The sustainable management of seawater reverse osmosis (SWRO) brines remains a critical issue for desalination processes and acquires increasing significance at larger scale. In addition to disposal challenges, these brines offer opportunities for resource recovery and circular economy implementation. Increasing brine concentration can facilitate both volume reduction and the extraction of valuable components. This study evaluates, at pilot scale, an integrated brine concentration process combining osmotically assisted reverse osmosis (OARO) technology with a conventional RO pre-concentrator stage, using real seawater RO (SWRO) brine pre-treated by nanofiltration. System performance, energy efficiency and operational behaviour were assessed to identify feasible operating ranges and optimal conditions. Final brine concentrations of up to 245 g/L were achieved, while overall water recoveries varied between 72 and 85%. Specific energy consumption ranged from 7 to 14 kWh/m 3 of permeate production and 18–56 kWh/m 3 of concentrated brine. Optimal performance was identified at target concentrations of 210–230 g/L, providing a balance between recovery, energy demand and process stability. Membrane temperature limitations (≤40 °C) defined the practical operating range and were identified as a key factor influencing system performance. The results confirm that OARO is an energy-efficient and scalable technology for SWRO brine concentration. This experimental validation under unique real conditions supports the integration of OARO in advanced brine management and resource recovery strategies. • Pilot-scale evaluation of OARO technology for real SWRO brine concentration. • Integrated RO–OARO system (28–59 m 3 feed/d) achieved up to 245 g/L brine salinity. • Overall water recovery between 72 and 85% with stable operation. • SEC values of 7–14 kWh/m 3 of permeate and 18–56 kWh/m 3 of brine. • OARO enables energy-efficient and scalable SWRO brine concentration.

The development of advanced electrode materials is critical for improving the efficiency and durability of capacitive deionization (CDI) technologies for water desalination and separation processes. In this work, a novel conductive hydrogel based on agarose (Aga), tannic acid (TA), and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) was designed, optimized, and evaluated as a functional coating for CDI electrodes. The hydrogel formulation was systematically optimized by varying the TA and PEDOT:PSS contents, identifying an optimal composition containing 10 wt% TA and 20 wt% PEDOT:PSS. This formulation exhibited a favorable combination of mechanical robustness, high porosity (~93%), well-distributed pore size, preserved swelling capacity, and enhanced electrochemical properties. Electrochemical characterization revealed improved cathodic stability and capacitive behavior, supporting enhanced ion storage and transport. When implemented in CDI cells, the hydrogel-coated electrodes demonstrated significantly enhanced salt adsorption capacity and higher charge efficiency compared to conventional activated carbon (AC) electrodes. Although the initial salt adsorption capacity was slightly lower than that of other soft-coated electrodes, the gel-based system showed progressive performance improvement and superior long-term cycling stability during aging tests. The enhanced hydration, facilitated ion transport, and sustained structural integrity contributed to improved operational efficiency and durability. Overall, the proposed Aga-TA-PEDOT:PSS hydrogel represents a promising electrode material for energy-efficient, stable, and scalable CDI systems, with potential applications in low-salinity and brackish water treatment. • Conductive Aga-TA-PEDOT:PSS hydrogels developed as CDI electrode coatings. • Optimized hydrogel shows high porosity and mechanical stability. • Gel-coated electrodes enhance ion transport and adsorption efficiency. • Superior cycling stability achieved compared to bare carbon electrodes. • Hydrogel electrodes improve CDI efficiency with potential energy savings.

Time-aware attention network for multi-step future prediction in wastewater treatment.

