A three-dimensional multiphysics coupled numerical model for a proton-conducting planar single-rib PCEC was developed using COMSOL Multiphysics to investigate electrochemical and thermal behaviors under different operating conditions. The model integrates electrochemical kinetics, mass transport, momentum conservation, and heat transfer to analyze interactions among flow distribution, polarization heat generation, and temperature evolution within the PEN region and flow channels. Experimental polarization curves were used for validation, and the maximum relative error remained within 2.43%. Parametric analyses were conducted to evaluate the effects of operating temperature, air flow rate, air-to-steam ratio, and flow configuration under co-flow and counter-flow operation. Operating temperature was identified as the dominant factor affecting electrolysis performance. Under 1.5 V operation, current density increased by 163.6% in co-flow configuration and 156.9% in counter-flow configuration when temperature increased from 873K to 973K. Higher operating temperature also increased PEN temperature difference from 50.51 K to 129.90 K in co-flow configuration. Air flow rate and gas ratio showed moderate effects on electrochemical performance, with current-density variations remaining within 10-12%, but strongly affected temperature distribution and heat removal behavior. Increasing air flow rate improved oxygen transport and reduced concentration polarization, whereas higher steam fraction improved heat removal and reduced temperature gradients because of the larger heat capacity of the gas mixture. Flow direction showed limited influence on electrochemical performance, with current-density differences remaining within 5-8% under identical operating conditions. However, counter-flow configuration significantly improved thermal uniformity and reduced PEN temperature difference by 60.5-69.8% compared with co-flow operation under 1.5 V electrolysis conditions.

This study aims to develop proton exchange membranes that can be effectively utilized in energy systems like fuel cells, batteries, etc. These systems play an important role in developing green energy solutions for the sustainable environment. This research mainly focuses on fabricating various proton exchange membranes using different polymers and nanomaterials. The aim was to fabricate proton exchange membranes that can be used in energy systems such as fuel cells. Incorporating SPEEK and metal organic framework has enhanced the membranes water uptake and ion exchange capacity. The functionalized polymers showed higher proton conductivity when incorporated in different matrices. In contrast, the membranes showed lesser conductivity when added together in the same matrix. The PES/SPEEK/MIL-100(Fe) have achieved highest proton conductivity of 6.324 × 10–5 S/cm respectively, due to the presence of sulfonic acid group and the proton conductive nature of MIL-100(Fe). The chemical stability of membranes has reduced upon addition of SPEEK and MIL-100(Fe). Overall, PES/SPEEK/MIL-100(Fe) demonstrates significant potential to enhance proton exchange membrane performance.

The unique properties of metal–organic frameworks (MOFs), such as their high surface area, adjustable pore structure, and diverse chemical function, have led them to be a new class of crystalline porous materials. Due to these properties, MOFs have a wide range of uses in the energy and environmental fields, including gas storage and separation, carbon capture, catalysis, sensing, energy storage, wastewater treatment, and the remediation of pollutants. This article presents a review of MOFs structural properties, synthesis methods, classification schemas, and characterisation techniques. It will consider MOFs environmental applications, including: CO2 capture, dye adsorption, heavy metal removal and the filtration of particulate matter, as well as MOFs' energy applications, specifically: gas storage, electrocatalysis, photovoltaics, and energy conversion systems. The review also cover recent advances in MOF design and functionality through newly developed computational modelling tools and modification techniques, as well as provide an analysis of the practical issues facing MOFs related to their scalability, stability, and implementation, while summarising future research needs

Optimal thermal management is critical for ensuring the safety and longevity of lithium-ion batteries (LIBs), yet efficient thermal regulation remains challenging. This study investigates air cooling and phase change material (PCM)-based strategies, examining how fin geometry (disk versus sinusoidal) and quantity affect thermal performance. A three-dimensional numerical model was developed using the enthalpy-porosity method to simulate PCM melting, with validation against experimental data. Seven cooling configurations were evaluated, including air cooling, PCM-only cooling, and PCM combined with copper fins of varying geometries (disk and sinusoidal) and quantities (one to three). The module comprised a tightly packed array of 18650-type LIB cells, with uniform heat generation applied under 3C discharge/charge conditions. Two key novelties are presented: a comparative evaluation of cooling strategies and a quantified thermal-mass trade-off relevant to electric vehicles and aerospace applications. Results show that air cooling produced a 12 K temperature gradient, with final row temperatures reaching 318 K. PCM reduced peak temperature by similar to 16 K and improved temperature uniformity. Increasing fin count from 1 to 3 enhanced uniformity by 35.2 %, with fin number more influential than shape. The two-fin configuration offered optimal balance, lowering temperature variation by 28 % with only a 3.26 % increase in system mass.

