Browse Items (14421 total)
Sort by:
-
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) -
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 -
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 -
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) -
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) -
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) -
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 -
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. -
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. -
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 -
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 -
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. -
Relative thermal distribution between rectangular and convex parabolic fin in local thermal non-equilibrium model: a statistical analysis
This research investigates the numerical study of heat transfer between a fluid phase and the solid surface of a rectangular and convex parabolic fin, considering radiation and natural convection. This work helps to understand the design of advanced thermal devices employing fin systems under LTNE conditions. A mathematical model consisting of equations of solid and fluid phases is assumed to be distinct and coupled through local convective heat transfer. The governing equations of the mathematical model are non-linear ordinary differential equations reduced to dimensionless forms by applying suitable non-dimensional variables. The resultant set of equations, along with the boundary conditions are resolved using Runge-Kutta Fehlberg (RKF 45) method, and the obtained solution is validated with pre-existing results. The primacy of crucial aspects of the temperature profile and average Nusselt number is demonstrated through graphs. From the graphs, it is found that both solid and fluid phase temperature is more in the rectangular profile when compared to the convex parabolic profile. Further, the rate of heat transfer is analysed for both the profiles using response surface methodology (RSM). The response surface methodology is performed on derived rate of heat transfer as a response function for input factors for different parameters. Additionally, the accuracy of the model for rectangular and convex parabolic fin are found to be 97.47% and 96.36%, respectively. 2026 The Authors. -
Smartphone-integrated quantitative determination of bilirubin using luminescent carbon nanoparticles
Herein, using a green, cost-effective, and sustainable biomass precursor, luminescent carbon nanoparticles (CNP) are synthesized for selective bilirubin quantification. From high-resolution transmission electron microscopy (HRTEM) image, the prepared CNP exhibited spherical morphology and the crystallite size ranges from 8 to 16 nm. X-ray photoelectron spectroscopy (XPS) analysis confirmed the presence of both sp2 and sp3 hybridized carbon bonds within the carbon nanoparticles (CNP). Furthermore, Dynamic Light Scattering (DLS) analysis was performed to determine the hydrodynamic diameter of the CNP and the average particle size was found to be approximately 12.94 0.19 nm. Under ultraviolet (UV) radiation of 350 nm wavelength, the CNP displayed excitation dependent emission characteristics, having an average lifetime of 4.4 ns. The fluorescence intensity of carbon nanoparticles reduced considerably in the presence of Fe3+ ions and fluorescence turn ON was achieved upon the addition of different concentrations of bilirubin. The probe displayed remarkable selectivity towards bilirubin over other potential interferences. Using a sensing platform based on a mobile phone application, the fluorescent probe exhibited a limit of detection (LOD) of 32 nM. Moreover, the fluorescent probe was efficiently employed for the detection of bilirubin in human serum and urine specimens. This cost-effective carbon-based turn-off-on fluorescent sensor makes it easy to detect bilirubin, through visual inspection under ultraviolet light, thereby enabling prompt diagnosis. 2025 Elsevier B.V. -
An ESIPT/AIE active Schiff Base for the selective detection of Picric acid, Ammonia, and its potential applications in anticounterfeiting and latent fingerprinting
