Interfacial engineering enables non-noble-metal electrocatalytic reduction of perfluorooctanoic acid in water.

Data routing protocols play a vital role in Wireless Sensor Networks (WSNs). However, large network sizes and constrained resources demand more energy-efficient routing strategies. In this context, conventional routing protocols often show weak load balancing and inefficient energy use. Low-Energy Adaptive Clustering Hierarchy (LEACH) and Low-Energy Adaptive Clustering Hierarchy Centralized (LEACH-C) remain the two most widely adopted hierarchical routing protocols in WSNs. LEACH operates as a non-geographic distributed routing protocol, whereas LEACH-C is a geographic-based centralized routing protocol. Compared with flat routing protocols, both can prolong network lifetime, but they still suffer from limited energy efficiency. To address this limitation, we in this research proposed an enhanced LEACH protocol based on cluster configuration and Quantum Beluga Whale Optimization (QBWO-LEACH). During the setup phase, the central base station (BS) employs the proposed QBWO approach, which integrates Beluga Whale Optimization (BWO) with the strengths of quantum computing, to centrally organize the clusters. This process includes determining the cluster centroids, assigning cluster members, and evaluating cluster energy, cluster priority, and cluster lifetime. In the cluster heads (CHs) rotation phase, local clusters use the position and energy information of all cluster members to perform distributed CHs switching, distributing cluster energy approximately evenly among all members. In the steady-state phase, the relay forwarding of monitored data flows is implemented. Compared with traditional LEACH and other improved variants of the LEACH protocols, the comprehensive performance of the protocol proposed in the present research is found to be superior. We compare our proposed QBWO-LEACH with the existing LEACH protocols in terms of node survival, network residual energy, half node dies (HND), last node dies (LND), and first node dies (FND), in all four cases using both simulation and statistical analysis. QBWO-LEACH demonstrates an average improvement of 51.87% over LEACH, 17.69% over Particle Filter LEACH (PF-LEACH) and 4.31% over a 2-stage Genetic Algorithm-based LEACH (GA2-LEACH) in node survival and network residual energy in all four cases.

Alternating current electroluminescence (ACEL) represents a promising strategy for the development of flexible, low-energy-consuming, smart textiles. However, conventional ACEL devices typically suffer from complex fabrication processes, high manufacturing costs, and rigid structural configurations, which severely limit their reusability, conformability, and large-scale deployment. Herein, a simple and scalable electrospinning strategy is proposed to directly incorporate luminescent powders into a polymer nanofiber network, where the fibers act as a supporting scaffold to immobilize the phosphor particles, enabling the construction of a flexible and uniform electroluminescent layer through a one-step process. This approach significantly simplifies device fabrication while markedly improving material utilization efficiency. Furthermore, an adhesively compatible aluminum foil is employed as the base electrode, endowing the resulting ACEL device with excellent portability and a repeatable adhesion capability. The as-fabricated device can firmly adhere to a wide range of textiles and irregular surfaces, allowing arbitrary attachment and on-demand application. The film formation mechanism of the electrospun luminescent layer is systematically elucidated, and key processing parameters governing structural integrity and luminescent performance are identified. As a result, the device maintains stable and efficient electroluminescence under repeated adhesion and bending cycles. This work provides a practical and energy-efficient route toward environmentally friendly, highly adaptable ACEL devices, offering significant potential for next-generation wearable electronics and smart textile applications.

Ultraviolet optoelectronic logic gates (OELGs) are becoming the core components of next-generation logic circuits with applications for precise detection, image recognition, and brain-inspired computing. However, their practical application is limited by high energy consumption and slow response. Here, we develop a CoAl2O4/4H-SiC heterojunction logic platform by utilizing both vertical and lateral photovoltaic effects and achieve multiple OELGs operating in the ultraviolet range and exhibiting ultralow energy consumption and ultrafast and stable operation. Explicitly, a single device achieves switching among XNOR, NOR, AND, and XOR operations under 51.7 nW/cm2 of 266 nm laser illumination simply by adjusting the laser position, with a response time of 0.39 μs. The resultant OELGs exhibit solar-blind and high radiation resistance characteristics and maintain 73% performance at 433 K. Compared to traditional CMOS logic gates, this design reduces transistor count by up to 91.7%, offering a highly integrated and energy-efficient route for large-scale data processing.

