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Performance comparison of tree-based and neural network models for wear prediction in coated and uncoated Al6061
This study compares the performance of machine learning (ML) and deep learning (DL) models in predicting the dry sliding wear of uncoated Al6061, plasma-sprayed flyash-Al2O3 and flyash-SiC coatings. Ensemble models, including random forest (RF), XGBoost and LightGBM, along with neural network models such as multilayer perceptron (MLP) regressors, backpropagation neural networks (BPNN) and deep neural networks (DNN), were trained on experimental data that varied load, sliding speed and sliding distance. The dataset was scaled and split into training (80%) and testing (20%) subsets. Model performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE) and root mean square error (RMSE). ML regressors accurately predicted the properties of uncoated alloys, with R2 scores above 0.97, though their performance decreased on coated samples. RF experienced the largest decline in accuracy, particularly flyash-SiC (R2 = 0.736). Gradient boosting models exhibited improved robustness, with LightGBM achieving R2 values of 0.977, 0.936 and 0.794 for uncoated, flyash-Al2O3 and flyash-SiC samples, respectively. Neural networks outperformed tree-based methods for coated systems, with MLP and DNN attaining R2 values up to 0.992, alongside lower MAE and RMSE. SEM analysis corroborated the predictions, showing severe wear in uncoated alloys, minimal surface damage in flyash-Al2O3 coatings and cracking and delamination in flyash-SiC coatings. 2026 The Authors. -
Efficient cyclization of 1,5-dienes to industrially important terpenes using amorphous metal aluminophosphate catalyst: A continuous flow approach
Amorphous metal aluminophosphate was used for the first time in a continuous flow process for the cyclization of pseudoionones. A series of metal aluminophosphates was synthesized by a simple coprecipitation method and characterized using various techniques to determine the physico-chemical properties of the materials. The synthesized metal aluminophosphates were evaluated as catalysts in the cyclization of pseudoionone via a continuous flow process utilizing a coil flow reactor. All catalysts facilitated the formation of ?- and ?-isomers of ionones through the cyclization of pseudoionone. Calcium aluminophosphate demonstrated a higher catalytic efficiency of 96 % compared to other reported methods, which is attributed to its large surface area, surface acid sites, and reduced by-product inhibition. The reaction was optimized by varying parameters such as catalyst amount, reaction temperature, pressure, and retention time and compared with a batch process. The scope of the reaction was investigated by employing a variety of terpene ketones. A suitable reaction mechanism was proposed which highlights the role of the surface acidity of the catalyst in the formation of a cyclized ring. The catalyst exhibited excellent reusability, maintaining its efficiency over three consecutive cycles with minimal degradation. 2025 The Authors -
Solid state, rapid mechanochemical route for TiO2 coated Schiff-base polymer as adsorbent for the exclusion of hexavalent Cr from water
The removal of hexavalent Cr from water is vital considering its harmful and carcinogenic effects on human health as well as the environment. Effective exclusion of Cr(VI) provides reliable water to consume, impedes bioaccumulation, and mitigates environmental pollution. The present work details the rapid, ecofriendly, solvent-free mechanochemical route for the development of a polymeric Schiff-Base-wrapped TiO2 (SBP@TiO2) nano-adsorbent for the effective removal of Cr(VI) from water. The comprehensive understanding of the structural and chemical characteristics of the newly synthesized materials were examined through Fourier transform infrared (FTIR) spectroscopy, X-Ray Diffraction (XRD), and Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) spectroscopy. To assess the adsorption capacity, kinetics, and equilibrium of Cr(VI) adsorbate on adsorbent material (TiO2 and SBP@TiO2) and to understand the interplay between the critical parameters and their impact on adsorption, a series of batch adsorption studies were carried out. The adsorption equilibrium data for the Cr(VI) adsorption process fitted well with the Freundlich isotherm model of adsorption and adsorption kinetics disclosed that the data are in excellent agreement with R2 values of 0.98721 for the pseudo-second-order, indicating that the sorption process is by chemisorption. Thermodynamic measurements revealed that the adsorption of Cr(VI) on SBP@TiO2 was spontaneous and endothermic, as corroborated by the ?ve value of ?Go and the +ve value of ?Ho, respectively. It was discovered that the sorption of 10 and 50 ppm of Cr(VI) on SBP@TiO2 was 96% and 75.4% under optimal conditions, respectively. In contrast, the sorption study of Cr(VI) on TiO2 under identical conditions was found to be 49%. The study found that surface functionalization of TiO2 by SBP admirably improved the adsorption capacity, signifying SBP@TiO2 as an efficient Cr(VI) adsorbent. 2024 The Authors -
