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Two-dimensional chromium telluride-coated 3D-printed architectures for energy harvesting
Rapid development of industries, urbanization, and technological advancements have increased demand for sustainable and cost-effective alternative energy sources. In this work, a self-powered flexible 3D-printed triboelectric nanogenerator coated with 2D chromium telluride (Cr2Te3) (3D-TENG) is presented as an innovative energy harvesting approach from pressure and temperature. The optimized flexible 3D-printed hexagonal structures with coatings show varying specific yield strength and porosity. The 3D-TENGs achieved a maximum output voltage of ?39 V under periodic impacts of ~0.8 kPa and their performance further increased (?45 V) in the presence of varied temperatures. The outstanding results and flexibility of the 3D-TENG devices highlight their potential in self-powered energy harvesting from external heat, magnetic fields, and body weight. Density functional theory (DFT) calculations further explained the interaction between 2D Cr2Te3 and the polymer surface under external impact. Therefore, we believe that our findings illustrate the potential of integrating 2D materials with 3D-printed architectures to enhance the efficiency and adaptability of flexible, lightweight, low-cost, and eco-friendly TENG devices for industrial applications. 2025 The Royal Society of Chemistry. -
Unlocking efficiency: experimental and theoretical insights into biomass-derived carbon nanofluids with enhanced thermal conductivity
The study presents an experimental investigation, supported by theoretical analysis, into the effects of nanoparticle (NPs) concentration, particle size, and shape on the thermal conductivity (TC) of carbon nanosphere (CNS)-based nanofluids (NF). CNS was synthesized from garlic peels (Allium sativum) via pyrolysis at varying temperatures and characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and other techniques. The NFs were prepared using a two-step method with different CNS concentrations in propylene glycol (PG) and deionized water (DI)/PG mixtures. Particle size distribution and colloidal stability were evaluated using dynamic light scattering (DLS) and zeta potential analysis. The TC of the NFs was measured across various temperatures, revealing a significant dependency on both particle size and concentration. All NFs exhibited enhanced thermal conductivity to the base fluid (BF), with increases of 52.60%, 101.28%, 108.51%, 114.60%, and 122.64% at 80 C for CNS synthesized at 500 C (AS500), 600 C (AS600), 700 C (AS700), 800 C (AS800), and 900 C (AS900), respectively. Rheological analysis showed a linear increase in dynamic viscosity (V) with rising CNS concentration within the dilute limits (0.01 to 0.1 wt%) and a strong correlation between particle size and thermal conductivity enhancement. These findings emphasize the critical role of CNS particle size in optimizing thermal performance, with potential applications in heat transfer systems. The study culminates with an exercise aimed towards presenting thermal conductivity and dynamic viscosity as surface plots. These plots provide behavioral trends for understanding the dependence of TC and V on nanoparticle size and temperature. 2025 The Royal Society of Chemistry. -
Unzipped MWCNT/polypyrrole hybrid composites: a pathway to high-performance asymmetric supercapacitors
A novel method has been developed for the conversion of multi-walled carbon nanotubes (MWCNTs) into unzipped MWCNTs (UzMWCNT) using a modified Hummer's method followed by reduction. This technique allows for the controlled modification of MWCNTs in both transverse and longitudinal directions. The UzMWCNT exhibits unique structural characteristics that combine the properties of 1D nanotubes and graphene-like features. The UzMWCNT/PPy composite exhibited an impressive specific capacitance of 944 F g?1 along with excellent cycling stability, retaining 92% of its capacitance after 5000 cycles. For the UzMWCNT/PPy//AC composite, the gravimetric capacitance decreased with increasing current density, from 400 F g?1 at 1.0 A g?1 to 162 F g?1 at 2.5 A g?1. Furthermore, the UzMWCNT/PPy//AC composite demonstrated outstanding long-term durability, retaining approximately 95% of its capacitance after 5000 cycles at a current density of 5 A g?1, underscoring its excellent cycling stability. This research paves the way for the development of high-performance supercapacitor electrodes using hybrid materials derived from MWCNTs. 2025 RSC. -
Enhanced transport, dielectric and magnetic properties of Ni-doped (YFeO3)0.5(BaTiO3)0.5 perovskite for NTC thermistor and multifunctional applications
