Efficient thermal management is essential for the performance and reliability of compact energy and electronic systems. This study proposes a self-powered thermoelectric generation-cooling (TEG-TEC) system that integrates a thermoelectric generator (TEG) with a thermoelectric cooler (TEC) to enhance heat dissipation using only available waste heat. The TEG converts part of the temperature difference into electrical power that directly drives the TEC, eliminating the need for external electricity. A Taguchi-based three-dimensional numerical model is employed to optimize four design factors: TEC thermoelectric couples, heat-transfer surface area, element length, and convection coefficient, each at four levels. An L16 orthogonal array requires only 16 simulations to explore the design space. Under the prescribed thermal boundary conditions, the optimized configuration achieves a maximum cooling capacity of 53.91 W, whereas a standalone TEG module delivers only 0.99 W. The internal power-amplification coefficient of performance, defined as COPP = QC,TEG/PTEG, reaches 50.38 for the best self-powered loop. In addition, the maximum thermal stress in the TEG module is reduced from 957.46 MPa in the standalone case to 936.69 MPa with the coupled TEG-TEC configuration, improving mechanical reliability. The Thomson effect reduces cooling performance by approximately 45%, underscoring the importance of incorporating Thomson heating into accurate TEG-TEC modeling. The proposed TEG-TEC configuration thus provides a compact, energy-efficient solution for low-load, space-constrained cooling while conceptually bridging waste-heat-driven power generation and active thermoelectric cooling.

There has been growing interest in geothermal energy and solar energy combined heating systems with seasonal thermal energy storage. However, the long-term operational stability of such coupled systems remains challenging due to dynamic ground temperature variation around borehole heat exchangers (BHEs). This study develops a co-simulation model to evaluate a solar-assisted medium-deep geothermal heating system, analyzing 20-year thermal performance across 1500-2500 m BHE burial depths using a Shaanxi residential case. The results show that the introduction of solar thermal recharging leads to a temperature increase of 26 degrees C in the shallow ground layer, while the temperature decrease observed in the deep layer is significantly affected by the burial depth of the BHE. Meanwhile, the annual heat extraction decay rates reduce from 14.89 % to 2.62 % (1500 m) and 11.57 %-2.01 % (2500 m), demonstrating enhanced sustainability. Through multi-objective optimization of three key parameters-solar collector area, thermal storage tank volume and BHE burial depth-the optimized configuration achieves synergistic benefits, including an 9.02 % reduction in total energy consumption and a 20.75 % decrease lifecycle cost compared to conventional designs. The presented method can serve as a reference for the rational design of the medium-deep geothermal energy and solar energy coupled heating systems.

Maintaining a precise microclimate is fundamental for the preventive conservation of cultural heritage housed in museum buildings. Conventional dehumidification systems for display and storage cabinets often compromise between energy efficiency, control stability, and the risk of damaging artifacts due to humidity fluctuations. To address this need, this study proposes an energy-efficient hollow fiber membrane-based dehumidification system for microclimate control in urban museum cabinets. Integrating multi-parameter modeling and genetic algorithm optimization, the developed system enables artifact-specific microclimate regulation aligned with urban conservation needs and preservation standards. Systematic simulations demonstrate the capability to maintain precise relative humidity within the range of 40-60 %RH. Response Surface Methodology analysis identifies three dominant control parameters: inlet humidity that primarily governs cabinet humidity, feed rate that determines dehumidification efficiency, and equivalent permeability coefficient that most strongly influences dehumidification capacity per unit membrane area. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) yields Pareto-optimal solutions, delineating an optimized domain with three demand-responsive regimes: (1) a gentle precision setting achieving 32.7 % efficiency at a flow rate of 0.0098 kg/s for vulnerable artifacts, (2) a balanced standard setting for everyday storage, and (3) a boosted high-capacity setting delivering 7.7 g/m2 & sdot;s moisture removal for seasonal demands. This research advances sustainable microclimate engineering for urban cultural facilities, offering a scalable, energy-conscious solution that integrates heritage conservation with sustainable city development.