Microplastic pollution and its ecotoxicological impact: Evidence from Vembanad Lake and zebrafish studies
Microplastic (MP) contamination is a threat to Earth and its aquatic systems by destabilizing ecological equilibrium. This study examined the distribution and impact of MPs in Vembanad Lake, an urbanized estuarine system in Kerala, India. MPs were identified at every sampling location, while sites 2, 3, and 4 exhibited peak contamination levels of 79 MPs/L. A survey among local inhabitants reported a decline in fish populations over many years. Analytical characterization using SEM, EDS, FTIR, and Raman spectroscopy revealed the dominance of HDPE, LDPE, PS, PET, PP, and PVC polymers. The presence of chromium (Cr), sodium (Na), aluminum (Al), and silica (SiO?) in MPs further enhances additional toxicity risks. Zebrafish exposed to the prevalent MPs for 21 days exhibited severe epithelial necrosis alongside goblet cell hyperplasia and muscle fiber degeneration, demonstrating systemic cytotoxic effects. These findings underscore the ecological threat of MP pollution and emphasize the urgent need for mitigation strategies to protect aquatic biodiversity. 2025 Elsevier B.V. -
A physics-informed neural network framework for consolidation parameter prediction using controlled clay-sand mixtures
This paper introduces a novel Physics-Informed Neural Network (PINN) model for predicting the coefficient of consolidation (Cv) in high plasticity clays. The model was trained from experimental data obtained from controlled clay-sand mixtures. The input parameters include clay content, Atterberg limits, initial void ratio, compaction energy, applied pressure and consolidation characteristics like compression index (Cc) and volumetric compressibility (mv). Additional parameters like plasticity index, porosity, activity-clay interaction and compaction efficiency were derived from feature engineering. The proposed PINN model combines data-driven loss and physics-based loss into a total loss function. The physics loss includes three constraints derived from modified Kozeny-Carman equations, activity-based mineralogical relations, and compression-volume consistency. Hyperparameter optimization identified the optimal configuration: 800 epochs, learning rate 0.001, architecture [128, 64, 32], and physics loss weights distributed as 0.7, 0.25, and 0.05. Five-fold cross-validation demonstrated robust performance (R2 = 0.9903 0.0026), significantly outperforming baseline neural networks (R2 = 0.9682 0.0126, p = 0.0116) with 73.9% reduction in Root Mean Square Error (RMSE = 6.37 10-11 m/s) and 5.71% improvement in Mean Absolute Percentage Error (MAPE = 4.48%). External validation showed the PINN (R = 0.9968) substantially outperformed empirical correlations (best R2 = 0.1636) and conventional machine learning models (best R2 = 0.9878). SHapley Additive exPlanations (SHAP) interpretability analysis validated physically meaningful decision-making, with plastic limit and activity emerging as primary drivers. This framework provides a transferable, physics-consistent solution applicable across diverse clay types for foundation design and site characterization. Copyright 2026. Published by Elsevier B.V. -
Hybrid CMNV2: DeepFake faces classification and recognition using deep learning methods
Deepfake detection has become a critical component of digital forensics and security, as manipulated images and videos increasingly threaten trust in visual media. However, existing methods often struggle with robustness under post-processing operations such as JPEG compression, Gaussian blur, scaling, and filtering, and with the growing diversity and realism of face image modification (FIM) forgeries. This work proposes CMNV2, a hybrid architecture that integrates MobileNetV2 with a custom CAFFE block to enhance feature extraction and classification accuracy. By adding five additional layers to a pre-trained structure, the model demonstrates superior resilience against complex real-world conditions and achieves 99.10% accuracy across multiple datasets, outperforming 13 baseline CNN models. The study trained and tested CMNV2 on 5,000 images (real and deepfake faces), using a combination of deep neural networks (DNNs), transfer learning (TL), and deep learning (DL) techniques. Compared to 13 CNN-based architectures, the proposed model achieved superior performance across some important evaluation metrics, including accuracy, precision, recall, F1-score, error rate, and computational efficiency. These results highlight hybrid CMNV2 as a robust and efficient solution for deepfake face detection and classification, with potential applications in security, healthcare, and education. 2025 -