The solid-state reaction method was successfully employed to synthesize the environmentally friendly polycrystalline perovskite (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5. X-ray diffraction (XRD) analysis, complemented by Rietveld refinement, confirms its multiphase crystalline structure, comprising two cubic and one orthorhombic phase. Field-emission scanning electron microscopy (FE-SEM) reveals a well-defined surface morphology, while energy-dispersive spectroscopy (EDS) and elemental mapping validate the homogeneous distribution of constituent elements. Raman and FTIR spectroscopy further confirm the vibrational and atomic structural integrity of the material. Dielectric studies indicate a high dielectric constant (?338 at 100 Hz, room temperature), with strong frequency and temperature-dependent relaxation effects. Impedance spectroscopy reveals non-Debye relaxation behaviour, NTCR characteristics and impedance in the megaohm range at lower temperatures. AC conductivity results align well with Jonscher's power law. The thermistor coefficient (?) reaches 4778.61 at 450 C, demonstrating excellent potential for thermistor applications. Magnetic studies confirm a prominent ferromagnetic response at room temperature, with a saturation magnetization of 3.654 emu g?1 and coercive field of 196.4 Oe. These combined properties make (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5 a promising candidate for multifunctional applications. 2025 RSC. -
Copper-boosted thiol-functionalized carbon nanospheres from biomass: a novel non-noble metal based recoverable catalyst for efficient nitro-to-amine reduction
In this work, the synthesis and catalytic activity of thiol-functionalized copper-deposited porous carbon derived from dry oil palm leaves (Cu/TF-CNS) was investigated for the reduction of aromatic nitro compounds. The procedure to synthesize porous carbon nanospheres involves the pyrolysis of oil palm leaves in a nitrogen atmosphere at 1000 C. The resulting porous carbon material was further functionalized with thiol groups to facilitate the uniform deposition of copper nanoparticles and serve as an efficient support. Excellent catalytic performance was shown by the Cu/TF-CNS catalyst in reducing aromatic nitro compounds to their corresponding aromatic amines with a low copper loading of only 4 mol% which is an inexpensive non-noble metal in the presence of NaBH4 as a reducing agent and EtOH/H2O as green solvents. The products were identified using 1H NMR spectroscopy. The catalyst was isolated from the reaction mixture and reused upto 10 cycles without any significant loss in the activity. The ICPAES analysis confirmed the successful incorporation of approximately 8.9% Cu during the deposition process and the reusability of the catalyst underscores its efficacy as a sustainable and effective heterogeneous catalyst for nitroarene reduction. 2025 The Royal Society of Chemistry. -
Fluorogenic selective detection of Zn2+ using a pyrazole-ortho-vanillin conjugate: insights from DFT, molecular docking, bioimaging and anticancer applications
A fluorescent sensor, (E)-N?-(2-hydroxy-3-methoxybenzylidene)-3,5-dimethyl-1H-pyrazole-1-carbohydrazide (HMPC), was designed and synthesized for the selective fluorescence recognition of Zn2+ in semi-aqueous media. Notably, HMPC exhibited a red-shifted, two-fold fluorescence turn-on enhancement in response to Zn2+ at 490 nm, with a detection limit of 1.68 ?M, which is significantly lower than the WHO guideline (76.0 ?M). The binding constant of HMPC with Zn2+ was calculated to be 5 104 M?1. The fluorescence enhancement of HMPC in the presence of Zn2+ is attributed to the suppression of the PET process and the enhancement of ICT, leading to fluorescence via the CHEF mechanism. The sensing mechanism was demonstrated through UV-vis, fluorescence spectroscopy, Job plots, ESI-MS, and DFT calculations. For biological applications, cytotoxicity and cell imaging studies were performed using MCF-7 cells. Molecular docking studies revealed a high binding energy of HMPC (?G = ?7.1 kcal mol?1) with the 4,5-diaryl isoxazole HSP90 chaperone protein, suggesting its potential as an anticancer agent. Additionally, its binding energy of ?6.5 kcal mol?1 with the HDAC8 protein indicates greater efficacy than suberoylanilide hydroxamic acid (SAHA) in inhibiting HDAC, as it binds more strongly to the HDAC8 protein than SAHA (?7.4 kcal mol?1). Furthermore, due to its favorable ADME profile, HMPC may be suitable for oral administration, enhancing its potential as an anticancer drug. 2025 The Royal Society of Chemistry. -