Hollow fiber membrane-based evaporative cooling is regarded as an efficient, energy-saving, hygienic, and versatile cooling solution. However, most existing studies simplify the calculation of evaporative area, leading to inaccurate estimates of heat and mass transfer behavior. This study proposes a theoretical model for determining evaporative area, addressing the physical phenomenon at the micro level by incorporating membrane surface morphology and hydrophobicity. Fractal theory is employed to quantify the roughness of the membrane surface, and the apparent contact angle and evaporative area calculation models are established based on the Wenzel model. The proposed model is validated through membrane material characterization and experimental testing of membrane modules. In addition, the influence of membrane properties on the evaporative area during the membrane-based evaporative cooling process is investigated. The results show that the theoretical model for evaporative area matches experimental data with a relative error of less than 5 %. Membranes with higher surface fractal dimension, intrinsic contact angle, and porosity are found to theoretically increase the evaporative area beyond the total membrane area, thereby enhancing heat and mass transfer performance. The developed evaporative area calculation method can be applied to numerical modeling of membrane-based evaporative cooling processes, enabling more accurate predictions of system behavior and providing theoretical guidance for membrane material selection and optimization.

Against the backdrop of China’s rapidly aging population, nursing homes face the dual challenge of ensuring thermal comfort for elderly adults while maintaining high energy efficiency. However, systematic investigations into how elderly-specific thermal preferences affect the load characteristics and long-term performance of ground-source heat pump systems remain limited. To address this gap, this study develops a framework that integrates dynamic building load modeling with transient ground-source heat pump system analysis, enabling a quantitative assessment of how elderly-oriented temperature settings influence the coupled behavior of building loads, system operation, and ground temperature evolution. A spatiotemporal zoning operation strategy tailored for nursing homes is also proposed. The results show that elderly adults generally prefer indoor temperatures approximately 2 °C higher than standard setpoints, which substantially increases winter heating demand, raising the annual heating load and total energy consumption by 55.4% and 30.6%, respectively. The spatiotemporal zoning operation strategy effectively reduces energy consumption by about 15% and enhances operational stability. Ten-year dynamic simulations further reveal that elderly-oriented parameters accelerate ground thermal depletion, whereas the proposed strategy significantly alleviates subsurface thermal imbalance. These findings provide important theoretical and technical guidance for the efficient design and long-term sustainable operation of ground-source heat pump systems in nursing homes.

Air-source heat pump (ASHP) technology holds promise for reducing energy consumption and carbon emissions in public building heating systems. However, its widespread application is hindered by inconsistent performance across climates, coupled with the absence of comprehensive models that effectively integrate control strategy with accurate long-term performance predictions. This study investigates the adaptability of ASHP systems across different climate zones in China, with a focus on energy efficiency and carbon reduction potential. Using dynamically calibrated TRNSYS models that account for external temperature variations and frost formation, the monthly and daily operational performance of ASHP systems was evaluated in several representative public building types. Results indicate that the COP is positively correlated with outdoor temperature and significantly influenced by the supply water temperature, with the highest annual COP observed in Beijing (2.7), followed by Shanghai (2.62) and Harbin (2.58). Notably, little correlation is found between the COP and building types. Furthermore, a comparative carbon emission analysis demonstrates that ASHP systems achieve substantial reductions compared to conventional coal and gas heating systems. Specifically, carbon reduction rates reached approximately 47 % in cold regions and 28 % in severe cold regions relative to coal-fired boilers. Additionally, ASHP systems exhibited superior carbon reduction performance over gas boilers when outdoor temperatures exceeded region-specific thresholds, such as -1 degrees C in Harbin, -5 degrees C in Beijing, and 4 degrees C in Shanghai. These findings underscore the effectiveness of ASHPs as a sustainable heating solution for public buildings in diverse climates, providing valuable insights for optimizing design and operation toward carbon neutrality goals.