Beamforming has emerged as an essential enabling technique for beyond 5G and future 6G systems because it improves spectral efficiency. However, optimizing antenna weights in beamforming is a highly nonlinear and multidimensional problem that traditional approaches struggle to solve. To address this, we offer a unique beamforming strategy based on the Caterpillar Fungus Optimization (CFO) algorithm, which strikes an optimal balance between exploration and exploitation, making it ideal for large-scale antenna systems. The CFO is inspired by the rare lifecycle of caterpillar fungus considering its soil exploration and parasitic behaviors. Its unique blend of wave-like and spiral search strategies, dual parasitism operators, and hybrid noise-handling makes it stand out among bio-inspired algorithms, enabling high accuracy and robustness in complex engineering optimization problems. The proposed scheme has two goals: first, to reduce the number of active antenna elements, thereby improving energy efficiency and reducing system complexity; and second, to suppress side lobe levels (SLL), which mitigate interference and improve communication performance. To assess its efficacy, the CFO-based method is compared to five established algorithms: Artificial Rabbits Optimizer (ARO), Whale Shark Optimization (WSO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Boomerang Aerodynamic Ellipse (BAE). According to simulation data, CFO maintains beamwidth deviations within 1% to 2% of the standard reference while achieving an average error reduction of up to 99.7% when compared to PSO and WSO. Furthermore, CFO outperforms all benchmark algorithms in terms of accuracy, and computing efficiency, delivering the lowest SLL deviations and the most steady convergence behavior. This paper provides a simulation-based beamforming optimization framework that employs the metaheuristic Caterpillar Fungus Optimization (CFO) algorithm for antenna array synthesis in beyond 5G and future 6G wireless systems.

The exponential demand for energy-efficient and adaptive computing architectures drives the evolution of artificial intelligence (AI) and machine learning (ML). Neuromorphic computing, inspired by biological neural networks, overcomes the limitations of traditional von Neumann architectures, including high energy consumption and limited scalability. The introduction of two-dimensional (2D) materials, such as transition metal dichalcogenides, hexagonal boron nitride, black phosphorus, and tellurene, enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties. These materials exhibit atomic-scale thickness, high carrier mobility, and tunable bandgaps, facilitating synaptic behaviours such as spike-timing-dependent plasticity and paired-pulse facilitation. This review describes the integration of 2D materials into neuromorphic systems, highlighting applications in wearable electronics, brain-machine interfaces, and quantum neuromorphic platforms. In wearable and edge computing, 2D-based devices enable localized, ultra-low-power data processing. In brain-machine interfaces, they enhance signal transduction and neural interfacing. Quantum effects in 2D materials further enable hybrid quantum-classical neuromorphic architectures for high-dimensional computational tasks. Despite significant advances, challenges in reproducibility, scalability, and stability remain. Addressing these limitations through innovations in synthesis and defect passivation is essential for practical application. This review underscores the transformative potential of 2D-material-based neuromorphic computing for energy-efficient AI. Integration of 2D materials into neuromorphic computing architectures offers a promising pathway toward energy-efficient and adaptive systems that bridge biological learning mechanisms with machine intelligence.

A key challenge in the oxygen evolution reaction (OER) is the development of efficient and durable catalysts for applications such as water electrolyzers and metal-air batteries. Due to sluggish reaction kinetics, the OER typically requires an overpotential significantly higher than the thermodynamic potential of 1.23 V vs RHE, which limits the overall efficiency of electrochemical energy systems. In this work, we report the design and synthesis of an iron-coordinated thiourea-formaldehyde framework, denoted as (Fe-TF) n , as a nonprecious metal-based electrocatalyst for the OER. Its significance lies in the integrated coordination of iron with sulfur and nitrogen atoms within the thiourea-formaldehyde framework. This coordination enables strong dπ-pπ interactions, which enhance electron delocalization and improve electronic conductivity, thereby boosting the catalytic activity of the material. When supported on Ketjen Black, (Fe-TF) n exhibited an OER potential (E j10) of 1.53 V vs RHE at a current density of 10 mA/cm2 and demonstrated stability over 300 h of continuous operation. Furthermore, to evaluate its practical viability, the catalyst was incorporated into a membrane electrode assembly (MEA), and achieved a current density of 70 mA/cm2 at 2.1 V in a water electrolyzer.

The infestation of weeds in cotton fields poses a serious threat to the production of lint. Weeds are eliminated either by spraying weed kill sprays, or removal by hand, or by automated machinery. Machine learning (ML) and deep learning (DL) models are used in these machines for weed classification and detection. Compared with machine learning models, DL models based on neural networks have greater ability to extract features. A spiking neural network (SNN) is used in this paper for the classification of weed images commonly found in cotton fields. The classification of the 6 classes of weeds is accomplished via transfer learning on the AkidaNet Convolutional Neural Network (CNN) model, which is converted to an SNN via software tools from BrainChip. The process includes quantization of the parameters to achieve compatibility for conversion and eventual deployment on neuromorphic devices. The classification accuracy and loss metrics are presented for the training and validation data. The accuracy and loss values achieved during training were 96.26% and 26.67% for the training data and 93.58% and 31.16% for the validation data respectively. Prior to conversion to the SNN, during quantization, the accuracy achieved is 87.96%. With the inclusion of Quantization Aware Training (QAT), the accuracy improved to 90.74%. After conversion from the CNN to the SNN, the model accuracy obtained for the testing data was 94.44%. The results demonstrate that SNN-based models can achieve good classification accuracy with reduced computational cost, making them suitable for deployment on energy-efficient neuromorphic systems. This approach holds strong potential for real-time automated weed detection in precision cotton agriculture.