Explainable artificial intelligence framework for wind turbine fault detection using random forest Extreme gradient boosting hybrid model
Though wind energy has great promise for clean energy generation in India, operational inefficiencies and underutilization still present major obstacles. Although installed wind capacity exceeds 51. 3 GW, actual power generation is still significantly lower than predicted mostly because of weak fault detection and maintenance techniques. Existing machine learning (ML) methods offer high accuracy but typically lack transparency in their forecasts, therefore making it difficult for engineers to correctly interpret and act on model outputs. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. This research aims to develop an understandable and high-performance anomaly detection model using real-time SCADA data from an Indian wind plant. A hybrid ensemble approach integrating Random Forest and XGBoost is proposed, combined with Local Interpretable Model-Agnostic Explanations (LIME) to provide local interpretability of predictions. The model was trained and evaluated on actual SCADA data using SelectKBest for feature selection, SMOTE for handling class imbalance, and RandomizedSearchCV for hyperparameter optimization. The tuned hybrid model achieved outstanding performance, with an accuracy of 0.9995, F1-score of 0.9995, and minimal error rates (MAE and MSE = 0.00052). LIME-based interpretability highlighted key features driving predictions, with Nacelle Temperature and Gearbox Temperature consistently emerging as critical indicators of turbine braking events, underscoring the importance of thermal variables in fault diagnosis. The findings suggest that interpretable machine learning not only enhances root cause analysis but also supports proactive maintenance, particularly by emphasizing improvements to cooling systems to reduce thermal failures. By providing transparent and reliable insights, the proposed solution enables wind farm operators to make informed, timely decisions, thereby improving turbine reliability and energy yield. The framework is practical, explainable, and well-suited for deployment in smart wind farms, aligning with the United Nations Sustainable Development Goals, including SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production) 2025 The Author(s) -
Study of internal heat source generated natural convection with sinusoidal and non-sinusoidal time-periodic vertical oscillations
This study explores the effect of gravity modulation on natural convection induced by a uniform internal heat source within a fluid-saturated porous medium, a topic of growing relevance in advanced thermal management applications. Four distinct gravity waveforms, square, sinusoidal, triangular, and sawtooth, are examined under three boundary condition combinations: Rigid-Adiabatic-Rigid-Isothermal (RARI), Rigid-Adiabatic-Free-Isothermal (RAFI), and Free-Adiabatic-Free-Isothermal (FAFI). A novel analytical framework is developed by integrating a Maclaurin series expansion with a minimal FourierGalerkin approach to derive a generalized Lorenz model. Linear stability analysis, via a modified Venezian method, to determine the critical internal Rayleigh number and its correction due to modulation. A weakly nonlinear analysis based on the GinzburgLandau equation also provides closed-form expressions for the mean Nusselt number, capturing heat transfer characteristics. The findings demonstrate that square wave modulation most effectively enhances heat transport, followed by sinusoidal, triangular, and sawtooth forms. The influence of key physical parameters reveals that increasing porous parameter (?2) and Brinkman number (?) suppress heat transfer, as do higher Prandtl numbers (Pr) and modulation frequencies (?). FAFI yields the highest heat transfer among the boundary types, while RARI performs the least. The novelty of this work lies in the combined analytical treatment of diverse waveform modulations while considering a uniform internal heat source and boundary condition for natural convection. 2025 The Author(s) -