Efficient water vapor removal is important for both building humidity control and industrial gas dehydration, where operating conditions may span broader temperature and pressure ranges. Driven by a pressure gradient, membrane-based dehumidification has emerged as an energy-efficient alternative, employing polymeric composite membrane materials to achieve effective moisture separation. However, traditional development of such membranes remains heavily reliant on inefficient trial-and-error approaches. To overcome this limitation, this study employs machine learning to directly predict the relationships between physicochemical structure, operational conditions, and water vapor permeation performance of composite membrane materials. A dataset comprising 138 experimental samples from 26 published studies was compiled, featuring five input features: selective layer thickness, operating temperature, feed pressure, relative humidity, and a newly proposed hydrophilicity score based on functional group composition. Among six machine learning models evaluated, the Gradient Boosting Decision Tree (GBDT) achieved superior predictive performance, yielding a test R2 of 0.912. SHAP analysis identified selective layer thickness as the dominant descriptor, followed by feed pressure, hydrophilicity score, operating temperature, and relative humidity, contributing 34.4%, 26.5%, 15.8%, 11.9%, and 11.5% to the model predictions, respectively. Within the investigated parameter space, a genetic algorithm integrated with the GBDT model identified a permeability-oriented parameter combination (18.25 μm thickness, 111.43 °C, 0.94 bar, 52.02%RH, and a hydrophilicity score of 5), achieving a predicted permeability of 136,418 Barrer. The framework offers a transferable strategy for accelerating the rational design of advanced membrane materials, significantly reducing the need for exhaustive experimental screening.

The efficient utilization of deep geothermal energy is crucial for sustainable building heating and decarbonization. Accurately simulating deep-buried ground heat exchangers (DBGHE) remains challenging due to complex geological structures. In this context, the numerical assessment of heat extraction from DBGHE has gained significant attention. Unlike conventional methods that simplify heat transfer models for computational expediency, this study introduces an innovative approach based on Model Order Reduction (MOR) techniques. The proposed method dissects the extensive computational domain of DBGHE into multiple full-order systems. By leveraging the Krylov subspace MOR technique, it establishes and solves reduced-order systems for each full-order counterpart. Notably, the application of MOR method demonstrates intriguing behavior, wherein deviations across various reduced-order systems converge to a constant value over time (i.e., time domain) during DBGHE operation. Consequently, different order reductions in the time domain are developed. Particularly, a large-reduced order is adopted in the initial stage of system operation, followed by a small-reduced order in the subsequent stages. The method yields impressive results, predicting outlet temperature with an average relative error of approximately 0.54% and reducing time consumption by 53% compared to ANSYS Fluent software. The relative errors of outlet temperatures between the MOR method and field data are less than 7.3%. The findings suggest that the developed method can effectively manage large-scale, complex geological structures within extensive rock-soil domains, which is a challenge to achieve with conventional simplified approaches. This method is anticipated to offer innovative solutions for analogous physics or engineering problems.

Enhancing membrane-based dehumidification requires innovative module designs that transcend the masstransfer and flow-resistance limitations of conventional uniform packing. Motivated by prior gradient studies on materials or channels, this study proposes a structurally graded hollow fiber module using a three-layer staggered bundle with progressively varied fiber diameter and spacing. By adapting fiber geometry to local flow and mass transfer, the graded design synergistically optimizes dehumidification and pressure drop. A comprehensive multi-physics model implemented in COMSOL Multiphysics is developed to resolve the coupled transport processes within the graded configuration. Simulation results demonstrate that the optimal graded structure achieves a 28% higher weighted average quantity (epsilon = 0.427 vs. conventional single-stage baseline epsilon = 0.333), which equally integrates normalized dehumidification capacity, pressure loss, and spatial efficiency, thereby confirming its superior balance between performance and energy consumption. The staged fiber arrangement renews the concentration boundary layer at each stage interface, counteracting the progressive thickening typical of uniform modules. Furthermore, decoupling analysis confirms that the synergistic variation of fiber diameter and spacing critically enhances shell-side hydrodynamic behavior, promoting efficient moisture removal under reduced flow resistance. Two distinct optimization pathways are identified: the Mass Transfer Intensification pathway, which elevates the specific moisture removal rate to 428.0 g/(m2 & sdot;h), and the Flow Resistance Optimization pathway, which reduces pressure drop by 51% while maintaining dehumidification capacity. This work establishes that effective performance enhancement requires deliberate flow channel engineering beyond simple bundle rearrangement, offering valuable insights for the design of energy-efficient humidity control systems in cabin environments and other demanding applications.

