swin Transformer. These extracted features undergo further refinement through K-means clustering, which enhances classification accuracy by structuring the feature space. Additionally, LIME-based feature selection identifies the 100 most salient features, ensuring a balance between computational efficiency and classification performance. The optimized feature vectors serve as inputs for multiple Machine learning classifiers, including SVM, KNN, Decision Trees, Gaussian Naïve Bayes, Logistic Regression, MLP, AdaBoost, XGBoost, Random Forest, LGBM (Light Gradient Boosting Machine), and Extra Trees, ensuring scalable and robust classification across diverse datasets. By integrating deep learning-based feature extraction, clustering-based refinement, and interpretable feature selection, the proposed methodology establishes a new benchmark in automated road maintenance. This comprehensive approach offers a scalable, high-precision solution that effectively balances accuracy, efficiency, and adaptability, making it well-suited for both localized and generalized datasets.
Reducing carbon emissions from diesel engines has increased interest in oxygenated biofuels that offer cleaner combustion without engine modifications. Among higher alcohols, n-pentanol offers a promising combination of high oxygen content, renewable origin, and favorable combustion properties. This study experimentally demonstrates an additive-free strategy for operating a 40% n-pentanol/diesel blend (D60P40) in a common-rail direct injection diesel engine through the synergistic integration of advanced injection timing (15 degrees BTDC) and exhaust gas recirculation (10% and 20%) across 20-80% engine loads. Advancing injection timing to 15 degrees BTDC improved combustion, with an 8.85% increase in peak cylinder pressure and 15.19% higher heat release rate compared to diesel. Brake thermal efficiency (BTE) of D60P40 at this timing was only 2.36% lower than diesel, while retarded timing (9 degrees BTDC) reduced BTE by 15.7%. EGR at 20% reduced NOx by 15.15%, but increased HC and CO due to thermal quenching. However, the inherent oxygen in pentanol helped limit these increases. The synergy of advanced injection timing with moderate EGR resolved the NOx-CO/HC trade-off, maintaining combustion efficiency while reducing emissions. These findings demonstrate a viable, retrofitting-free pathway for diesel engine decarbonization using oxygenated fuel blends, supporting global efforts toward cleaner and sustainable transport energy systems.
In this study, the synthesis, single-crystal X-ray analysis, photophysical properties, DFT, and docking studies of a chalcone analog (2E,4E)-5-(4-methoxyphenyl)-1-(thiophen-2-yl) penta-2,4-dien-1-one (MPT) are reported. The synthesis of the title compound was achieved by the conventional Claisen–Schmidt reaction. The identity of the title compound was confirmed by spectroscopic techniques, and by X-ray diffraction analysis of single crystals. In the solid state, the molecule exhibits weak π–π interactions. The fluorescence spectrum of the title compound in the solid state shows a broad emission with a λmax at 517 nm and a significant Stokes shift of 142 nm. Using the Z-scan technique the third-order nonlinear optical properties of MPT were explored. The compound displayed a two-photon absorption (β = 2.36 × 10–6 cmW−1), nonlinear refraction (n2 = 2.37 × 10–10 cm2W−1), and a third-order nonlinear susceptibility (χ(3) = 3.84 × 10–8 esu). Theoretical calculations provided insights into structural properties, including the HOMO–LUMO energy gap. Molecular docking studies revealed that the synthesized compound showed the binding affinities of -11.1, -4.9, and -6.6 kcal/mol against the SARS-CoV-2 receptor (PDB ID: DZP). Furthermore, docking studies also suggest that the molecule may inhibit ORF8, a viral immunoglobulin-like (Ig-like) domain protein essential for SARS-CoV-2 binding, fusion, and entry into host cells.