A novel ESIPT/AIE-active Schiff base fluorophore, N?1,N?6-bis((Z)-2,4-dihydroxybenzylidene)adipohydrazide (ADHB), has been designed and synthesized. ADHB exhibits remarkable selectivity and sensitivity towards picric acid in aqueous phase, as well as ammonia in both aqueous and solid phases, with LOD values of 55.5 nM and 88.7 nM respectively, facilitating its efficacy in real sample analysis. While exhibiting notable luminescence in polar solvents (? = 0.15 %), ADHB displays pronounced fluorescence enhancement in the solid state (??? = 320 nm) due to aggregation-induced emission (AIE). The molecular skeleton of ADHB incorporates two potential excited-state intramolecular proton transfer (ESIPT) active sites that exhibit distinctive, reversible halochromic properties in the solid state. The adaptability of this Schiff base as a multi-responsive fluorescent material was explored by the fabrication of a blue-emitting polyvinyl alcohol (PVA) composite film and paper-based test strips. The detection limits agree with the amount of contaminants that the U.S. Environmental Protection Agency (EPA) allows in drinking water. The sensing mechanism was elucidated through comprehensive DFT studies, NMR titration studies and Job's plot analysis. The tunable photophysical properties of this AIE-active probe facilitates practical applications in anti-counterfeiting and latent fingerprint visualization, highlighting its significance in forensic science and security authentication. These findings establish ADHB as a fluorescent platform for the sensitive detection and continuous monitoring of hazardous compounds in environmental systems. 2025 Elsevier B.V. -
In vitro storage under slow growth, plant regeneration, and ex vitro acclimatization of Ligusticum officinale (Makino) Kitag
Ligusticum officinale is an important medicinal plant belonging to Apiaceae. It does not set seeds and is propagated by rhizome division. However, its sensitivity to high summer temperatures makes field cultivation and genetic resource conservation challenging. To conserve L. officinale germplasm, we employed an in vitro slow-growth storage (SGS) method. Shoot cultures of L. officinale were established on Murashige and Skoog medium supplemented with 1.0 mg/L benzyl adenine, 30 g/L sucrose, and 2.4 g/L gelrite. Cultures were kept for one, three, five, and seven months. The effects of storage temperatures of 25 C (control) versus 15 C, medium supplementation with or without mannitol (3%), and abscisic acid (ABA), 0.5 mg/L, were examined. At the conclusion of the conservation period, survival was measured right away. Four weeks later, the shoot proliferation medium was used to measure the regrowth rate and recovery features. Subsequently, the regenerated shoots were transferred to MS medium supplemented with 1.0 mg/L indole-3-butyric acid for rooting of shoots for 4 weeks. The findings showed that even after seven months, shoot cultures kept at 15 C with medium supplemented with 3% mannitol and 0.5 mg/L ABA maintained a good survival rate of 83.3%. When compared to the control, most growth indices, including shoot length, fresh weight, number of shoots, and number of leaves, were significantly suppressed by mannitol and ABA combined treatment. A regrowth rate of 71% was achieved after transfer to proliferation medium. All the shoots that were cultured on rooting medium involved in rooting and plantlets were successfully acclimatized in controlled conditions. 2026 SAAB. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Optimizing resource management using hybrid metaheuristic algorithm for fog layer design in edge computing
The growing complexity of management in fog computing environments necessitates more efficient algorithms capable of optimizing resource allocation, minimizing latency, and maximizing throughput and energy efficiency. Existing techniques, consisting of the Multi-Objective Crow Search Algorithm (MOCSA) and Fuzzy Meta-Heuristics Optimization (FMHO), regularly suffer from suboptimal performance due to constrained exploration abilities and slower convergence fees. To overcome with these demanding situations, this paper proposes a singular Hybrid Metaheuristic Algorithm (HMA) that mixes the strengths of more than one metaheuristic techniques, along with genetic algorithms, simulated annealing, and gray wolf optimization (GA-SA-GWO). The HMA is specifically designed to enhance useful resource control in fog computing by optimizing useful resource allocation, lowering latency, and enhancing usual gadget performance. Experimental results exhibit that the proposed HMA significantly outperforms existing solutions, with 26.98 % improved latency, 90.64 % resource utilization, 96.05 % throughput, 37.06 % reduced energy utilization, and 93.85 % energy utilization. These outcomes spotlight the HMA's potential to successfully manage sources in dynamic and unpredictable fog computing environments, providing a greater scalable and robust solution for actual-time applications. 2025 -
Natural template-assisted green synthesis of cobalt oxide and its surface functionalization using ?-alanine for biological applications