Kibble-Zurek scaling and spatial statistics in quenched binary Bose superfluids
The emergence of order from an initially uncorrelated state across a phase transition is a central problem in quantum many-body physics, particularly in multicomponent systems where interactions between components lead to rich nonequilibrium dynamics. While defect formation is known to follow universal scaling laws, prior studies have focused mainly on defect density, leaving their spatial organization largely unexplored. Here we show that gradually tuning the chemical potential in a two-dimensional binary Bose gas drives condensation into either a miscible or immiscible phase. In the immiscible regime, domains form whose number, size, and boundary length obey Kibble-Zurek (KZ) scaling and evolve self-similarly. In the miscible regime, vortices emerge with KZ scaling. In both cases, the spatial distribution of vortices and domains is well described by a Poisson point process with KZ-determined density. These results reveal universal features of far-from-equilibrium dynamics and provide a framework to characterize stochastic geometry in multicomponent quantum systems. The Author(s) 2026. -
Quad-band SIW antenna with micro-pocket enabled frequency-agile design for 5G/6G IoT applications
A single polarized substrate integrated waveguide (SIW) cavity supported self-quadruplexing antenna, designed for 5G/6G IoT applications is proposed and prototyped. The model is backed by a rectangular substrate integrated waveguide (RSIW) cavity and features four resonating patches excited separately through four different 50? feed lines. The antenna center frequencies are obtained at 3.29GHz, 4.47GHz, 5.85GHz, and 7.07GHz. Additionally, the cavity is engineered with four sets of micro pockets beneath the patches which can be filled with different materials to offer frequency-agile response. The operating frequencies can be tuned over a wide range between 3.29GHz and 8.4GHz as per the required targets. The layout of the model is chosen meticulously to ensure all ports are co-polarized and isolation between any two is better than 32 dB. The proposed antenna design exhibits competitive performances with a compact size of 0.09 ?g, Front-To-Back-Ratio (FTBR) above 17.83 dB and peak gain of 7.6 dBi. Importantly, all ports are single polarized for the first time in their class. The performance is validated by an equivalent circuit model and prototype characterization. The proposed antenna specifications and configurations well suit for future high-end applications like IoT/5G/6G/satellite communications. The Author(s) 2026. -
Efficient detection of intrusions in TON-IoT dataset using hybrid feature selection approach
This research improves IoT attack classification by introducing a bias-aware dataset refinement strategy that eliminates IP- and port-based identifiers and applies a domain-guided hybrid feature selection framework to derive a lightweight and generalizable feature set. Motivated by the need for intrusion detection models that generalize beyond predefined network configurations, this study focuses on behavior-driven network features that enable more realistic attack categorization in IoT environments. Wrapper-based feature selection methods, including forward selection, backward elimination, and genetic algorithms, identify five optimal features. To assess the robustness of the selected feature subset, both simple classifiers (Decision Tree and KNN) and ensemble learning models, including Random Forest, Gradient Boosting, XGBoost, Bagging, and Voting Ensemble, are evaluated under binary and multi-class settings. Using the proposed reduced feature set, the Decision Tree classifier achieved an accuracy of 0.986 for binary classification and 0.972 for multi-class attack classification, while the K-Nearest Neighbor classifier consistently achieved an accuracy of 0.972 for both binary and multi-class scenarios, while ensemble models yield only marginal performance improvements. Evaluation using precision, recall, F1-score, confusion matrices, and Cohens Kappa confirms that the discriminative power primarily arises from the selected feature subset rather than classifier complexity. These results demonstrate that effective feature selection enables lightweight models to achieve competitive intrusion detection performance suitable for real-world IoT deployments. The Author(s) 2026. -
Analysis of the pooled effect of compression ratio and injection timing variation on conventional diesel engine powered with nano doped biodiesel blend
