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Surfactant-Guided Synthesis of Mn?O? Nanostructures for Photocatalytic, Photoelectrochemical Applications and Sustainable Water Reuse
Currently, the global community is confronting water contamination from several sources, which poses significant environmental challenges. The reutilisation of polluted water constitutes a feasible strategy for sustainable wastewater management. In the present study, Mn3O4 was synthesised via chemical precipitation using polyvinyl alcohol (PVA), cetyltrimethylammonium bromide (CTAB) and glycine as surfactants and without any surfactants. The synthesised Mn3O4 using Glycine(MNG) showed the best photocatalytic efficacy compared to other synthesised materials, about 93 0.35% disintegration of Rhodamine B in 150min when illuminated with visible light. The progress of photodegradation was in conformity with the pseudo-first-order kinetic model with a velocity of 0.017min?1. Photoelectrochemical investigations assessed charge transfer, stability, and light-harvesting behaviour of Mn3O4 catalysts, showing their improved performance beyond photocatalysis. Seed germination in the treated water, controlled and uncontrolled conditions, was compared to ensure the agricultural and environmental viability. Additionally, ~ 84.54% of total organic carbon removal was achieved. A conceivable degradation mechanism was suggested after the degraded intermediates were examined using high-performance liquid chromatography (HPLC) and the elution patterns and retention periods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Eco-friendly AgZnO Nanocomposites Synthesis and Their Role as Photocatalyst for Textile Dye Degradation
Recent research in the field of nanotechnology revealed that plant extract and their derivatives are good stabilizing and reducing agents. Artemisia stelleriana (Dusty Miller) is widely used as an ornamental plant. The current study, explores one-pot method to synthesise A. stelleriana-mediated silver/ zinc oxide nanocomposites (AS-Ag/ZnONCs). Using UV-visible spectrophotometer, scanning electron microscopy, energy-dispersive X-ray, Transmission Electron Microscope, X-ray diffraction, and Fourier transform infrared spectroscopy the characterisation of the synthesised AS-Ag/ZnONCs was examined. The crystalline size of the AS-Ag/ZnONCs was determined to be 45.39nm using the Williamson-hall equation. Irregular-shaped nanocomposites were observed from AS-Ag/ZnONCs, exhibiting an average size of 35.2nm. To check the activity of AS-Ag/ZnONCs as photocatalysts to degrade RY145, RY86, RB222A and RB220 dyes was determined. The order of photocatalytic activity of AS-Ag/ZnONCs was as follows: RY145 > RB220 > RB222A > RY86. Low toxicity was observed when Vigna radiata (Mung bean) and Artemia salina (Brine shrimp) were exposed to treated dye solutions using AS- Ag/ZnONCs when compared with untreated dye solutions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Tribo-catalytic Dye Degradation Driven by Mechanical Friction Using ZnS Microparticles with Different Morphologies
This work examines the tribocatalytic properties of zinc sulphide (ZnS) microparticles for dye degradation using mechanical energy. ZnS microparticles were synthesized into four distinct morphologies: microrods, spherical aggregates, microflakes, and microflowers using the solvothermal method. These morphologies were characterized using XRD, FESEM, EDS analysis, UVVis spectroscopy, XPS, and BET analysis. The tribocatalytic activity was assessed by degrading methylene blue (MB) dye under magnetic stirring in a dark setting. The experiment was carried out at the neutral pH of MB solution (~ 6.5). Among the prepared ZnS morphologies, the micro flakes displayed the largest surface area (120m/g) and exhibited enhanced dye degradation efficacy, achieving 57% MB elimination after 15h of agitation at 800rpm, corresponding to a pseudo-first-order rate constant of 0.054min?. By analyzing the degradation kinetics as pseudo- first-order kinetics, we elucidated the crucial significance of surface morphology and contact area in facilitating effective electron transfer during tribocatalysis. Additionally, we investigated the influence of PTFE bar size, material concentration, stirring speed and initial dye concentration on degradation efficiency. Reusability test demonstrated stable performance over four consecutive cycles with a minor decrease (~ 5%). The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
HEDP-WBAN: Homomorphic Encryption and Differential Privacy for Secure Edge-Based WBANs