The incorporation of nanotechnology into material science has brought great advancements in diverse fields like medicine, electronics, energy, and the environment. Metal oxides gained notable attention from various nanomaterials due to their unique structure and properties. Cobalt oxide nanoparticles (Co3O4) stand out especially due to their diverse properties and applications. Synthesis of metal oxides through the traditional method faces many drawbacks, such as the use of toxic chemicals, a complex procedure, and environmental and health impacts. Whereas the green method of synthesis using natural resources, followed by surface modification with amino acids, acts as a better option for metal oxide synthesis. This paper focuses on developing a green, sustainable, and scalable method for synthesising Co3O4 nanoparticles, using a natural template, gum Arabic, followed by surface functionalization of ?-alanine. Various physico-chemical characterisation techniques such as DLS, TEM, FTIR and XRD were used to study nanoparticle composition and properties. Biocompatibility studies, cell viability assays and fibroblast cell lines from human skin by Alamar Blue assay, were carried out to test the effects of synthesised nanoparticles, and optimised protocols were also used to enhance performance for particular biomedical applications. Incorporating green synthesis and advanced techniques, ?-alanine functionalized Co3O4 nanoparticles, this research points toward developing more stable, biocompatible, and reactive nanoparticles under biological conditions. and multifunctional Co3O4 nanomaterials. Overall, the current study aims at sustainability with innovation towards transformative various biological applications in healthcare, biomedicine, diagnostics, MRI, biosensors, photo-sensing agents and energy technologies while addressing significant gaps in present methodologies. 2025 Elsevier B.V. -
Smart and sustainable PVA/cellulose-ZnO/anthocyanin Films: Dual-action packaging for extended shelf life and real-time freshness detection
Sustainable sensing films for intelligent packaging applications are the need of the hour. Herein, for the first time, cellulose nanofibers (CNFs) from pineapple pomace are used as the host polymer for preparing cellulose-ZnO (CZnO) powder. The structural and dimensional properties of the CZnO powder were confirmed using different characterization techniques. Red cabbage-derived anthocyanin and CZnO were infused with polyvinyl alcohol (PVA) to make the antibacterial sensing films (CZnO/PVA (CPVA) and CZnO/anthocyanin/PVA (CAPVA)). The effective bonding between the PVA and the fillers is studied using the AT-FTIR analysis. The incorporation of C-ZnO and CZnO/anthocyanin resulted in the improving the UV screening ability of the developed films. The tensile strengths of the CPVA and CAPVA films were 14 and 11 % greater than that of the neat PVA film. The interaction with water was significantly reduced when CZnO was added to PVA making the material more hydrophobic. This was further confirmed using wettability studies. In 100 days, in wet conditions, the PVA, CPVA, and CAPVA showed biodegradability of 17, 37, and 39 %, respectively. As a packaging material, CPVA and CAPVA films increased the shelf life of fresh shrimp by up to 4 days as they have significant water barrier properties and antibacterial activity with an inhibitory zone of 28 and 20 mm (for CAPVA) against S. aureus and E. coli respectively. Furthermore, the release of volatile amines from shrimp spoilage caused the color of the CAPVA films to change over time from pink to yellow, indicating the possibility of using these films for real-time freshness monitoring. 2025 Elsevier B.V. -
Hesitant bipolar fuzzy set-based decision system for electric vehicle charging station location planning
The selection of electric vehicle (EV) charging station locations is a critical challenge that significantly affects the growth and acceptance of the EV industry. As EVs offer a sustainable solution to fossil fuel depletion and environmental pollution, identifying optimal charging station sites involves dealing with uncertain, inconsistent, and conflicting criteria. To address these challenges, this paper presents an innovative decision-making framework based on Hesitant Bipolar-Valued Fuzzy Sets (HBVFSs), which account for both positive and negative hesitant membership values to better model uncertainty in expert judgments. A novel hybrid Multi-Criteria Decision-Making (MCDM) technique is proposed, combining the Step-wise Weight Assessment Ratio Analysis (SWARA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods to determine robust criteria weights within the HBVFS environment. The Preference Ranking Organization METHod for Enrichment Evaluation II (PROMETHEE-II) is employed for the final site ranking. This integrated approach enables a more comprehensive and reliable evaluation of potential locations by incorporating both qualitative and quantitative factors. The proposed methodology has practical applications in real-world infrastructure planning and supports more resilient decision-making in sustainable transportation networks. The results demonstrate the model's effectiveness and adaptability in addressing the site selection problem under uncertainty. 2025 Elsevier Ltd