The transition toward sustainable building heating is vital for achieving carbon neutrality. Medium-deep borehole heat exchanger (BHE) systems provide a promising geothermal solution, but large-scale planning is hindered by the high computational demand of traditional simulations for performance prediction across diverse geologies. This study integrates machine learning (ML) algorithms with a validated finite-volume model to develop an efficient framework for evaluating the long-term thermal performance and potential of BHEs. Focusing on five major Chinese cities (Hebei, Tianjin, Shandong, Henan, and Shaanxi), the framework analyzes the impact of key geological and operating parameters (depth: 2000-3000 m; flow rate: 20-40 m3/h; inlet temperature: 5-20 degrees C). Among three ML algorithms-Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Quantized Conjugate Gradient (QCG)-the LM algorithm achieved superior accuracy (MSE = 3.0261, R = 0.99965) and robustness against overfitting. Regional analysis highlights the crucial influence of local geology. Henan exhibits the highest heat extraction (235.5 kW) with moderate 10-year decay (5.0 %), while Shaanxi shows the steepest decline. Economically, geothermal deployment can reduce heating costs by 60-95 % and COQ emissions by 73-89 % compared to conventional coal systems. This ML-driven framework provides rapid, data-informed decisionmaking for low-carbon heating investment and geothermal integration in sustainable development.

The unique operational environment of AI-driven data centers exacerbates high energy consumption, a large portion of which is attributed to electronic cooling systems. Developing energy-efficient thermal management solutions is critical for establishing green data centers. Thermosyphons have emerged as highly effective passive cooling devices, offering a promising approach. However, the electronic miniaturization leads to the development of compact thermosyphons, influencing their thermal performance. Regarding structural design constraints, surface modification presents a viable optimization strategy. This study experimentally investigated the boiling heat transfer enhancement in a 10 mm-height compact thermosyphon featuring modified surfaces. Wettability hybrid and gradient surfaces (hydrophobic 120 degrees- hydrophilic 30 degrees/75 degrees) were fabricated using an ultraviolet laser etching technique. The effects of surface modification on bubble dynamics were explored. The thermal performance enhancement was evaluated by the heat transfer coefficient, overall thermal resistance and dimensionless parameters. The experimental results illustrate that, compared to smooth surface case, the 120 degrees- 75 degrees modified surfaces exhibit limited thermal performance improvement due to bubble expansion effects. Both 120 degrees-30 degrees modified surfaces featured larger contact angle difference demonstrate superior heat transfer in the compact thermosyphon. Specifically, the 120 degrees-30 degrees wettability hybrid surface promotes bubble nucleation and prevents bubble expansion on the condensation surface, while the 120 degrees-30 degrees wettability gradient surface enhances liquid replenishment and bubble detachment. Thermal performance analysis shows that the 120 degrees-30 degrees modified surfaces achieve maximum heat transfer enhancements of 2.96 and 3.29 times that of the smooth surface. This research aims to provide design guidance for optimization of integrated heat sinks in electronic cooling fields.