In the current era, healthcare is precious for all, so recent technology helps monitor the patients health via sensors and nodes, collect the same data, and analyze the patients condition. This is a technology known as a wireless body area network (WBAN). Nano-sensor devices are attached to the human body as part of WBAN to collect and monitor patient data, but have limited processing power and battery life. So, it cannot perform heavy computations within the node. Edge computing addresses this issue by processing data and reducing response times (in a near-edge node), which also helps mitigate delays and offloads work from WBAN nodes, but creates a privacy risk. Sensitive patient health information can be exposed through cyberattacks, unauthorized access, or profiling from edge nodes. This research proposes that PrivEdge-WBAN (Privacy-Preserving Edge Computing for WBANs) is integrated with edge computing, creates a framework for authentication and secure data processing, and supports new privacy-preserving and energy-efficient techniques. The proposed model combines lightweight Homomorphic Encryption (HE) and Differential Privacy (DP) to enable privacy-preserving computations and security at the edge node while maintaining energy efficiency. Moreover, the proposed framework combines an adaptive security engine that dynamically regulates cryptographic processes and authentication complexity derived from real-time energy levels and device workload. PrivEdge-WBAN aims to provide a comprehensive solution for security, privacy, and battery conservation in the real-world applications of WBAN. The outcome of this research can significantly influence the design of sustainable and secure surveillance systems for the healthcare sector, particularly for chronic illnesses, aging care, and other emergencies. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments
High-Impact Weather (HIW) events, particularly high-impact heavy rainfall, pose significant risks to urban infrastructure in Australia. Traditional forecasting approaches often struggle to resolve the complex, non-linear thermodynamic interactions that drive these infrequent events, while standard supervised machine learning models are hindered by severe class imbalance. This study presents a novel, multi-disciplinary framework that integrates synoptic climatology with unsupervised anomaly detection to classify and predict high-impact heavy rainfall events in Darwin, Sydney, Brisbane, and Perth. Using daily meteorological observations (20242025), we developed a multi-phase analytical framework comprising precursor, thermodynamic, kinematic, and system evolution phases to isolate the physical signatures of storm genesis. Exploratory analysis using Danger Rose polar histograms revealed a strong anisotropic risk pattern, with heavy rainfall predominantly associated with South-South-East (SSE) and West-South-West (WSW) vectors. Bivariate Kernel Density Estimation (KDE) revealed a distinct Thermodynamic Lock-in mechanism, where severe events are confined to narrow regimes of low pressure (< 1010 hPa), high humidity (> 60%), and compressed diurnal temperature ranges. To address the limited representation of severe events data (12.1%), we benchmarked five unsupervised anomaly detection algorithms. The results indicate that DBSCAN (Density-Based Spatial Clustering) yields the optimal performance (F1-Score: 0.319; Recall: 67.5%), significantly outperforming Isolation Forest and PCA. Topological validation via t-SNE projection confirms that high-impact heavy rainfall events form dense, cohesive clusters within the phase space rather than appearing as randomly distributed stochastic outliers. These findings prove that hybridizing physical phase-space analysis with density-based machine learning offers a robust pathway for early warning systems in data-sparse environments. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework
The Pichavaram mangrove forest in Tamil Nadu is one of Indias most ecologically significant regions, supporting coastal health and local communities. However, effective mangrove area classification remains challenging due to field inaccessibility and inefficiency of traditional assessment methods, highlighting the demand for advanced solutions. As the existing remote sensing-based studies suffer from limited classification accuracy and high computational complexity, this study combined Hyperspectral Image (HSI) with an Optimized Channel Level Residual CNN (OC-LRCNN) model for improved results in mangrove-related research. The proposed model employs unsupervised feature extraction to capture essential patterns with minimal training data while channel-level residual connections enhance discriminative feature selection and reduce spectral redundancy. Utilizing the Pichavaram EO-1 Hyperion and AVIRIS-NG datasets, the proposed model is compared with traditional CNN, state-of-the-art deep learning architectures (VGG, ResNet, DenseNet) and machine learning methods like SVM and RF. The OC-LRCNN achieved classification accuracies of 98.2% and 99.0% for the Hyperion and AVIRIS-NG datasets with consistently high precision, recall, F1-score and kappa values. These findings demonstrate the models effectiveness in reliable mangrove classification and monitoring applications. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
L-arginine functionalized NiFe?O? nanoparticles: synthesis, characterization, antimicrobial activity, and biocompatibility evaluation in a zebrafish model