Scientific computing must integrate AI with simulation and focus on energy-efficient methods and systems.

The development of Mn4+-activated luminescent materials with high optical thermometric sensitivity remains challenging. In this study, a series of novel red-emitting phosphors Sr3La2W2O12:xMn4+ (0.002 ≤ x ≤ 0.012) were successfully prepared by high-temperature solid-state reaction. A systematic investigation was conducted on the crystal structure, elemental composition, photoluminescence characteristics, temperature-dependent luminescence, fluorescence lifetime, and concentration quenching mechanism of this phosphor series. The emission spectrum of phosphor Sr3La2W2O12:Mn4+ exhibits a broad band centered at 717 nm, attributed to the 2Eg → 4A2g and 4T2g → 4A2g transition of Mn4+, which corresponds to far-red emission. This spectral feature aligns well with the far-red absorption band of phytochrome Pfr, making it highly suitable for plant photomorphogenesis applications. Moreover, phosphor Sr3La2W2O12:0.008Mn4+ demonstrates exceptional optical thermometric performance, achieving a relative sensitivity (Sr) of 2.01% K-1. Given its outstanding luminescent and thermal sensing properties, phosphor Sr3La2W2O12:Mn4+ emerges as a promising bifunctional material for applications in non-contact optical thermometry and energy-efficient plant growth lighting systems.

ABSTRACT Infrared and visible image fusion is crucial for multimodal perception, but high-performance models incur excessive computational costs for resource-constrained edge devices. This work introduces TL2Fusion, a unified compression-to-deployment framework that transforms the SeAFusion network into a hardware-efficient implementation. First, a dual-stream-adapted Tucker decomposition achieves substantial model compression. Second, a structured pruning strategy leverages hierarchical dependency analysis and L2-norm-based filter sensitivity to intelligently remove redundant convolutional filters. Finally, a targeted retraining phase restores model accuracy. Evaluations on TNO, FMB, and MSRS datasets demonstrate competitive fusion quality and superior performance in downstream segmentation tasks. Furthermore, by optimizing the GRDB module for FPGA deployment, TL2Fusion reduces memory footprint by 71.3–73.2% and improves power efficiency by 64.2% with minimal computational overhead. This research provides a validated pipeline for deploying real-time, energy-efficient multimodal perception systems at the edge.

Ternary static random-access memory (TSRAM) has emerged as a promising solution for enhancing energy efficiency and information density beyond binary limits, particularly in data-intensive and low-power applications. This paper presents a power-efficient single-supply 14-transistor CNTFET-based ternary SRAM cell that uses a buffer-based topology with a single-bitline read/write scheme. The proposed design eliminates the need for dual supply rails and redundant voltage-division paths, significantly reducing dynamic power and delay while maintaining robust stability. Extensive HSPICE simulations using the Stanford CNTFET compact model demonstrate superior performance compared to state-of-the-art TSRAM cells, achieving the lowest power-delay product of 5.37 aJ at 0.9 V, along with a competitive static noise margin under process variations. To evaluate practical effectiveness, the proposed TSRAM is integrated into a ternary medical image processing framework using hardware-based signal mapping and a weighted k-nearest neighbor classifier. Application-level results show a 26.65% reduction in average energy consumption, a peak signal-to-noise ratio of 41.06dB, a mean structural similarity of 99.83%, and a prediction accuracy of 97.86%. A comprehensive figure of merit confirms a 61.58% improvement over existing designs, highlighting the proposed architecture as a strong candidate for energy-efficient ternary biomedical processing systems.