Precision humidity control is vital for lithium battery and semiconductor production, where conventional dehumidification is energy-intensive. Vacuum-driven membrane dehumidification offers an energy-efficient alternative but faces performance limitations due to uniform flow in conventional straight-fiber modules. This study innovatively proposes a sinusoidal hollow fiber membrane structure and systematically explores its internal moist air flow dynamics and mass transfer enhancement through rigorous three-dimensional numerical simulations. Four distinct periodic configurations (single- to quadruple-sinusoidal fibers) were evaluated for hydrodynamic and dehumidification performance. An orthogonal design (L27(35)) coupled with empirical modeling assessed the sensitivity of key geometric parameters (amplitude, wavelength, and phase). Results reveal that the multi-sinusoidal configurations generate periodic flow constrictions and secondary flows, disrupting concentration boundary layers and increasing mass transfer efficiency. The multi-sinusoidal configurations (double, triple, and quadruple) show progressively higher capacities than the straight fiber (205 g & sdot;kga- 1m- 2),& sdot; reaching 207.5 g & sdot;kga- 1 & sdot;m- 2 for the quadruple design. The quadruple-sinusoidal structure achieves an exceptional performance balance with the specific dehumidification gain of 17.29 g & sdot;kga- 1 & sdot;m- 2 at only a 59.8% pressure drop penalty. Parametric analysis demonstrates that the amplitude of the primary fiber (A1) dominates overall performance. Secondary amplitude ratio (A2/A1) shows inverse effects on pressure droop (Delta p) and dehumidification capacity per unit pressure drop (Delta d/Delta p). Wavelength and phase parameters exhibit negligible influence. Relative to the straight fiber, the optimal quadruple-sinusoidal design reduces pressure drop by 49.7% and increases dehumidification capacity per unit pressure drop by 97.8%. This study provides a rational design framework for sinusoidal membrane modules and practical guidance for high-performance dehumidification systems.

Ambient particles infiltrate indoor environments through cracks in the building envelope, deteriorating indoor air quality and posing risks to human health. This study investigates the factors influencing particle infiltration in controlled laboratory settings, utilizing an orthogonal experimental design. The effects of outdoor particle concentration (Cout), crack structure factor (q), indoor temperature (Tin), and indoor relative humidity (RHin) on indoor particle concentration (Cin) and infiltration factor (Finf) are quantified using multivariate regression analysis. Particle mass and number concentrations were measured in sixteen experimental scenarios to characterize infiltration behavior and particle size distribution. Results demonstrate that Cin increases predominantly with Cout, such that a 1 mu g m-3 increase in Cout leads to a 0.382 mu g m-3 increase in Cin, assuming all other factors remain constant. Regression modeling identifies optimal conditions for Cin at Cout = 50 mu g m-3, RHin = 30 %, and U-shaped cracks. Meanwhile, Finf rises with q and RHin but decreases with increasing Tin, achieving its minimum at RHin = 30 %, Tin = 30 degrees C, and Ushaped cracks. Size-resolved analysis shows infiltration peaks for 0.4-0.5 mu m particles at RHin = 90 %, suggesting that particles smaller than 0.5 mu m undergo hygroscopic growth and coagulate into this range. By incorporating temperature and humidity variables, this study establishes predictive models for particle infiltration, providing actionable strategies for optimizing indoor air quality management while balancing energy efficiency. These findings advance the understanding of particle infiltration in buildings and inform the formulation of targeted design codes.

The increasingly severe energy crisis and associated environmental issues pose new challenges for the efficient and rational utilization of renewable energy. The solar-assisted ground-source heat pump (SAGSHP) system is a novel heating system that effectively combines the advantages of both solar and geothermal energy. In this study, an SAGSHP system was established through TRNSYS simulation software to provide winter heating and year-round domestic hot water for a residential building. By varying the area of solar collectors (A) and the number (n) and the depth (H) of the borehole heat exchangers (BHEs), the system operational performance, including the system energy consumption, ground temperature attenuation, and heat pump efficiency, was investigated. A comparison with a single ground-source heat pump (GSHP) system was also conducted. After 20 years of operation, the parameter optimization resulted in a reduction of approximately 60 MWh and 70 MWh in system energy consumption, equivalent to saving 7.37 t and 8.60 t of standard coal, respectively. At the same time, the total costs over 20 years can be reduced by 48.20% and 33.77%, respectively. The proposed design method and simulation results can serve as the reference for designing and analyzing the performance of the SAGSHP system.