This research aims to explore how incorporating nanoparticles into a biodiesel-diesel blend influences the performance, combustion, and emission characteristics of a diesel engine under varying conditions, including compression ratios, engine loads, and injection timings. The biodiesel and nanoparticles considered for this investigation are mahua biodiesel and Titanium oxide nanoparticles (TiO2), respectively. The experimental fuel is formulated by blending diesel and mahua biodiesel with the addition of titanium oxide nanoparticles. In this study, compression ratio is varied from 17.5 to 18, whereas fuel injection timing of 20, 23, and 25 BTDCs along with engine load variation of 20%, 40%, 60%, 80%, and 100% are considered. The experimentation utilized a single-cylinder diesel engine equipped with a variable compression ratio (VCR) feature and a power output of 3.5kW. The results indicate that the maximum brake thermal efficiency was achieved at a compression ratio of 18 and a fuel injection timing of 20 before Top Dead Center. For the same setting, the nanoparticle-enriched biodiesel-diesel blend exhibited the lowest levels of CO and HC emissions among all test runs, with reductions of 45.34% and 40%, respectively, compared to standard diesel operation. The Author(s) 2025. -
Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases
Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subjects performance in the offline can manage the task more easily than in online mode. The Author(s) 2025. -
Cloud-enabled e-commerce negotiation framework using bayesian-based adaptive probabilistic trust management model
Enforcing a trust management model in the broker-based negotiation context is identified as a foremost challenge. Creating such trust model is not a pure technical issue, but the technology should enhance the cloud service negotiation framework for improving the utility value and success rate between the bargaining participants (consumer, broker, and service provider) during their negotiation progression. In the existing negotiation frameworks, trusts were established using reputation, self-assessment, identity, evidence, and policy-based evaluation techniques for maximizing the negotiators (cloud participants) utility value and success rate. To further maximization, a Bayesian-based adaptive probabilistic trust management model is enforced in the future broker-based trusted cloud service negotiation framework. This adaptive model dynamically ranks the service provider agents by estimating the success rate, cooperation rate and honesty rate factors to effectively measure the trustworthiness among the participants. The measured trustworthiness value will be used by the broker agents for prioritization of trusted provider agents over the non-trusted provider agents which minimizes the bargaining conflict between the participants and enhance future bargaining progression. In addition, the proposed adaptive probabilistic trust management model formulates the sequence of bilateral negotiation process among the participants as a Bayesian learning process. Finally, the performance of the projected cloud-enabled e-commerce negotiation framework with Bayesian-based adaptive probabilistic trust management model is compared with the existing frameworks by validating under different levels of negotiation rounds. The Author(s) 2025. -
ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites
This study investigates the effectiveness of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. The focus was on analyzing key process parametersabrasive size, feed rate, and standoff distance (SOD)under submerged cutting conditions and their impact on material removal rate (MRR), kerf width, and surface roughness. Experimental trials were conducted, and advanced computational techniques, including Response Surface Methodology (RSM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were used for parameter optimization and predictive analysis. The results showed that submerged cutting significantly improved machining quality by reducing surface roughness and ensuring uniform kerf widths. Increasing the jet diameter in underwater conditions stabilized the nozzle, leading to smoother and more precise cuts. Among the predictive models, XGBoost demonstrated the highest accuracy and efficiency in forecasting MRR, while Random Forest and ANN provided competitive performance. The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. These findings are particularly relevant for industries like aerospace and automotive, where machining efficiency and precision are crucial. The Author(s) 2025. -
High-efficiency stepdown/step-up converter for series-connected energy storage system