Among the most energy and cost-efficient flue gas after-treatment technologies is the catalytic oxidation of harmful emissions including carbon monoxide and organic gaseous compounds (OGC). The present work aims to shed light on the transition of lab-scale research under ideal conditions toward realistic experiments focusing on the catalytic oxidation of carbon monoxide (CO), as the simplest pollutant via Pt-Pd monolithic catalyst. The catalytic testing demonstrated that the conversion curves could be divided into three major, almost linear parts (A, B and C) and two transition parts, with part B being much steeper, implying that a minor change in flue gas temperature causes a significant change in CO conversion rate. Part B is considered as a measure of the catalytic activity considering that the steeper the increase the better the catalytic performance as it approaches the full conversion. The comparison of the conversion curves of the artificial and realistic flue gas experiments demonstrated a shift towards higher temperatures (30 - 40 degrees C) in part B, indicating that in the realistic flue gas, there are inhibiting factors such as water vapour and/or CO2, especially at lower temperatures (<200 degrees C) where the adsorption process of these species is favoured.

The performance of solar chimney power plants is limited by insufficient quantitative insight into how geometric parameters affect buoyancy-driven flow, thermodynamic irreversibilities, and power output under site-specific climates such as Tabas, Iran. To address this gap, this study develops a validated computational framework integrating CFD simulations with a machine-learning-based Artificial Neural Network (ANN), implemented using a Multi-Layer Perceptron (MLP) architecture, to efficiently capture nonlinear geometry–performance interactions. The numerical framework solves the Reynolds-averaged Navier–Stokes equations with the standard k–ε turbulence model, the discrete ordinates radiation model, and the Boussinesq approximation for buoyancy-driven flow, while resolving coupled heat transfer mechanisms. The MLP is specifically employed to learn complex nonlinear interactions among geometric parameters, enabling accurate, low-cost prediction and sensitivity analysis across the design space. Results are reported relative to a baseline configuration (chimney height 200 m, collector radius 140 m, divergence angle 0°; power = 85.54 kW, entropy generation = 0.04562 W m−3 K−1). Increasing chimney height to 250 m raises power to 97.30 kW and lowers entropy generation to 0.04321 W m−3 K−1; lowering height to 100 m reduces power to 52.08 kW and raises entropy generation to 0.04926 W m−3 K−1. Expanding collector radius to 170 m increases power to 93.74 kW and entropy generation to 0.04669 W m−3 K−1, while reducing it to 80 m yields 66.88 kW and 0.02801 W m−3 K−1. Raising divergence angle to 0.4° boosts power to 116.22 kW and lowers entropy generation to 0.03397 W m−3 K−1, demonstrating strong geometric sensitivity. Diurnal simulations predict a peak power output of 267.3 kW at a pressure drop of 275 Pa. The MLP model achieves R2 > 0.99, indicating strong potential for site-specific performance analysis of future SCPP designs.

Lithium batteries generate substantial heat during high-rate charging and discharging, which can result in nonuniform temperature distributions, leading to performance degradation and severe thermal runaway risks. To improve the thermal stability and operational safety of battery systems, this study focuses on Lithium Titanate (LTO) cells-renowned for their superior safety and long cycle life-within an immersion cooling system. It proposes a structural optimization strategy for the internal guide vanes of the battery module. A 3D computational fluid dynamics model was developed to simulate the thermal and flow behavior under various geometric parameters, including guide vanes number (N), length (L), angle (alpha), and inlet/outlet position (P). The Taguchi method was first applied to analyze the sensitivity and variance of single performance metrics: maximum temperature difference (Delta T max ), and horizontal and vertical temperature standard deviations (IOHS, IOVS). Subsequently, a multi-objective optimization approach was performed using Grey Relational Analysis (GRA) integrated with the Entropy Weight Method (EWM) to account for the relative importance of each performance indicator. Simulation results show that the optimal configuration (Case 19) with 4 guide vanes, 60 mm length, 45 degrees inclination, and diagonal inlet/outlet layout achieves a 21% reduction in Delta T max (from 5.84 degrees C to 4.62 degrees C), a 17.6% decrease in horizontal, and 14.2% in vertical temperature standard deviation, significantly improving temperature uniformity and cooling effectiveness. The weighted GRA analysis demonstrates improved decision robustness over unweighted methods by incorporating the importance of the entropy-based indicator.