Desorption energy remains the primary bottleneck in CO2 capture technologies, with most existing processes relying on energy-intensive thermal or electrochemical swings. Here, we report a solar-regenerable CO2 capture system that enables complete desorption using light alone, eliminating external heating and significantly reducing energy penalties. Plasmonic amine sorbents were engineered by anchoring tetraethylenepentamine onto black gold. The high-surface-area fibrous nanosilica supports dense amine loading, while broadband plasmonic absorption generates intense electromagnetic hotspots for light-responsive behavior, resulting in high CO2 capacity (4.16 mmol g-1) and efficient light-induced desorption. Kinetic analysis shows rapid desorption in light as compared to dark. Mechanistic studies reveal that both photothermal effects and hot electrons synergistically drive CO2 desorption, unlocking a new nonthermal desorption pathway. In-situ DRIFTS reveals reversible ammonium-carbamate photoswitching, corroborating the CO2 capture-release mechanism with sustained performance across multiple cycles. This work enables a modular and energy-efficient route toward a zero-heat, light-driven CO2 capture process.

Microwave-assisted pyrolysis of kraft lignin was used to produce highly porous carbonaceous materials for environmental applications (biochars). LignoForce hardwood (LFHL) and softwood (LFSL) lignins were mixed with 30% potassium phosphate (K3PO4) and pyrolyzed at 450 °C to produce 45-47% biochar after 25 min of microwave irradiation. A 10 L min-1 nitrogen flow was used for purging during the pyrolysis and cooling. Elemental analysis (CHNS-O) revealed that kraft lignins were highly deoxygenated, but nearly 26% of their original oxygen content remained after pyrolysis, suggesting retention of oxygenated compounds in the biochar structure. Nitrogen adsorption (BET), scanning electron microscopy (SEM), and X-ray diffraction (XRD) demonstrated the formation amorphous micro- and mesoporous carbonaceous materials with a high potential for water and soil remediation. Significant specific surface areas (142 and 202 m2 g-1 for LFHL-B and LFSL-B, respectively) were obtained for these lignin biochars, which were tested for methylene blue (MB) adsorption at pH 4.5, 6.0, 7.5, and 9.0. LignoForce lignin biochars presented an adsorption capacity of approximately 15.5 mg g-1 and a removal efficiency higher than 94%, especially at higher pH levels (>4.5). However, MB adsorption was slightly faster for LFHL-B compared to LFSL-B. These results position kraft lignin as a valuable carbon-accumulating raw material for environmental applications and establish MAP as a fast and energy-efficient route for their thermal conversion.

Underwater exploration is vital for understanding marine life and accessing natural resources, yet only a small fraction of the underwater world has been studied due to communication challenges. Underwater Wireless Sensor Networks (UWSNs) enable data collection through distributed sensor nodes, but existing routing protocols face critical issues including high energy consumption, frequent node failures, packet losses, long delays, and poor throughput. To address these limitations, we propose Robust Uneven Load Balancing-Direction Aware Best Route Selection (RUL-DBRS), a novel energy-efficient routing protocol for UWSNs. RUL-DBRS employs uneven load balancing based on the average residual energy of available paths, reducing dead nodes, lowering end-to-end delay, and enhancing throughput. By combining direction-aware routing with adaptive load distribution, the protocol minimizes congestion and packet losses, leading to more reliable communication. Simulation results demonstrate that on average RUL-DBRS reduces the packet loss rate by 20%, minimizes end-to-end delay by 40s, enhances nodes' life network by 5%, improves throughput by 20%, and consumes 29% lesser energy in comparison with E2MR-HOA, making it a promising solution for efficient and sustainable underwater communication.