NiFe?O? nanoparticles and L-argininefunctionalized NiFe?O? (NiFe?O4LA) were synthesized, characterized, and evaluated for antimicrobial performance and in vivo biocompatibility in a zebrafish embryo model. The XRD and HRTEM studies confirmed the formation of single-phase cubic spinel NiFe?O? with average crystallite size ? 2030nm. Surface modification with LA preserved the spinel structure while reducing crystallinity and hydrodynamic size 128.7nm. FTIR and XPS verified successful surface functionalization. UV vis measurements showed band-gap narrowing after La conjugation (Eg ? 3.23 ? 3.15eV). NiFe?O?LA exhibited enhanced antimicrobial activity compared with bare NiFe?O? against MRSA and C. albicans an effect attributed to enhanced surface interaction and ROS-mediated oxidative damage. The MIC values of C. albicans is found to 1200g/mL while in the case MRSA is about 900g/mL. The ROS assays with histidine modulation supported a role for redox activity in antimicrobial action. In vivo zebrafish embryo assays showed minimal developmental toxicity at tested exposures with better viability for the LA coated material. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
A novel mobile sink placement in wireless sensor network using deep maxout network based energy prediction with adjacent cell score
The majority of Wireless Sensor Networks (WSNs) are made up of energy- and cost-efficient detecting nodes. Traditional wireless sensor networks encounter serious problems, including latency, network failure, and congestion, since they rely on individual base stations (BSs) to gather data from the whole network. Sensor nodes adjacent to the base station will use more energy because of excessive energy consumption and energy-hole constraints, affecting the network's life. Understanding the best place for mobile sink nodes can help alleviate this issue by lowering energy usage and extending the network's lifespan. In this paper, utilizing a deep learning-based energy prediction and neighbour cell score model, we build and construct an efficient method to locate mobile receivers using distance, expected energy, and fairness variables. Furthermore, a Deep Maximum Output Network (DMN) calculates the desired power. However, the minimum length, maximum residual energy, complete normalized right, maximum network lifespan, and maximum normalized throughput for our suggested neighbor-based cell scoring with Deep Maxout Network are 137.364, 30.903, 64.426, and 60.613, respectively. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A Hybrid Intrusion Detection System for detecting Cross-layer DoS attacks in IoT
The Internet of Things (IoT) is critically prone to Denial of Service (DoS) attacks at multiple layers. If designed carefully, intrusion detection systems (IDS) can detect these attacks effectively. In the proposed study, we develop a Hybrid IDS to detect Cross-Layer DoS attacks in IoT. The proposed Cross-Layer system reduces the false positive rate considerably than a single IDS. The IDS is designed by ensembling multiple machine learning techniques to avoid overfitting or underfitting. The Hybrid IDS works in two stages, the first stage for detection of the attack occurrence (Anomaly detection) followed by a second stage to classify the attack types (Signature of the attacks). The output of the first stage is Correctly Detected Samples (CDS), which are again tested by the second stage to get Correctly Classified Samples (CCS). Another unique aspect of the proposed study is the dataset generation for different attacks considered. Rather than using the existing dataset, we have developed a trace file in NetSim Simulator by designing an attack environment. At the same time, during the feature selection process, a novel and efficient technique is applied to select the best feature set along with the critical component (CF). Simulation results accurately detect CDS of up to 95% and CCS of up to 96% with a weighted average F1 score. The testing time of the proposed model is also considerably lower than that of individual models, which makes the system efficient and lightweight. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Employee relations: a comprehensive theory based literature review and future research agenda
This study aims to conduct a systematic and integrative literature review to consolidate the extensive information on employee relations accumulated over the past century, thereby offering new insights into domain-specific phenomena. The research followed a four-phase search strategy in accordance with the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The keyword search utilized terms such as 'employee relations,' 'employee relation,' 'employment relation,' and 'employment relations' in the Scopus and Web of Science databases. By employing an integrative approach along with specific inclusionexclusion criteria, the researchers synthesized articles from leading journals in the field of employee relations, categorizing them based on geographical region, article types, prominent authors and their affiliations, and the most cited research articles. In the final stage, the researchers presented new insights through a conceptual framework utilizing the ADO-TCCM approach, which encompasses antecedents, outcomes, theories, context, methodology, mediators, and moderators of employee relations. This study synthesizes findings and reorganizes key themes into innovative frameworks, providing fresh perspectives on various aspects of employee relations. Ultimately, it offers valuable insights into the critical factors that strengthen long-term employee-employer relationships. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. -