Geothermal energy is one of the most competitive renewable energies. For large-scale projects, multiple medium- deep geothermal heat exchangers (MGHEs) with depths of 2000-3000 m are often used to satisfy the building heating. Limited by occupied area, thermal influencing radius is an important parameter reflecting the MGHE influencing range in surrounding strata. This paper presented a novel investigation on the impact of inclination on MGHE thermal performance and influencing range. A finite volume method (FVM) based algorithm coded by MATLAB was established and validated by 28 days' project operating data, before it was used to study the influencing range affected by the depth of kick-off point (H), angle of inclination (alpha) and working fluid flow rate. It was concluded that for a conventional vertical MGHE with a 2500 m burial depth, the maximum influencing radius increases from 8.06 m to 31.84 m after 10 years of operation. For the inclined MGHE with the same length, increasing alpha decreases the maximum thermal influencing radius position (TIRP). The relationships among heat extraction rate, TIRP and MGHE parameters were established through non-linear regression. When alpha increases from 0 degrees to 10 degrees, TIRP can be reduced to a minimum value of 5.3 m when H = 500 m. A smaller H conduces to reducing TIRP without affecting the thermal performance. The proposed methods and results will be conducive to the MGHE array design and efficient use of geothermal energy.

Geothermal energy, a form of renewable energy, has been extensively utilized for building heating. However, there is a lack of detailed comparative studies on the use of shallow and medium-deep geothermal energy in building energy systems, which are essential for decision-making. Therefore, this paper presents a comparative study of the performance and economic analysis of shallow and medium-deep borehole heat exchanger heating systems. Based on the geological parameters of Xi’an, China and commonly used borehole heat exchanger structures, numerical simulation methods are employed to analyze performance and economic efficiency. The results indicate that increasing the spacing between shallow borehole heat exchangers can effectively reduce thermal interference between the pipes and improve heat extraction performance. As the flow rate increases, the outlet water temperature ranges from 279.3 to 279.7 K, with heat extraction power varying between 595 and 609 W. For medium-deep borehole heat exchangers, performance predictions show that a higher flow rate results in greater heat extraction power. However, when the flow rate exceeds 30 m3/h, further increases in flow rate have only a minor effect on enhancing heat extraction power. Additionally, the economic analysis reveals that the payback period for shallow geothermal heating systems ranges from 10 to 11 years, while for medium-deep geothermal heating systems, it varies more widely from 3 to 25 years. Therefore, the payback period for medium-deep geothermal heating systems is more significantly influenced by operational and installation parameters, and optimizing these parameters can considerably shorten the payback period. The results of this study are expected to provide valuable insights into the efficient and cost-effective utilization of geothermal energy for building heating.

This study optimizes a segmented thermoelectric generator (STEG) under a 400 K temperature difference. Hotside materials consider different doping amounts of indium (In). STEGs with different leg lengths and crosssection areas are explored for the first part of the study. It shows that the output power of the STEG with a leg length of 3 mm and a cross-section area of 4 mm x 4 mm is 84.28 % higher than that with a leg length of 6 mm and a cross-sectional area of 2 mm x 2 mm, but the conversion efficiency becomes 28.77 % lower. There have been no studies on segmented thermoelectric generators (STEGs) doped with different amounts of p-type and n-type thermoelectric materials, especially to analyze their performance through numerical predictions. The second part uses a multi-objective genetic algorithm (MOGA) for optimization analysis. The results show that the STEG using undoped p-type and 3 % n-type doping produces the best output power (1.337 W) and the highest conversion efficiency (18.71 %). Compared with the non-optimized STEG, the output power and efficiency of the optimized STEG are increased by 14.53 % and 32.49 %, respectively. Central composite design (CCD) is used for artificial neural network (ANN) model architecture, and ANN is used for STEG prediction and optimization. The calculation time of ANN is 1820 times less than MOGA, and the error is about 3-8% smaller, although the optimized value is 5-8% smaller.