Fable Hospital 3.0 aims to demonstrate the economic return on investment (ROI) of high-performing design strategies towards regenerative healthcare. Building on the legacy of Fable Hospitals 1.0 (2004) and 2.0 (2011), this third iteration seeks to refute the persistent myth that high performance is unaffordable while addressing current healthcare challenges, including financial pressures, workforce burnout, climate resilience and technological integration. Fable Hospital 3.0 is a cost-benefit analysis of a hypothetical 300-bed, 750 000-square-foot community hospital located in 'Anywhere, USA'. The design incorporates more than 20 high-performance and healing strategies, ranging from biophilic design and energy-efficient systems to flexible infrastructure and community integration. Each strategy is evaluated for its construction cost and potential operational benefits. Excluding the cost of the land, the analysis uses conservative cost estimates based on national averages and industry benchmarks, quantifying ROI reductions in medical errors, energy and water consumption, staff turnover, renovation costs and inpatient length of stay, along with improvements in operational resiliency, material first costs and speed to market. The study demonstrates that an estimated additional investment of $25-30 million (approximately 3% of total construction costs) can be recovered within the first 2 years of operation. Key annual savings include: a major contribution of ~$7.25 million from reduced inpatient length of stay, ~$1.0 million from fewer medical errors, ~$1.2 million from improved staff retention, ~$500 000 from maintaining operations during emergencies, ~$250 000 from energy savings, ~$1.5 million from lowered renovation costs, ~$5.4 million from lower material first costs and ~$100 000 from water use reduction. Fable Hospital 3.0 proposes a model for analysing the impact of adopting high-performance design strategies in healthcare and demonstrates that this approach is environmentally and socially responsible and financially wise, aligning with healthcare's mission to promote health and resilience within and beyond hospital walls.

The rising demand for high-performance, energy-efficient neuromorphic systems has driven the exploration of chalcogenide materials with tunable electronic properties. Here, Ag/GeSe/FTO resistive-switching devices are demonstrated that exhibit Pavlovian associative learning, mimicking classical conditioning through paired electrical stimuli and voltage-dependent adaptation. Amorphous and crystalline GeSe thin films deposited by RF sputtering were systematically compared. Structural analysis confirmed that annealing at 375 °C induces phase crystallization with improved grain integrity and stoichiometric uniformity. Crystalline devices show superior performance, featuring lower SET/RESET voltages, larger hysteresis windows, endurance exceeding 4000 cycles, and retention over 1500 s. Conduction analysis reveals a transition from ohmic to a space-charge-limited transport-mediated mechanism in crystalline GeSe, enabling controlled Ag+ ion migration along grain boundaries, leading to confined filament formation and stable rupture dynamics, which significantly enhance switching uniformity and analog synaptic behavior. Neuromorphic behavior includes stable potentiation, depression, paired-pulse facilitation, and inhibition of long-term potentiation, all demonstrating adaptive and nonlinearly tunable learning. Associative learning, analogous to classical conditioning, was successfully reproduced across multiple cycles. Furthermore, an artificial neural network (ANN) implemented using experimentally extracted long-term potentiation/depression characteristics achieved classification accuracies of ∼73% and ∼87% for the Fashion-MNIST and digit-MNIST data sets at 0.9 V, improving to ∼75% and ∼87% at 1.1 V. These results firmly establish crystalline GeSe as a promising, stable, and cost-effective memristive material for energy-efficient neuromorphic and artificial-intelligence hardware applications.

ABSTRACT Energy justice has emerged as a framework for understanding how energy systems distribute access, affordability, governance power, and the benefits and burdens associated with energy production and consumption. Yet its environmental implications remain insufficiently understood. This study examines whether energy justice contributes to or mitigates environmental degradation using an unbalanced panel of 69 countries from 2000 to 2022. Environmental degradation is measured through the ecological footprint, with CO2 emissions used as an alternative proxy. Using a dynamic two-step system GMM estimator, the results show that higher levels of energy justice are associated with greater environmental degradation. This points to an equity – sustainability trade-off, whereby gains in energy access and affordability may translate into higher consumption and greater environmental pressure when they are achieved through fossil-fuel-dependent energy systems. However, renewable energy deployment moderates this relationship by weakening the adverse environmental effect of energy justice. Additional analyses indicate that technological innovation and economic development reduce this trade-off, while threshold results suggest that the relationship may shift toward synergy once sufficient levels of renewable energy, innovation, and income are reached. The findings are robust to alternative measures of energy justice and environmental degradation, as well as to additional estimators addressing endogeneity and cross-sectional dependence. This study contributes to the literature by showing that energy justice is not automatically aligned with environmental sustainability. Rather, its ecological implications depend on the extent to which equity-oriented energy policies are combined with renewable energy expansion, technological innovation, and economic development that supports cleaner and more efficient energy systems.