Application of Fuzzy-NSGA-II for achieving maximum biodiesel yield from waste cooking oil
The increasing demand for renewable energy and efficient waste management has highlighted the need for innovative biodiesel production techniques. This study optimises biodiesel production from waste cooking oil (WCO) using fuzzy modelling and non-dominated sorting genetic algorithm-II (NSGA-II). The optimisation process focuses on key input parameters: methanol quantity, reaction temperature, reaction time, and catalyst concentration, which were normalised and represented using linguistic variables. Fuzzy logic was employed to predict biodiesel yield, expressed in terms of linguistic variables, and defuzzified to yield crisp output values. The developed model achieved a high R2 value of 96.34%, demonstrating a strong correlation between input variables and biodiesel yield. The NSGA-II algorithm was utilised for multi-objective optimisation, determining the optimal conditions for biodiesel production: 150ml of methanol, a reaction temperature of 62C, a reaction time of 63min, and a catalyst concentration of 7.5g. These parameters resulted in a maximum biodiesel yield of 97.36%. The Box-Behnken experimental design validated the models efficiency, achieving a yield of 96.88%. This study emphasises the practical implications of optimised biodiesel production, such as reducing environmental pollution by recycling WCO and minimising reliance on fossil fuels. The optimised process meets ASTM standards and exhibits scalability potential for industrial-level production with minor modifications. The models robustness makes it suitable for integration into intelligent manufacturing systems, ensuring consistent biodiesel quality and yield through automated monitoring and control mechanisms. Despite its success, challenges such as feedstock variability and initial setup costs must be addressed. Future studies should focus on adaptive models and energy-efficient processing technologies to enhance scalability and sustainability. This research demonstrates a significant step towards sustainable biofuel production, combining waste management with renewable energy generation. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Unveiling the Quassia indica derived synthesis of Co3O4/ZnO nanohybrids for efficient dye degradation and cytotoxicity assessment
While there are exciting possibilities in nanotechnology, creating environmentally safe nanoparticles with a variety of uses in photocatalysis and biomedicine continues to be a significant issue. This work addresses the gap by introducing Quassia indica leaf extract as a bio reductant and stabilizer in the green synthesis of cobalt oxide-zinc oxide nanoparticles (QI: Co3O4/ZnO NP). The synthesized nanoparticles were characterized using various techniques, including UVvisible spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS), high resolution transmission electron microscopy (HR-TEM), selected area electron diffraction (SAED), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray spectroscopy (EDX). The existence of hexagonal zinc oxide and cubic cobalt oxide phases in the synthesized nanoparticles was verified by XRD analysis. The elemental composition was confirmed by EDX, which showed that oxygen, zinc, and cobalt were present. The average hydrodynamic diameter of 244.5 d. nm was found via DLS measurements, indicating well dispersed nanoparticles. Under UV light irradiation, photocatalytic activity of QI: Co3O4/ZnO NP was assessed for the degradation of textile dyes (Reactive Blue-222, Reactive Blue-220, Reactive Red-120, and Reactive Yellow-145). Phytotoxicity tests were conducted to examine the possible environmental impact of the deteriorated dye solution, revealing promising results in mitigating the detrimental impact of industrial dyes. QI: Co3O4/ZnO NP was also assessed for cytotoxicity studies in DLA and EAC cells which showed a concentration-dependent cytotoxic effect. The research outcomes emphasize the significant potential of these nanoparticles in diverse arena by offering a sustainable and efficacious resolution to the present-day problems. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Valorization of pomegranate peel waste as a sustainable feed additive: enhancing growth, digestion, immunity, and disease resistance in the freshwater prawn Macrobrachium rosenbergii