Electrochemical oxidative dehydrogenation (ODH) of ethane in solid oxide electrolysis cells (SOECs) offers an energy-efficient route to ethylene but faces a trade-off between conversion and selectivity due to over-oxidation. Conventional voltage-current regulation can suppress deep oxidation but inevitably compromises ethane conversion. Here, we engineer surface electronic structures by depositing a V2O5 layer on SrFe0.9Ti0.1O3-δ (STF), introducing intrinsic O 2p (-1.33eV) and V 3d (-0.18eV) states closer to the Fermi level than in STF (-1.49/-4.52eV). Density functional theory and operando infrared spectroscopy reveal three synergistic effects: enhanced ethane adsorption (ΔEads -0.33vs. -0.11eV), reduced first dehydrogenation barrier (ΔG1 1.13vs. 1.15eV), and promoted ethylene desorption ((ΔGdes-ΔG3) -4.98vs. -1.92eV). The optimized anode delivers 65% yield and 90% selectivity at 750°C, exceeding unmodified STF by 10%. This work highlights band-center engineering as a promising design concept for regulating hydrocarbon electrode reactions.

This paper proposes QIHOR-WSN, a Quantum-Inspired Hybrid Optimization Framework that jointly optimizes clustering and routing in Wireless Sensor Networks (WSNs) to address the dual challenge of energy depletion and network lifetime in large-scale, heterogeneous deployments. In resource-constrained WSNs with unevenly distributed nodes and limited battery capacity, existing methods treat clustering and routing as separate problems, yielding suboptimal global performance and insufficient adaptability to dynamic network conditions. The proposed framework integrates Quantum Particle Swarm Optimization with a Genetic Algorithm to achieve a balanced exploration-exploitation trade-off and superior convergence characteristics. An innovative multi-objective fitness criterion is developed by collectively addressing residual energy, node density, communication distance, and network stability to facilitate energy-conscious cluster-head selection and the most energy-efficient routing paths. The framework presents a density-load-aware radio energy model that extends the classical first-order radio model to account for spatial heterogeneity and communication interference. A complete algorithmic implementation is provided, accompanied by a theoretical complexity analysis establishing O(T·(N2 + P·N)) computational cost and O(P·N) memory requirements. Comprehensive simulations over 1,000 rounds on 100-node random deployments demonstrate that QIHOR-WSN consistently outperforms all five baseline protocols. Against classical protocols (LEACH, DEEC), network lifetime improvements reach 41-120% (FND metric); against the strongest hybrid baseline (Hybrid PSO-GA), QIHOR-WSN achieves 21.5% longer FND, 10.2% lower total energy consumption at round 1,000, 27.9% extended high-PDR operation, and 22.6% lower end-to-end delay-all statistically significant over 30 independent simulation runs (CV ≤ 5.6%). These gains confirm the robustness, scalability, and practical suitability of QIHOR-WSN for next-generation WSN applications including industrial IoT, environmental monitoring, and smart city infrastructure.

Optoelectronic synaptic devices enable in-sensor processing of enhanced edge detection and contrast resolution in complex visual scenes due to their excellent capability to emulate the functions of visual neurons, such as light perception and image processing, while lateral inhibition synaptic plasticity refines spatial selectivity and extends the dynamic range by suppressing redundant signals and amplifying subtle variations in input intensity. The incorporation of lateral inhibition into a single optoelectronic synaptic device will offer a cost-effective and energy-efficient route for directing a robotic arm to perform responding motions and developing highly efficient machine vision systems. Herein, we demonstrate an optoelectronic artificial synapse established on a novel heterostructure consisting of metal oxide In2O3, polycrystalline Cs2AgBiBr6perovskite, and indium-gallium-zinc oxide thin film, which enhances the optoelectronic response and corresponding synaptic plasticity of the devices, enabling the emulation of neural behaviour and advanced information processing. The structure simulates excitatory synaptic activity through light stimulation and mimics lateral inhibition through electrical stimulation, effectively replicating the neural mechanisms of synaptic plasticity in processes such as Mach bands, contrast enhancement, and Hermann's grid. Leveraging these properties, we develop a lateral inhibition network for image recognition, achieving 97% accuracy-surpassing conventional networks at 93%. Additionally, through seamless integration with robotic arms, it can execute colour chip recognition on a machine cart, providing a promising strategy for the design of intelligent autonomous devices and bioinspired robots.