Enhanced oxygen transfer, stable nitrifying biofilm, and low N2O emissions in a pilot-scale hybrid MABR incorporating gear-structured gas-permeable membranes.

As atomic-scale archetypes for multi-electron transfer, iron-sulfur clusters are compelling candidates for the design of redox-active, catalytic nanomaterials; yet, translating their enzymatic proficiency into robust synthetic platforms remains a challenge. A key bottleneck lies in replicating the outer-sphere environment, which modulates the cluster's electronic landscape, provides site-isolation, and facilitates catalytic pathways via programmable noncovalent interactions. Herein, we demonstrate a host-guest strategy for systematically tuning iron-sulfur cofactor models within tetrahedral [M4L6]8+ cages (M = Ni, Fe, Zn). Successful encapsulation of [Fe4S4(SR)4]2- (R = Ph, ─CH2CH2OH) was confirmed by 1D/2D NMR spectroscopy and ESI mass spectrometry, while titration studies confirmed association constants (Ka) > 107 m-1. Spectroscopic characterization by 57Fe Mössbauer, X-ray photoelectron, IR, and UV-vis spectroscopy reveals significant shifts in Fe─S bond covalency and electron density that converge toward those of the native, protein-bound cofactors. Control studies with [Fe4S4(StBu)4]2-, which associates with the host primarily through outer-sphere ion pairing, demonstrate that while the electrostatic field generated by the framework's corner vertices is a primary driver of these electronic perturbations, internal confinement effects such as π-π stacking and hydrogen bonding provide important secondary modulations. This work establishes a foundation for utilizing cage-stabilized cofactors in the development of sophisticated, energy-efficient catalytic systems.

Efficient prediction of the remaining useful life (RUL) of lithium ion batteries is essential in ensuring safety, reliability and energy efficiency of new energy-storage and mobility systems. This paper suggests a hybrid machine-learning model that combines regularized kernel canonical correlation analysis (RKCCA) with random-forest (RF) regression, hence allowing one to predict RUL accurately at early stages. In this context, the battery-degradation data can be separated in interpretable information granules by two complementary base models, Granular Model1 (GM1), which uses a small bunch of polynomies to employ degradation patterns on a low-order basis; and Granular Model2 (GM2), which utilizes deep neural networks to implement degradation movements on a higher-order platform. In the RKCCA component, fusion of nonlinear features is through the projection of both heterogeneous sensor measurements, such as voltage, current, and capacity, to an assumed common space in the latent features. This is a transformation that improves the correlation between features and also alleviates effects of noise and the dimensionality in the data. The resulted fused latent representations are used as inputs to the RF regression model to give the final RUL estimate. The application of the experimental analysis to the dataset of MIT Battery Degradation (A123 LFP/Graphite cells) reveals that the suggested RKCCA + RF model significantly exceeds GM1 and GM2. The model outputs a mean relative error of 3.45 cycles, root-mean-square error of 4.75 cycles and coefficient of determination ([Formula: see text]) of 0.9995. Overall, the offered results are the testimony to the fact that the model is a high-ability framework that is able to model nonlinear dynamics of degradation and provide high-accuracy predictions, and that can be implemented effectively in the sphere of data-driven prognostics and predictive maintenance and real-time battery-management systems.

This study investigates the effect of incorporating non-conducting calcium carbonate (CaCO3) nanoparticles into polyvinylidene fluoride (PVDF) to improve its insulating properties. PVDF is widely used as an electrical insulation polymer because of its dielectric performance, thermal stability, and mechanical strength; however, these properties can be further improved for advanced applications through nanoparticle modification. PVDF--CaCO3 nanocomposite samples were prepared, characterized, and tested for dielectric and mechanical properties. The results show that adding CaCO3 nanoparticles improves the insulating response of PVDF, especially at 0.5 wt% and 1.0 wt%, making the nanocomposites promising materials for high-performance insulation in electrical and electronic devices. The findings demonstrate the potential of nanoparticle-modified PVDF for improving energy efficiency and reliability in insulation systems.

Integrating NeuralProphet (NP) forecasting with MATLAB/simulink for energy-efficient PV-grid water pumping

Redefining ground truth for estimating the charge level of lithium-ion batteries using relaxation-based voltage measurements