The growing demand for sustainable aquaculture and eco-friendly feed resources necessitates the development of circular economy strategies that valorize agro-industrial by-products. This study evaluated pomegranate (Punica granatum) peel, a nutrient-rich fruit waste, as a functional and sustainable feed additive in the freshwater prawn Macrobrachium rosenbergii. A 60-day feeding trial included a control (0X) and five experimental diets (P1P5) supplemented with P. granatum peel protease (0.02X0.10X). Growth, digestive enzyme activity, immune-oxidative responses, and tissue histology were assessed, followed by a 72-h post-challenge with Aeromonas hydrophila. Growth improved significantly in P4 and P5 (p < 0.05), with the highest specific growth rate (2.14 0.03%/day in P4; 2.13 0.01%/day in P5) and lowest feed conversion ratio (0.39 0.01) in P5. P4 showed the highest protein (240.01 2.68mg/g) and amino acids (148.16 0.83mg/g). Protease activity peaked in P4 (1.55 0.03IU/g), while amylase and lipase remained unchanged. Antioxidant defenses in P5 were elevated, including superoxide dismutase (43.31 0.33%), catalase (1.91 0.05 U/min/mg protein), glutathione S-transferase (1.43 0.01 U/min/mg protein), glutathione peroxidase (5.97 0.02 U/min/mg protein), and total hemocyte count (22.80 0.05 10? cells/mL). Histology confirmed improved hepatopancreas structure. P. granatum exhibited in vitro antibacterial activity against A. hydrophila (MIC 6.25mg/mL), and in vivo challenge showed the lowest mortality in P4 and P5 (16.25 1.77%) versus control (71.25 1.77%). These results highlight P. granatum peel as a viable circular bioresource, promoting nutrient recycling, waste reduction, and sustainable aquaculture productivity while minimizing reliance on synthetic additives. Future studies should focus on long-term feeding trials and large-scale farm evaluations to further validate the commercial viability of P. granatum peel as a sustainable functional feed additive in aquaculture. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Redefining copreneurs: a four decadal review adopting computer aided text analysis
The study defines copreneurs and presents a four decadal review on copreneurial literature. The purpose is to bring conceptualization and characterization of copreneurs, on surface from its fragmented literature. A structured literature review on copreneurship research published between 1984 and 2023 is conducted. The search is made adopting indexing (Scopus, Clarivate and ABDC), digital libraries including ProQuest and EBSCO, and research articles published in journals by renowned publishers namely Elsevier, Emerald, Inderscience, Sage, Springer, Taylor & Francis and Wiley. Inclusion/exclusion criteria was defined and duplicates were eliminated. Finally, using POWER review model, the existing literature is organized under six themes namely Gender Roles, Spousal Support & Relationship Satisfaction, Work Life Balance, Business Commitment & Motivation, Leadership & Decision Making and Division of Labour & Responsibilities in the Intertwined Worlds. Using Inter- Rater Reliability, five definitions of copreneurs were framed and rated by nine experts from academics and industry. Finally, the definition with highest score and acceptable I-CVI value for simplicity & clarity is proposed. The fragmented literature on copreneurs speaks volume about the need for more impactful research on them. By using the proposed definition of copreneurs, scholars can uniformly identify the copreneurs, with future opportunities for micro-level research on copreneurs. Policy makers can utilise the findings of these research and formulate schemes, policies & programmes for betterment of copreneurs. The study intends to bridge the disciplinary gaps existing for identifying copreneurs and serve as a foundation for information sharing, regarding copreneurs and their entrepreneurial practices. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Ag Ions Versus Ag Nanoparticle-Embedded Glass for Antimicrobial Activity Under Light
Incorporating silver nanoparticles (NPs) into a host material has been recognized to limit the release of Ag+ ions, yet their efficacy in neutralizing nearby microorganisms remains uncertain. This study aims to compare the toxicity of Ag+ ions versus the plasmonic effect of Ag NPs within a glass matrix, assessing their respective killing efficiency and mechanisms against microorganisms. To achieve this objective, a simple ion exchange technique was employed to embed glass with silver ions, nanoclusters (NCs), or NPs, which was confirmed by UVVis-NIR spectrometer, photoluminescence (PL), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). The biocidal action of these Ag species on model Escherichia coli (E. coli) bacteria was investigated in the absence and presence of visible light. The findings revealed that in the absence of light, plasmonic Ag NPs were less toxic to E. coli compared to Ag+ ions due to the predominant release of Ag+ ions dictating the antibacterial effect. However, exposure to visible light triggered the plasmonic effect in Ag NPs to disintegrate 100% E. coli in 1h compared to Ag+ ions (68%) owing to the localized heating around the Ag NPs, facilitated by surface plasmon resonance relaxation. The cell morphology investigated by Bio-AFM assisted in unraveling the mechanism leading to bacterial cell damage. Overall, this study demonstrates the sustained disinfection capability of Ag NPs embedded in glass without significant leaching, emphasizing their potential in prolonged antimicrobial applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