An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining
Blast induced ground vibrations (BIGV) pose critical challenges in surface mining, threatening structural integrity, worker safety, and environmental compliance. This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. Unlike empirical equations that lack generalizability or black box ML models with limited transparency, the proposed approach embeds domain specific physical laws while leveraging data driven learning to improve both predictive accuracy and interpretability. A multiobjective optimization scheme is employed to balance competing goals: minimizing peak particle velocity (PPV), maximizing fragmentation efficiency, and reducing operational costs. Crucially, the framework incorporates Explainable AI (XAI) techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME) and uncertainty quantification (UQ) methods based on Bayesian Neural Networks to provide insight into model decisions and confidence in predictions. Validation across five operational mines in the Godavari Valley Coalfields (India) demonstrates strong generalizability, achieving up to a 20% reduction in RMSE compared to empirical baselines. The improvement is statistically significant (p<0.01) as confirmed through a paired t-test across cross-validation folds. These findings highlight that a physics informed, explainable, and uncertainty aware AI framework can substantially improve vibration prediction, ensure regulatory compliance, and support safer, more sustainable blasting operations in modern surface mining. 2025 The Author(s) -
MHD Maxwell nanofluid flow over a porous conical surface: A fractional approach
The current novel study focuses on the two-dimensional magnetohydrodynamic flow of fractional Maxwell nanofluid through porous conical geometry under convective boundary conditions. The nanofluids considered for the study are suspensions of single and multi-walled carbon nanotubes with blood as the base fluid. Fractional-ordered governing equations are transfigured into non-dimensional forms using appropriate transformations. The finite difference approximations are obtained by discretizing the momentum and energy profiles. The results of both profile are plotted against various physical flow-pertaining parameters. It is evident, that multi-walled carbon nanotubes consistently show higher velocity profiles and lower temperature phases than single-walled carbon nanotubes nanofluid across all embedded parameters. Further, the study revealed that the absence of magnetic parameter improves by 11.36% of velocity distribution and the presence of heat source parameter improves by 18.37% of temperature distribution. This framing highlights the convergence criterion of the findings with previous work, emphasizing both reliability and accuracy within the range of 10?4 to 10?6. Graphical representation concludes that the model involving the fractional technique is superior to the integer one. Thus, achievement demonstrates practical application potential in optimizing the efficiency of fluid heating and cooling processes, underscoring its importance in thermal management. 2025 -
Electro-osmotic peristaltic streaming of a fractional second-grade viscoelastic nanofluid with single and multi-walled carbon nanotubes in a ciliated tube
Mathematical modeling of carbon nanotubes (CNTs) in biological fluids is essential for drug delivery, biosensing, and targeted therapy. This study explores the transport dynamics of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) based nanofluids under electro-osmotic peristaltic flow influenced by ciliary motion. A microfluidic channel lined with cilia, hair-like structures found in human airways and reproductive tracts, is considered. The coordinated beating of cilia generates a wavelike motion that propels the surrounding biological fluid. When an electric field is applied across the channel, electro-osmotic forces further modify the flow, affecting velocity and temperature distribution. A nanofluid, consisting of CNTs suspended in a base fluid, flows through this cilia-driven microchannel. The transport process is governed by electro-osmosis, heat transfer, and thermal radiation effects, with simplifications based on long-wavelength and low Reynolds number assumptions. The Caputo fractional model and DebyeHkel linearization are used to analyze the interaction between electro-osmotic forces and thermal-mechanical effects. The results reveal that the negative Helmholtz-Smoluchowski parameter (Uhs) reduces the axial velocity in the core whereas it increases in the periphery of the channel, while the opposite trend is observed for positive Uhs. Longer cilia (?) and higher electro-osmotic parameter (m) slow the core flow while accelerating peripheral transport. Thermal effects indicate that an increased heat source (B) raises temperature and axial velocity, whereas a higher nanotube volume fraction (?) enhances axial velocity but reduces temperature. Notably, MWCNTsexhibit superior axial velocity and temperature enhancement compared to SWCNTs. These outcomes provide valuable insights into electro-osmotic cilia-driven nanofluid transport, offering a theoretical foundation for optimizing microfluidic and biomedical applications. 2025 -