This work introduces a novel stepdown/step-up converter designed to optimize the run time of series-connected Battery, whose voltage drops progressively with increased usage, eventually falling below the necessary operating levels. The proposed converter automatically transitions between stepdown, step-up, and stepdown/step-up modes based on a comparison of input and output voltages, with the stepdown/step-up mode restricted to the narrowest range to minimize its lower efficiency in power conversion. It supports an input voltage range from 2.5 to 8V and incorporates a capacitive coupling level shift circuit to maintain the gate-source voltage of the power transistor under 5V, protecting against gate oxide layer damage. Fabricated with 180nm BCD technology, the converters compact size is 1.44mm by 0.73mm. Testing reveals that this converter achieves up to 93% power conversion efficiency, an 11% improvement over conventional models, and supports an output current up to 500mA, a 67% increase, enhancing the performance and longevity of Battery in compact electronic devices. The Author(s) 2025. -
Interface improvement and multiscale assessment of recycled concrete aggregates with epoxy resin polymer
Recycled concrete aggregate (RCA) exhibits challenges like weak bonding, high porosity, and inferior strength compared to natural aggregates. This study evaluates the effect of epoxy resin polymer treatment on RCA on enhancing compressive and split tensile strengths in concrete, replacing natural aggregates with untreated RCA (UTRAC) and treated RCA (ERTAC) at 25%, 50%, 75%, and 100% levels. The tests were conducted at 3, 7, and 28 days. UTRAC showed reductions of up to 26.32% in compressive strength and 35.38% in tensile strength at 100% replacement; ERTAC outperformed control concrete (CC) with gains of up to 26.32% in compressive strength (at 25%) and 122.73% in tensile strength (at 100%), identifying 25% as the optimum replacement ratio. SEM and XRD analyses confirmed improved particle packing, reduced porosity, and stronger interfacial transition zones (ITZ) in ERTAC. The Author(s) 2026. -
A scalable scheduling and resource management framework for cloud-native B2B applications
In modern cloud computing environments, customers increasingly depend on on-demand resource provisioning to handle dynamic workloads. However, fluctuations in job arrival rates can result in prolonged queue times, which negatively affect overall system performance. Although existing scheduling algorithms provide efficient job management, they often fail to account for the combined impact of queue delays and the need for flexible resource provisioningparticularly in business-critical applications. In order to tackle these issues, the paper proposes a new Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm designed to improve job execution efficiency and support elastic resource management in cloud environments. The OJSRS algorithm integrates two key components: Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS). The TJS component constructs a hierarchical structure that concurrently maps incoming jobs to the most suitable Virtual Machines (VMs), thereby minimizing queue delays. Meanwhile, ARSS adjusts resource allocation dynamically, increasing or decreasing capacity according to workload requirements and cloud service provider policies, enabling responsive and adaptive provisioning. Experimental results show that the OJSRS algorithm increases resource utilization by approximately 510% and accelerates job completion through proactive resource scaling. This approach provides a significant performance advantage for cloud-native business applications that require both efficiency and scalability. The Author(s) 2025. -
Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networks
Simultaneous task allocation is crucial for enhancing service quality in Industrial Internet of Things (IIoT) environments. The distribution and management of tasks remain among the biggest challenges in the IIoT era. Efficient allocation strategies are needed to enable transparent network configurations and maximize task throughput. Although recent methods address the dynamic management of objects, they often overlook the correlations between tasks and their associated functionalities. This paper introduces a novel Connected Harmonical Adaptive Task Allocation (CHATA) model for IIoT health networks to ensure fair task distribution. CHATA leverages similarity measures of object functionalities to identify the most suitable object to perform each task. Simulations conducted in NS-3 demonstrate that CHATA achieves up to 90% allocation efficiency in 5G Radio Access Technologies IIoT health environments and significantly outperforms recent approaches in task assignment performance. The Author(s) 2025. -