All-Optical Plasmonic Neurosensor for Self-Learning Anomaly Detection in Smart IoT Systems
An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817nm with a field enhancement of approximately 28x with gap dimensions of 10nm long. Fabricated devices attained resonance of 823nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 10? and overall shift of the resonance at 51nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Analysis of magnetohydrodynamic casson fluid flow with chemical reaction in a vertical channel: thermal and mass transfer effects
This study investigates Casson fluid flow in a vertical channel within a magnetohydrodynamic (MHD) region, incorporating chemical reaction effects. The channel consists of two regions: one filled with an electrically conducting fluid and the other with a Casson fluid. The nonlinear coupled governing equations are solved using the perturbation method with a small perturbation parameter. The results are presented graphically to analyze the flow characteristics. This systematic analysis yields the velocity and temperature distributions, governed by key parameters such as the Grashof number (Gr), Hartmann number (M), Casson parameter (?), and chemical reaction rate, all of which critically influence the hydrodynamic and thermal behavior of the system. It is observed that the larger the values of the viscosity ratio, width ratio, and the conductivity ratio, the larger the flow field. The findings reveal that the presence of Casson fluid enhances the thermal and mass Grashof numbers, attributed to buoyancy forces. Conversely, the chemical reaction parameter and Hartmann number exhibit a suppressive effect on the flow. The Author(s) under exclusive license to Universitdegli Studi di Ferrara 2026. -
High performance symmetric supercapacitor based on microporous PANI@?-Fe2O3/MXene hybrid nanocomposite
MXene (Ti3C2Tx), a 2D layered material, has become a trending topic in the field of energy storage, due to its high-power density, flexibility, hydrophilic nature, and ease at which it can form composites with polymers, CNTs, metal oxides, and more. However, the layers of MXene restacks quite easily restricting the number of active sites to the flow of charges. Herein, we have synthesized tri-composite of Ti3C2Tx, ?-Fe2O3, and polyaniline (PANI@?-Fe2O3/Ti3C2TX) via hydrothermal treatment followed by oxidative polymerization. The insertion of ?-Fe2O3 broadens the interlayer distance allowing easy charge/discharge of ions, further addition of PANI enhanced MXenes energy density without altering its power density making MXene more reliable material for energy storage application. The composite exhibits a very high and notable electrochemical performance with 2689.3 Fg?1 specific capacitance at a current density of 1 Ag?1 compared to that of bare MXene (402 Fg?1 at 1 Ag?1). The retention capacitance of 120.7% over 5000 cycles is reported making MXene a promising material in reducing the volume expansion of PANI. A symmetric supercapacitor was fabricated exhibiting a very high energy density of 75.60 Wh kg?1. This work intends to increase MXenes performance favoring hybrid energy storage systems. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Green Energy Harvesting using a Flexible Bio-triboelectric Nanogenerator
Bio-triboelectric nanogenerators (B-TENGs) show promise as a sustainable and renewable source for harvesting green energy using natural biocompatible and biodegradable substrates. Dry leaves contribute to a large amount of waste accumulation daily, even though they can be used to generate energy. Almost all the dry leaves collected during cleaning procedures are burned, resulting in greenhouse emissions and air pollution. This work aims to consider using biodegradable fresh and dry leaves as a bio-source for a cost-effective, sustainable, and flexible energy-harvesting system. When different frequencies and pressures were applied to B-TENGs, significant potential power output of around ~30V and ~350?W was produced. The experimental results and density functional theory (DFT) calculations support the charge transport phenomenon in dry-leaf powder under different compressive strains. A surface influences charge generation in B-TENGs and the presence of functional groups with inhomogeneous particle distribution, as demonstrated by experimental and mathematical modeling. The current work is ideal for large-scale manufacturing since it uses natural waste materials in dried forms, as well as simple and low-cost preparation. Therefore, our environmentally friendly solutions highlight the special abilities of plants to produce electricity for various flexible electronic applications. The Minerals, Metals & Materials Society 2025.