Enhanced mechanical properties of CNT/Graphene reinforced PLA-based composites fabricated via fused deposition modelling
This study addresses the mechanical properties of polylactic acid (PLA) composite materials reinforced by CNTs and graphene, produced using the Fused Deposition Modeling process. The collaborative impact of graphene and CNTs upon the primary mechanical attributes including UTS, yield strength, modulus of elasticity, and impact resistance has been investigated. Three distinct CNT's weight percentages of 0.5, 1, and 1.5 have been fabricated under constant graphene content at 0.5 wt%. These findings revealed that the UTS of pure PLA were 28 MPa, whereas for the composite with 1.5 wt% CNT and 0.5 wt% graphene, it was raised to 48 MPa. From 2.6 GPa to 4 GPa the young's modulus enhancement is seen and the yield strength enhancement is seen up to 28 MPa for the composite from 20 MPa of pure PLA. The impact strength was greatly enhanced from 1.2 J for pure PLA to as high as 4 J for the composite comprising 1.5 wt percentage CNT and 0.5 wt percentage graphene. 2025 The Author(s) -
Photocatalytic and antimicrobial properties of Boerhavia diffusa bio-callus synthesized Silver nanoparticles
Plant tissue culture plays a pivotal role in plant biotechnology, and offers innovative and reliable avenues for synthesizing nanoparticles. The approach is safe, replicable, and efficient for therapeutic and environmental sustainability. Despite the proven efficiency of green synthesis approaches, plant callus extracts for nanoparticle synthesis remain moderately investigated. The current study bridges the gap by synthesizing ecofriendly silver nanoparticles (Ag-NPs) using callus extracts of Boerhavia diffusa (Punarnava), an important medicinal plant with proven potential pharmacological properties. These synthesized Boerhavia diffusa-mediated Ag-NPs (BD-Ag-NPs) were characterized using UV-Vis spectroscopy, SEM, FTIR, and XRD. Spectral analysis showed spherical-shaped BD-Ag-NPs with an average size of 9 nm at wavelength 420 nm. Energy-dispersive X-ray (EDX) analysis revealed that silver ions constituted 51.78 % of the total weight of the nanoparticle solutions, while the crystalline structure of the BD-Ag-NPs was confirmed through XRD. Phytoconstituents present in the callus were utilized for capping and the reduction of Ag ions to Ag-NPs was confirmed through FTIR analysis. In addition, BD-Ag-NPs exhibited functional properties like textile dye degradation and broad-spectrum antimicrobial activities against bacterial and fungal pathogens. The current study highlights the potential of employing callus-derived nanoparticles for sustainable environment and biomedical applications. This study advances the application of green nanoparticle synthesis using tissue culture systems and makes significant contributions to addressing global challenges. 2025 The Authors -
Effects of DESI and GW observations on f(T) gravitational baryogenesis
Baryogenesis refers to the physical process responsible for generating the observed baryon asymmetry in the early universe. The presence of a nonzero baryon number density suggests a surplus of matter over antimatter. In this study, a novel approach is proposed to verify the direct consequences of late-time observations on gravitational baryogenesis. The incorporation of two key data sets, DESI and gravitational wave observations, makes the analysis more intriguing. In the teleparallel framework, the methodology connects the primordial time to the late time. The intermediating epochs are also investigated with the help of the deceleration parameter. Our results show that the net remaining asymmetry yields a baryon-to-entropy ratio in excellent agreement with observations. 2025 The Authors. -
Quantum corrections in general relativity explored through a GUP-inspired maximal acceleration analysis