Inverse vulcanization represents an effective strategy for transforming surplus elemental sulfur into value-added polymeric materials; however, current approaches are largely restricted to olefin-based monomers and rely on high-temperature radical processes. Here, we establish an epoxide-enabled inverse vulcanization platform that expands sulfur-rich polymer formation beyond olefin chemistry under solvent-free, base-catalyzed conditions. Mechanistic studies confirm a nucleophilic ring-opening pathway in which sulfur is incorporated into the polymer backbone when catalyzed by base catalyst. By integrating bio-based epoxidized vegetable oils with elemental sulfur, sulfur-rich networks are constructed through a catalytically tunable ring-opening pathway, enabling controllable network formation. The resulting materials maintain high sulfur content while exhibiting tunable mechanical properties, shape memory behavior, and strong adhesion on stainless steel, with lap shear strengths adjustable up to 10 MPa. The combination of mild processing conditions, renewable monomer feedstocks, and robust structural performance demonstrates a controllable and energy-efficient route for advancing inverse vulcanization toward sustainable adhesive and functional material applications.

Defect-engineered hydrogen-terminated diamond optoelectronic synapses for UV-driven neuromorphic computing.

We numerically demonstrate a compact hybrid metasurface enabling low-voltage and dual-field control of enhanced Faraday rotation in the sub-terahertz regime. The structure combines a thin-film antiferromagnet (MnF2), exhibiting resonance near 268GHz, with TiO2-based memristive resonators whose plasma frequency is electrically tunable. Resonant quasi-crossing between antiferromagnetic and electronic modes results in more than sixfold enhancement of the polarization rotation compared to a bare antiferromagnetic layer. Continuous tuning of the rotation angle is achieved via simultaneous application of static magnetic and DC electric fields. Voltage-driven modulation below 1 V provides efficient electrical control of the magneto-optical response without requiring strong magnetic bias variation. The proposed platform offers a compact and energy-efficient route toward reconfigurable polarization control in sub-THz photonics, with potential applications in tunable isolators, modulators, and hybrid quantum transduction systems.

Selective separation of naphthalene sulfonic acids and salts from wastewater by electric field-assisted nanofiltration membranes.

In recent times, wireless sensor networks are constrained by limited node, dynamic topology, and uneven cluster formation, which affect energy-efficient routing, balanced load distribution, and reliable data transmission. To address these challenges, this research introduces a Fuzzy Logic-based Multipath Hybrid Goshawk Game Search Optimization framework for energy-efficient clustering and routing. The Hybrid Goshawk Game Search Optimization algorithm is employed to select optimal cluster heads by integrating the global exploration capability of Northern Goshawk Optimization with the adaptive local exploitation efficiency of Hunger Games Search algorithm, where the hybrid interaction is governed by a fitness-driven adaptive mechanism that balances exploration and exploitation, thereby improving convergence stability. The Fuzzy Logic System is designed for dynamic sleep scheduling using residual energy, node degree, and distance to the cluster head, which reduces redundant energy consumption and enhances load balancing. Moreover, the Hybrid Goshawk Game Search Optimization-based multipath routing mechanism is designed to establish a reliable and energy-aware communication path by considering hop count, signal strength, buffer rate, and residual energy. Simulation results demonstrate that the proposed model achieves superior performance with a higher packet delivery ratio of 98.89%, throughput of 350 Mbps, and reduced energy consumption of 36J compared to existing methods. These results emphasize that the proposed model provides a robust and scalable solution for efficient data transmission in a large-scale and dynamic wireless sensor network environment.