This study presents a numerical simulation to investigate the effects of channel-to-rib ratio and serpentine flow field design on the performance of vanadium redox flow batteries. The analysis consists of two stages: first, three different channel-to-rib width ratios (Wc:Wr = 1:2, 1:1, and 2:1) are compared in terms of pressure drop, species concentration distribution, and charge–discharge behavior. Based on the best-performing ratio (1:2), the second stage further examines the effect of serpentine flow fields with varying inlet numbers, 1-inlet, 2-inlet, and 4-inlet serpentine configurations, on battery performance. Simulations are conducted under fixed operating conditions of 80 mA/cm2 current density and 60 ml/min electrolyte flow rate. Key metrics such as pressure drop, concentration field, initial voltage, capacity, and three efficiency indicators (Voltage efficiency, Coulombic efficiency, and Energy efficiency) are analyzed. Experimental results reveal that best performance across all metrics, with a Coulombic efficiency of 98.42%, voltage efficiency of 90.64%, and energy efficiency of 89.21%, were exhibited by the Wc:Wr = 1:2 structure. The 1:1 configuration ranks second (Coulombic efficiency of 97.82%, voltage efficiency of 89.91%, and energy efficiency of 87.96%, respectively), followed by the 2:1 configuration (Coulombic efficiency of 97.60%, voltage efficiency of 88.71%, and energy efficiency of 86.58%). Regarding inlet configurations, the 1-inlet serpentine provides the highest capacity (1086.65 / 1063.96 mAh) with the lowest polarization loss, while the 2-inlet design achieves the highest energy efficiency (89.21%) and Coulombic efficiency (98.42%). In summary, the 1-inlet and 2-inlet serpentine designs are respectively suitable for capacity-oriented and efficiency-focused applications, offering valuable insights for optimizing VRFB flow field design.

This study explores a natural convection model in a cavity placed horizontally with fins. The traditional continuous fin geometry is replaced by a staggered type. The effects of fin height, spacing, thickness, and arrangement are investigated. Firstly, the inverse method is used to estimate the actual heat transfer rate in the experiment and discuss the turbulence model suitable for the experimental model. After that, the direct method is used to study the impact of different geometric parameters and arrangements on the natural convection heat transfer of the staggered fins placed horizontally in the cavity. The built-in function is used to obtain the temperature field and velocity field and then calculate the average Nusselt number and equivalent thermal resistance for comprehensive analysis. The inverse solutions show that the RNG k-ε model is the most suitable among the three turbulence models. The direct solution results show that increasing the fin height reduces the average Nusselt number, but expands the heat transfer area and improves the thermal performance. In addition, increasing the fin spacing enhances the natural convection effect between fin channels, thus improving thermal performance. Although expanding the fin thickness can increase the heat dissipation effect, it is not as significant as increasing the fin height. Finally, the staggered fin arrangement can promote fluid convection and reduce the thickness of the thermal boundary layer, thereby improving thermal performance.

Gram-positive bacteria are essential for the structural stability and functionality of biofilms in microbial electrochemical systems (MESs). This study evaluated the effects of lysozyme-induced disruption of Gram-positive bacteria on the microbial electrolysis cell (MEC) performance and biofilm composition. Lysozyme treatment reduced biofilm thickness by 37.7% and biomass by 80% due to the peptidoglycan hydrolysis and increased cell lysis, leading to higher proportions of dead cells in both the anode (18.02 to 57.7%) and the cathode (21.9 to 55.2%) biofilms. However, the metabolic capacity of anodic microorganisms (59 to 360 Coulomb produced by 1010 microorganisms) was enhanced due to enhanced cell permeability and a looser biofilm structure that facilitated electron transfer. Conversely, the cathodic performance of the electron recovery efficiency decreased (from 81.97 to 70.92%), and the H2 and CH4 productions were reduced by 43 and 11%, respectively. This was attributed to the loss of key Gram-positive species and weakened microbial network connectivity. Network analysis revealed enhanced modularity at the anode with stabilized performance, whereas the cathode network was sparse and had impaired microbial interactions. These findings have accentuated the dual roles of Gram-positive bacteria in maintaining biofilm stability and microbial interactions, influencing discrete anode and cathode processes.