A maximun acceleration analysis by Pati dating back to 1992 is here improved by replacing the traditional Heisenberg Uncertainty Principle (HUP) with the Generalized Uncertainty Principle (GUP), which predicts the existence of a minimum length in Nature. This new approach allows one to find a numerical value for the maximum acceleration existing in Nature for a physical particle that turns out to be [Formula presented] that is, a function of two fundamental physical quantities such as the speed of light c and the Planck length lp. An application of this result to black hole (BH) physics allows one to estimate a new quantum limit to general relativity. It is indeed shown that, for every real Schwarzschild BH, the maximum gravitational acceleration occurs, without becoming infinite, when the Schwarzschild radial coordinate reaches the gravitational radius. This means that quantum corrections to general relativity become necessary not at the Planck scale, as the majority of researchers in the field think, but at the Schwarzschild scale, in agreement with recent interesting results in the literature. In other words, the quantum nature of physics, which in this case manifests itself through the GUP, appears to prohibit the existence of real singularities, in this current case forbiddiing the gravitational acceleration of a Schwarzschild BH from becoming infinite. 2025 The Authors -
Inter-relational dynamics of factors affecting the emergence of orphan drugs; [Dynamique interrelationnelle des facteurs influennt lergence des micaments orphelins]
Orphan drugs are medications that are produced for the treatment of rare diseases. As there is less number of patients, the drug manufacturing companies are not keen in producing these drugs. Due to high costs of research and development and low profitability, companies do not want to invest in manufacturing of orphan drugs. Several laws have been passed by Governments of different nations to encourage the development of orphan drugs and make it available to patients. This study explores the interrelation dynamics of factors that has resulted in the greater availability of orphan drugs in recent times. Ten factors: internet technology, legislation, online patient support groups, government subsidiary, biotechnological advancements, corporate social responsibility, awareness and diagnosis of rare diseases and exclusive budgeting by pharmaceutical industries for orphan drugs related research and development and production were taken for the study. With a sample size of 38 experts, the technique of decision-making trial and evaluation laboratory (DEMATEL) was used for the study. It was found that information technology, legislation, support groups, and budget were the causes and the factors awareness, diagnosis, medicine availability, subsidiary, CSR and biotechnology emerged to be the effect. 2024 Acadie Nationale de Pharmacie -
Study of multilayer flow of non-Newtonian fluid sandwiched between nanofluids
This theoretical investigation examines the nonlinear convective heat transport and multilayer flow of a non-Newtonian fluid within a vertical slab, incorporating viscous heating effects. The middle layer of the slab contains a third-grade fluid, while the outer layers are filled with a water-based Ag-MgO hybrid nanoliquid. Continuity in temperature, heat flux, velocity, and shear stress is maintained at the interfaces of the fluid layers. The thermal buoyancy force is modeled using the nonlinear Boussinesq approximation. The governing system comprises conservation equations for mass, momentum (Navier-Stokes), and energy for each of the three layers. These differential equations are non-dimensionalized, and the resulting dimensionless four-point nonlinear boundary value problem is transformed into a two-point boundary value problem before being solved numerically. For limiting cases, analytical and semi-analytical solutions are computed and used as benchmark results to validate the numerical method employed. Entropy generation analysis indicates that higher third-grade fluid parameters reduce the magnitude of velocity and temperature fields, as well as entropy production across all regions. The third-grade fluid parameter shows a decreasing influence on velocity and temperature fields throughout the system. The continuity of interfacial conditions induces a dragging effect; despite the absence of third-grade fluid parameters in regions I and III, their influence is apparent in these regions. The Bejan number slightly decreases at the walls with increasing third-grade fluid parameters, exhibiting a dual effect in the third-grade fluid layer. Near the walls, the Bejan number decreases as the nanoparticle volume fraction increases. Findings of this work may have applications in polymer industries and processes involving high temperatures. 2024 -