Robust and imperceptible image watermarking using chaotic map-integrated quantum-inspired multi-objective cuckoo search optimization
With the rapid growth of multimedia data transmission for secure and reliable communication has become critical due to increasing cyber threats. This paper presents a Chaotic Map-integrated Quantum-Inspired Multi-Objective Cuckoo Search (CMQICS)-based watermarking approach to achieve high imperceptibility, robustness, and embedding efficacy. The proposed approach integrates quantum-inspired cuckoo search optimization with chaotic mapping to enhance watermark embedding. A multi-image watermarking scheme is also used to strengthen payload capacity while minimizing visual distortion. The embedding process operates in the frequency domain using a hybrid Discrete Cosine TransformTwo-Dimensional Discrete Wavelet Transform (DCT-2DWT) combined with a zig-zag scanning strategy. This ensures attack resilience. The experimental results show that CMQICS achieves a Peak Signal-to-Noise Ratio (PSNR) of approximately 89 dB, a Structural Similarity Index Measure (SSIM) of 0.99, and an average embedding time of around 1s. Randomness analysis further validates the security of the embedded watermark. The comparative evaluations states that the CMQICS outperforms existing approaches. The Author(s) 2025. -
f(Q) gravity as a possible resolution of the H0 and S8 tensions with DESI DR2
The symmetric teleparallel framework brings about the possibility of alleviating cosmological tensions. The current burning issue in cosmological studies is the increase in discrepancies in measurements from several surveys. Here, we have focused on and tensions, which are important factors in describing the evolution of the Universe from primordial perturbation to late-time acceleration. Additionally, the consistency of the sound horizon is verified against the Planck results. The gravity model is constrained using recently obtained data. Implementing gravitational wave data to study late-time acceleration is one of the key features of our study. Since standard sirens show promising results, the implementation of gravitational waves to probe dark energy is an interesting study. Through our work, we introduce this possibility by performing statistical MCMC analysis for late-time cosmological evolution. Also, the and tensions are explored utilizing gravitational wave data alongside other prominent datasets, such as the latest DESI BAO, redshift space distortion, cosmic chronometers, Pantheon+SH0ES, and cosmic microwave background data. With the results obtained, we analyzed the profile of cosmological parameters. Finally, the study presents the tension of the model with observations, which is found to have a much lower magnitude compared to the current trend. Thus, the considered f(Q) model alleviates tension, making it the best candidate for further investigation. The Author(s) 2025. -
Simultaneous photovoltaic distributed generation and capacitor optimization for enhancing performance indices of radial power distribution system
This paper presents an effective metaheuristic framework using the Osprey Optimization Algorithm (OOA) for the simultaneous allocation of distributed generation (DG) units and capacitor banks (CB) in radial distribution systems (RDS). The method optimizes the locations and sizing for DG units and CB to minimize active power losses (APL), to reduce voltage deviation (VD), and to enhance voltage stability. The performance of the proposed approach is tested on IEEE 69-bus and 118-bus benchmark RDSs and the real-time Tala Egyptian RDS. The OOA achieved superior results compared to popular heuristic algorithms such as antlion optimizer (ALO), hunter-prey optimizer (HPO), and whale optimizer algorithm (WOA). Specifically, for three units of DG and single capacitor integration in the 69-bus system, OOA reduced the total APL by 75.1%, lowered the total voltage deviation (TVD) by 1.4835p.u., and improved the total voltage stability index (TVSI) by 3.0229. With optimal assimilation of three units of DG and capacitors each, APL reduction, TVD minimization, and TVSI improvement further extended to 79.9%, 1.5013p.u., and 2.2787, respectively. Furthermore, OOA validation on a variable-load 69-bus RDS and the real 37-bus Tala Egyptian RDS demonstrated consistent and superior performance, showcasing its robustness. Statistical analyses also confirm OOAs efficiency and ability to solve DG planning in the distribution networks. The Author(s) 2025.