The thermal performance assessment of desiccant air-conditioning (DAC) systems is critical for improving energy efficiency in climate control applications. This study aims to develop an optimal deep learning model to predict the thermal performance of silica gel-based standalone and Maisotsenko cycle (M-cycle) integrated DAC systems. Fourteen different tuning models were constructed using various optimization strategies, including Bayesian optimization (GBRT and GPR), random search, dummy models, and random forest models with various acquisition functions (EI, PI, RCB, and GB hedge). These models were fine-tuned using a comprehensive set of hyperparameters, such as the learning rate, dense layers, activation function, initialization mode, optimizer, decay rate, batch size, and epochs. The performance of each model was evaluated based on the R2 values, with the optimal model achieving an R2 value of 0.998, demonstrating high prediction accuracy. This optimal model was trained on an experimental dataset of DAC systems to predict thermal performance. The findings highlight the effectiveness of using AI-based surrogate models for the robust and optimal assessment of DAC systems, contributing to more efficient design and operation strategies for climate control technologies.

ABSTRACTThis article investigates the use of osmotic dehydration and ultrasonic pretreatment to improve the efficiency of vacuum freeze‐drying (FD) and microwave‐assisted vacuum freeze‐drying (FD‐VMD) methods. The study examines the influence of pretreatment time, osmotic solution concentration, and ultrasonic power on the moisture content of the pineapple samples. The results show that osmotic dehydration pretreatment can reduce moisture content and weight by up to 15.2% and 8.36%, respectively, while increasing sugar content by 7.5°Bx. Ultrasonic pretreatment is even more effective, with moisture content and weight decreasing by up to 37.5% and 19.45%, respectively, and sugar content decreasing by 4.1°Bx. The FD process shows no significant difference in moisture curve between pretreated and untreated samples, but the pretreated samples have a lower initial moisture content, leading to a potential 35.01% reduction in drying time. The study also evaluates the quality of the dried samples using eight performance indicators, finding that osmotic dehydration pretreatment improves sugar content, crispiness, and flavor, whereas ultrasonic pretreatment enhances rehydration rate, reduces final moisture and sugar content, and results in a softer texture. Additionally, pretreatment significantly reduces drying time and energy consumption, particularly ultrasonic pretreatment with a 120 W power and 40‐min duration, significantly reduces drying time and energy consumption by up to 30.02%. These findings demonstrate the positive impact of pretreatment on the energy efficiency and quality of pineapple slices under the FD‐VMD process.

To predict the bubble departure diameter in pool boiling heat transfer, this study proposes a deep-learning neural network based on physical input parameters from the existing bubble departure diameter predicting correlations and Pearson correlation for a variety of working fluids, engineered surfaces, and materials subjected to different pool boiling testing conditions. This work analyzes nearly 5,000 data points (from the literature) of bubble departure diameters ranging from 0.2-28.7 mm using neural network by incorporating the impactful input parameters such as saturation temperature, pressure, contact angle, surface roughness, surface tension, liquid density, vapor density, wall superheat, and heat flux, and other thermophysical properties, predicting their impact on the bubble departure diameter, and also uses them for training neural networks. The best neural network designated as Case-4, selected on the basis of coefficient of determination (R2), mean absolute error (MAE), and mean-square error (MSE) was used to understand the degree of influence of each input parameter and it was found that surface inclination (theta) and heat flux (Q) have the highest impact on the model. A comparison was also done to the existing correlations and it was found that neural networks have much better efficiency and accuracy than the empirical correlations for the considered data range and thus can be an essential tool to predict the bubble diameter.