Enhancing boiling heat transfer of the confined vapor chamber through surface modification is an essential way to address the thermal management of integrated electronic devices. This work conducted a boiling heat transfer experiment on the confined vapor chamber with conical and smooth surfaces. Deionized water was used as the working fluid, and the operating pressure in the confined vapor chamber was 5 kPa. The characteristics of bubble nucleation and liquid rewetting on the conical surface were investigated by a visualization method. The thermal performance of the confined vapor chamber with conical surface at different heat fluxes and filling ratios was compared with the smooth surface. Experimental results show that the bubble behaviours in the confined vapor chamber with conical surface are different from those on the smooth surface. The surface cavity at the bottom of the conical structure entraps the bubble and reduces the nucleation energy barrier, promoting an increase of 3.5 times in nucleation site density. At the heat flux of 20 W.cm(-2), the bubble growth rate on the conical surface is 23.2% higher than that on the smooth surface. At the bubble departure period, the combination of liquid gravity and bubble-bubble interaction on the conical surface improves the average bubble departure rate and reduces the dry spot duration. The confined vapor chamber with conical surface exhibits an improvement of 1.4 times in the heat transfer coefficient compared to the confined vapor chamber with smooth surface. This research aims to provide suggestions for the enhancement of boiling heat transfer in confined vapor chambers.

The rapid expansion of wearable and portable electronics has intensified the search for compact, sustainable energy sources capable of continuous operation without frequent recharging. Flexible thermoelectric generators (FTEGs), which convert low-grade heat into electricity via the Seebeck effect, offer a promising solution but are often constrained by the limited thermal conductivity of surface materials and mechanical robustness. This work develops an FTEG incorporating graphene, carbon nanotubes, and carbon black into dual-elastomer encapsulation, Ecoflex as the inner layer for compliance, and PDMS as the outer shell for structural stability. Graphene integration delivers the highest thermal conductivity (4.24-5.63 W m-1 K-1) and markedly enhanced tensile strength. Under a 75 degrees C temperature gradient, the optimized device generates 0.021 W, surpassing the unfilled PDMS control by over three orders of magnitude. Finite element simulations, with a 2.38% deviation from experimental results, confirm uniform heat flow and current density. Substitution of Bi2Te3 with a highperformance MgAgSb/Mg3.2Bi1.5Sb0.5 pair in simulations increased output power by approximately 60%, demonstrating strong potential for next-generation devices. An artificial neural network (ANN) model trained on 189 datasets achieved excellent predictive performance of R2 = 0.9978 and mean error = 1.68%, enabling rapid design evaluation. Life cycle assessment identifies Bi2Te3 and energy-intensive processing as the main contributors to a carbon footprint of 5.57 kg CO2-eq, approximately 89% lower than conventional rigid TEGs. The results demonstrate a combined materials-modeling-sustainability framework that advances FTEGs for wearable electronics, IoT devices, and low-grade heat recovery applications.

Membrane dehumidification technology has gained significant attention for its efficiency, energy savings, and simplicity. Enhancing the performance of membrane dehumidification is crucial as it directly impacts energy efficiency and indoor comfort, promoting wider adoption of this innovative approach. Significant advances have been made in enhancing membrane dehumidification performance from the perspectives of materials, modules, and systems. This review delves into recent developments, focusing on enhancement methods, dehumidification effects, and limitations. Innovations in membrane materials, such as the use of nanoparticles and hydrophilic functional groups, improve permeability, selectivity, and durability. Moreover, novel module designs, like porous or spiral-wound configurations, increase the surface area and optimize flow dynamics, thereby boosting the dehumidification efficiency. Connecting multiple modules in series or parallel enhances performance but introduces manufacturing complexities, higher flow resistance, and fouling risks. At the system level, integrating membranes with heat recovery or renewable energy systems can reduce energy consumption by over 20 % compared to traditional methods. In this review, the optimization recommendations for membrane materials, modules, and systems were proposed. Combining molecular-scale modeling with experimental testing provides a precise path for upgrading membrane properties. The mass transfer characteristics within modules, along with multi-objective optimization, support a more efficient and rational design of the membrane module. Additionally, the exergy analysis can identify energy-intensive areas, refining the system design strategies for greater efficiency.