Cr2C MXene quantum dots for selective detection of mercury ions
Mercury (Hg), a highly toxic environmental contaminant that presents significant ecological and health risks, even at low concentrations. There is an urgent need for precise and sensitive methods for detecting Hg2+ ions in the environment. In recent years, a new class of 2D layered materials, MXene, has gained enormous attention due to their unique properties, such as high surface area, oxidation resistance, thermal and chemical stability, electrical and thermal conductivity. This study presents the synthesis and characterization of Cr2C MXene quantum dots (MQDs) derived from Cr2CTx nanolayered MXene sheets via the probe-sonication method. The Cr2C-MQDs were characterized using XRD, FTIR, SEM-EDS mapping, and Zeta potential analysis. The vibrant green fluorescence material, Cr2C-MQDs, was investigated for Hg2+ detection, which exhibited high selectivity and stability with a limit of detection of 30.7 nM. The sensing mechanism is attributed to the strong affinity of Cr2C MXene quantum dots for Hg2+ ions. 2025 Elsevier B.V. -
Optical properties of MnTe2 few-layer quantum dots
Quantum dots (QDs) are gaining attention as a possible emissive material that might be used in flexible optoelectronic and photonic systems. In the present work, the temperature-dependent photoluminescence (TDPL) property of manganese di-telluride (MnTe2) QDs was investigated. The room-temperature PL is attributed to the abrupt breakage of the large-area MnTe2 nanosheets by ultrasonication, which integrates defect-mediated localized trap states inside the electronic bandgap. As a result, deliberately generated defect states ultimately generate such PL emission of QDs. Density functional theory (DFT) results further validate the experimental interpretations of the origin of TDPL. In addition, through an in-situ liquid diffusion approach, the QDs were also integrated into a NaCl matrix. Due to light scattering properties, the hybrid crystals exhibit fluorescence centres at various excitation wavelengths. These results suggest that these MnTe2 QDs can be used as an effective basis for future flexible optoelectronic applications. 2024 Elsevier B.V. -
Therapeutic profiling of Saraca indica bark oil silver nanoparticles: Bioactivity and cytocompatibility in human keratinocyte (HaCaT) cells
This study explores the potential of silver nanoparticles synthesized from Ashoka (Saraca indica) bark oil, which has properties as a natural therapeutic agent. The silver nanoparticles (Ag-NPs) were produced using a green synthesis method from the Saraca indica bark oil and characterized through UV-Vis spectrophotometry, FTIR, and SEM techniques. Fungal infections are mainly caused by Candida spp., especially Candida albicans, which significantly contributes to diseases like candidiasis. The antifungal and antibacterial activities were tested against Candida albicans and Bacillus subtilis. Using the disk-diffusion method, different concentrations of Ag-NPs were evaluated and compared with fluconazole and streptomycin. Results showed that the inhibition zones were concentration-dependent, with a maximum inhibition zone of 21.751.768 mm, 21.751.06 mm at 100 g/mL against C. albicans and B. subtilis. The DPPH assay showed 62.17 % antioxidant activity at 80 g/mL, and IC?? values were 36.43 g/mL for AO-Ag NPs compared to 26.88 g/mL for crude oil. The increasing resistance to antifungal drugs and limited effective treatments highlight the need for alternatives. The DPPH antioxidant assay confirmed the nanoparticles free radical scavenging ability, indicating antioxidant potential. An albumin denaturation anti-inflammatory assay revealed notable inhibition by the nanoparticles compared to Ascorbic acid. Cytotoxicity was assessed on human keratinocyte (HaCaT) cells, showing dose-dependent cytocompatibility, with > 90 % viability at lower concentrations and 12.31 1.62 % viability at 100 g/mL. Compared to crude bark oil and positive controls, the nanoparticles exhibited enhanced bioactivity with reduced cytotoxicity to normal skin cells. Morphological observations also suggested apoptosis, possibly linked to ROS-mediated oxidative stress pathways. Overall, this research indicates that Saraca indica-silver nanoparticles are cost-effective, eco-friendly, and biocompatible, with antimicrobial, antioxidant, anti-inflammatory, and low cytotoxic properties. These properties support their potential use in developing nanomedicine treatments for infections and inflammation. 2025 The Authors