This study evaluates the dehumidification performance of Nafion 212, Nafion 117, and self-made Carboxyl polyimide (PI) composite membranes. Performance indicators include dry air permeance, measured using the ISO 15105-1 standard, and water vapor permeance, tested according to ASTM-E96. Experimental conditions varied across flow rates (5 LPM, 10 LPM, 15 LPM, 20 LPM), temperatures (27 degrees C, 35 degrees C, 40 degrees C, 45 degrees C), relative humidity (50 %RH, 60 %RH, 70 %RH, 80 %RH), and vacuum pressures (5 Torr, 10 Torr, 15 Torr, 20 Torr). Results showed that for dry air permeance under specified parameters, the vacuum pressure recovery rate followed the order: Nafion 212 > Nafion 117 > Carboxyl PI. Similarly, water vapor permeance tests indicated a ranking of Nafion 212 > Nafion 117 > Carboxyl PI in dehumidification water rate and water vapor permeance, with variations attributed to the influence of the measurement module's membrane area on flow rate effects. Additional experiments on temperature, humidity, and vacuum pressure effects further confirmed the same trend, where Nafion 212 has the best performance of the three in all dehumidification conditions.

Heat transfer and energy storage characteristics in double-layered enclosure packed with microencapsulated phase change material (MEPCM) are investigated numerically and experimentally in details. The rectangular enclosure is partitioned by an Al-plate to provide a double-layered enclosure. The top surface of enclosure is heated with varied heat flux with sine wave variation, the bottom surface is maintained at a low and constant temperature and the other vertical surfaces are thermally insulated. Two microencapsulated phase change materials made by paraffin with melting temperatures about TM = 28 degrees C and 37 degrees C, are selected. The hightemperature wall heat fluxes (qh) of 22.7sin(omega t)W/m2, 39.0sin(omega t)W/m2, and 61.3sin(omega t)W/m2 are considered. The low-temperature wall boundary conditions are set to 15 degrees C, 20 degrees C , and 25 degrees C. The results show that better net thermal energy storage is found for a case with a higher wall heat flux at the top surface. In addition, better thermal energy storage is noted when the MEPCM with low melting temperature is packed at the upper enclosure near the heated wall. Also, more energy storage is experienced for a double-layered enclosure with a higher partitioned ratio lambda. The melting point temperature of microcapsule phase change materials needs to be between high/low-temperature wall heating conditions to effectively store heat.

Contaminated wastewater is produced as an inevitable by-product in many industrial applications. This paper thoroughly investigates the toxicity and environmental burden of wastewater from the Fischer-Tropsch catalytic synthesis process. This significant by-product was analysed through the means of root growth inhibition of Sinapis alba, acute lethal effects on Eisenia andrei, chromatography on organic compounds, and determination of the content of metals. Two samples based on Co and CoMnK fixed bed catalytic synthesis using a standard range of parameters (250-280 degrees C; 1.5 MPa; 1145 h-1) were prepared. The results showed a severe toxicological effect in all investigated means. Significant Co, Mn, and K occurrences were detected in wastewater in amounts of 0.40-0.57, 0.83, and 0.13 mg & sdot;l-1, respectively. The root growth inhibition test proved that only 9.85 % solution is necessary to reach a 50 % growth inhibition in the case of Co synthesis. CoMnK synthesis presented slightly less toxic results-11.75 % solution for 50 % growth inhibition. Similarly, Wastewater Co with lethal concentration LC50 87.74 % was more toxic to earthworms than Wastewater CoMnK with LC50 95.99 %. The level of toxicity of this widely produced substance was surprisingly high. Rising environmental protection activity is expected to prove problematic for the current or future industrial applications, and a need to adopt treatment processes discussed in this study or upgrade the existing ones might be of the essence. The issue of wastewater toxicity and its environmental impact are discussed and evaluated hereby.

The main purpose of current study is to reduce the temperature gradient and pressure drop in the heat sinks by using a new double-layer mini/micro-channel stacked heat sink. In this numerical study, the conjugate heat dissipation characteristics of concurrent flow of pure water/water-based nano-emulsion through a mini- and micro-channel stacked double-layer heat sink is investigated. The potentials of pure water/phase change nanoemulsion in a mini- and micro-channel stacked double-layer heat sink for heat dissipation are compared with those for pure water in the single-layer microchannel heat sink. The effects of different parameters, such as flow rate ratio, total flow rate, heat flux, and concentration of the phase change nanoemulsion on the heating surface temperature suppression, pressure drop ratio, thermal resistance ratio, heating surface temperature uniformity index ratio, total heat transfer coefficient gain, and performance indexes are investigated. The three-dimensional velocity field in the channel is calculated by the pseudo-vorticity-velocity method, and the finite volume method is used to discrete the mathematical formulas. The numerical results showed that when the ratio of flow rate is 0.5, the total flow rate is 25.48 cm3/min, and the heat flux is 25 W/ cm2, the overall heat transfer coefficient of the mini- and micro-channel stacked double-layer heat sink with pure water/10 % mass fraction of phase change nanoemulsion as the coolants increases by 36.14 % compared with single-layer heat sink with pure water as the coolant. In addition, when the flow rate ratio is high and the total flow rate is low, the values of average and maximum thermal resistance ratios are greater than 1.

Sustainable freshwater supply due to the destructive effects of climate change has been a real challenge in today's world, especially in arid areas. Among the several approaches for tackling this challenge, the solar thermal desalination technique can be a promising solution as the potential of solar thermal energy for distillation in a decentralized approach. However, these systems have low water production performance and environmental issues due to their brine discharge into their surroundings. The current research proposes a solar humidificationdehumidification with a novel brine recirculation system for brine management purposes. For cost effectiveness, a stepped solar still was selected as an auxiliary system for brine concentration. The augmented solar still enhances the system's freshwater productivity and makes the system work as a minimal liquid discharge system. Then, the designed hybrid desalination system was experimentally tested to investigate its performance and brine concentration procedure. The findings recorded a maximum freshwater production of 1308 ml/day for the overall desalination system. The reduction of the inlet flow rate of the brine to the solar still enhanced the overall system's productivity by 347 ml/day. Moreover, the brine recirculation system's results show the brine concentration increase from 8690 to 9480 mu S/cm along the test. The cost of the distillation was determined as 0.16 $/l based on the cost analysis. Finally, it was shown that the utilization of a solar still for a brine recirculation system promisingly makes an efficient, decentralized, and environmentally friendly solar thermal desalination system.

Air conditioning systems are significant contributors to electricity consumption in building operations, making thermal management and energy efficiency critical. This study focuses on enhancing the thermal performance and heat transfer efficiency of the chilled water system by implementing smart control of variable frequency chillers during low-load operations. Monthly cumulative cooling capacities and chilled water circulation data were analyzed, selecting January, February, March, November, and December for nighttime low-load energy-saving experiments. Regression models for each chiller were established, using indices such as R2, P-value, YIF, and average error rate of Y-values to validate the performance equations. Results demonstrated a significant positive correlation between load rate and chiller efficiency. During the selected months, operational data revealed efficiency improvements and energy-saving cumulative effects. The findings indicate that employing the chilled water interconnection pipeline system for load transfer increases chiller load rates, enhancing overall system efficiency and reducing the total power consumption of auxiliary equipment. The total energy saved during the operation amounted to 607,738 kWh, leading to a reduction in carbon emissions of 257,681 kgCO2, equivalent to the annual carbon sequestration of Daan Forest Park (approximately 110.5-242.6 metric tons).

The present study aims to optimize the microwave vacuum drying (MVD) of pineapples using three analytical stages: One-way analysis of variance (ANOVA), the Taguchi method, and process adjustment. Key performance indicators, including drying curve, rehydration rate, color, texture, energy consumption, and sensory evaluation, were assessed to determine optimal settings. Initial ANOVA identified critical factors, including temperature control, microwave power density, vacuum pressure, and carousel speed. Higher microwave power densities significantly reduced drying time, and temperature control ensured product quality by balancing drying efficiency and minimizing overheating risks. Vacuum pressure contributed to enhanced moisture removal and improved color preservation, while turntable speed had a minimal impact on drying efficiency but ensured uniform temperature distribution. The Taguchi method further optimized these parameters, comprising a microwave power density of 7 W/g, a vacuum degree of 20 kPa, temperature control at 55 °C, and a turntable speed of 4 rpm. Subsequent process adjustments refined the settings to improve drying quality and stability, achieving comprehensive scores of 7.7 and 7.5 for the optimized configurations of 50°C with microwave power densities of 6 W/g and 7 W/g, respectively. This three-stage approach significantly improves MVD efficiency and product quality, offering practical insights for industrial-scale applications.