Natural and human-made disasters can severely impair terrestrial communication infrastructures and disrupt emergency response coordination in modern smart cities. To address these challenges, this paper introduces the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme, an adaptive, secure, and energy-efficient drone-assisted solution for post-disaster network recovery and emergency response. WAY-CR integrates three main components: First, a novel WAY-based metaheuristic optimizer incorporates the concept of Yo-Yo Motion into the conventional Weighted Average Algorithm (WAA), improving the balance between exploration and exploitation during CH selection and clustering. Second, a secure communication model combines the Paillier Homomorphic Cryptosystem (PHC) with a trust evaluation model to provide end-to-end security and authenticity, ensuring that only authenticated and trustworthy drones participate in communication and routing. Third, a Trust-Aware Boltzmann Path Selection method introduces probabilistic decision-making into routing, allowing adaptive selection of secure and energy-efficient routing paths. WAY-CR formulates a multi-objective optimization model that minimizes communication cost and energy consumption while maximizing trust, link stability, and coverage. Stage 1 addresses secure intra-Ground Control Station (GCS) clustering, authentication, and trust management, whereas Stage 2 restores inter-GCS connectivity through a Secure Relay Discovery and Verification procedure based on Boltzmann Path Selection. An adaptive maintenance mechanism further supports dynamic reconfiguration in response to CH failures, mobility, or trust degradation, thereby preserving stable network performance under disaster-induced disruptions. Extensive simulation results show that WAY-CR outperforms state-of-the-art Flying Ad Hoc Network (FANET) baselines in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery, highlighting its potential as a resilient, scalable, and secure solution for post-disaster smart-city environments.

Energy-efficient routing in Wireless Sensor Networks (WSNs) is a critical challenge due to uneven energy depletion and dynamic topology changes. The paper suggests a Lifetime-Aware Ant Colony Optimization-based Routing Algorithm (LTAWSN) which incorporates the residual energy, hop count and spatial proximity to probability routing. The proposed approach, in contrast to the traditional Ant Colony Optimization (ACA) and Energy-Aware ACA (EAACA) uses two energy metrics and spatial awareness to balance energy usage and enhance routing efficiency. LTAWSN performance is measured by using NS-2 simulations and compared to the performance of ACLR and ACA and EAACA. The simulation outcomes indicate that LTAWSN can save the energy consumption by 18-25%, enhancement in the percentage of packet delivery (PDR) by 6-10%, and network lifetime in different node densities. These findings verify the suitability of the proposed strategy to increase the network stability, reliability, and energy balancing in WSNs settings.

Optimization of kneading parameters for lignocellulose fibrillation.

This work demonstrates the effectiveness of rapid solid-state calcination to synthesize sillenite-type Bi 12 TiO 20 with superior solar photocatalytic activity. Stoichiometric mixtures of Bi 2 O 3 and TiO 2 were calcined at 500–800 °C for 3 to 240 min to evaluate the effect of thermal treatment on phase formation, microstructure, and optical properties. XRD and SEM confirmed that a pure Bi 12 TiO 20 phase formed completely at 800 °C after only 3 min. Although all samples at this temperature were phase pure, prolonged calcination produced subtle variations in lattice parameters and band gap, revealing a strong influence of dwell time on structural and electronic properties. In Rhodamine B degradation under sunlight, the 3 min sample showed the highest activity, achieving 85% removal in 60 min and outperforming commercial anatase TiO 2 . This enhanced efficiency arises from a favorable density of oxygen vacancies that promote charge carrier separation and reactive oxygen species generation, enabling a rapid, energy-efficient route to high-performance photocatalysts.

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an -greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs.