Although methane (CH 4 ) predominantly exists in low concentration, it contributes significantly to global warming and just second to CO 2 . Its inert characteristics present both a pressing need and a significant challenge for mitigation strategies, including thermal catalytic oxidation. To address this challenge, we synthesized low cost mordenite zeolite (MOR) with highly dispersed Cu–Pd (Pd: 0.10 wt%) as efficient thermo‐catalyst to oxidize low‐concentration CH 4 (200 ppm). The Cu–Pd–MOR catalyst achieves 90% CH 4 conversion at 358 °C and exhibits improved durability over 100 h of continuous operation. Incorporating Cu enables reduction in Pd loading while maintaining high activity, demonstrating a synergistic Cu–Pd interaction that enhances metal dispersion and thermal stability. Compared with Pd‐only MOR catalysts, the Cu–Pd system delivers advanced low‐temperature activity and long‐term stability, offering a cost‐effective route for methane removal.

The study of water resistance of methane photo-oxidation catalysts is important and challenging for the removal of methane from the atmosphere. Herein, we investigate the mechanistic role of water in the photocatalytic oxidation of trace methane on ZnO decorated with various metal (Cu, Pt). We found that suppressing water dissociation at active sites mitigates hydroxyl-induced catalyst poisoning. Critical insight reveals that photogenerated holes (h+) mediate the conversion of passivating hydroxyl groups (OH*) into reactive hydroxyl radicals (center dot OH), simultaneously liberating active sites and promoting the reaction. In addition, temperaturedependent experiments revealed that increasing the temperature from 20 to 80 degrees C enhanced the methane conversion by approximately 4-fold, proving that partial desorption of water molecules from the catalyst surface releases active sites. The experimental results show that 0.5 % Pt/ZnO is about 2-fold more water resistant than 0.5 % Cu/ZnO. These findings provide valuable experience and guidance for both mechanistic understanding of water-mediated methane photooxidation and rational design of high-performance catalysts.

The urban heat island effect is a widespread phenomenon in major cities worldwide, exerting significant impacts on urban environments, human health, and energy consumption, thus attracting extensive research interest. The manifestation of the urban heat island effect varies across different urban forms. This study employs a three-dimensional porous media model to comprehensively analyze the urban heat island effect and the influence of wind speed on three typical urban layouts: single-center, multi-center, and grid.The results indicate: (1) The more dispersed the city center, the weaker the urban heat island intensity, with differences in peak heat island intensity of up to 0.3 K among different urban forms. (2) Under favorable environmental wind conditions, higher wind speeds lead to weaker heat island intensity. At v = 5 m/s, the grid-based heat island intensity decreases by 0.57 K. (3) With increasing building density, the urban heat island effect intensifies in all three types of cities, with the single-center city exhibiting the largest increase in heat island intensity, up to 23.5 %

To mitigate the rising concentration of atmospheric methane and its contribution to the intensification of the greenhouse effect, this study investigated the impact of fluid dynamics on the photocatalytic degradation of atmospheric methane in a microchannel reactor, aiming to optimize the flow regime for improved reaction performance. A two-dimensional microreactor model was developed using fractal theory to represent the porous media, and the lattice Boltzmann method (LBM) was employed to simulate fluid flow, heat transfer, and mass transfer. It was shown that disordered barriers enhanced fluid flow uniformity and improved interaction with photocatalyst, thereby boosting chemical reaction efficiency. Regarding the inlet flow, decreasing the flow rate from 1500 to 500 mL/min increased methane catalytic efficiency by 47.44%. A square barrier layout maximized fluid-photocatalyst contact, enhancing methane conversion efficiency by 12.86% and 26.6% compared to the circular and diamond layouts, respectively. This work provides a theoretical foundation and design insights for optimizing microchannel reactors to improve methane degradation and reduce greenhouse gas emissions.

Solar chimney power plant coupled with solar pond (SCPP-SP) was proposed as a novel concept for the solar-driven removal of methane from the atmosphere. However, its performance is sensitive to ambient conditions. This study developed a comprehensive numerical model to evaluate the SCPP-SP under open-environment conditions. The effects of ambient crosswind (ACW), relative humidity (RH), turbine pressure drop, and ground heat flux (GHF) on methane removal rate and carbon reduction performance were systematically investigated. The results indicated that methane depletion was dominated by oxidation pathways involving hydroxyl radicals (•OH) and chlorine atoms (Cl•). The generation and distribution of these reactive species were highly sensitive to the external environment. At an ACW of 15 m/s and a GHF of 600 W/m2, only 22.33% of ozone was converted into •OH, whereas high Cl• concentrations were confined to the region above the SP. ACW and RH exerted much stronger effects on methane removal rate than the turbine pressure drop. The methane removal rate varied non-monotonically with ACW, reaching a maximum of 2.31 kg/h at 10 m/s. With RH increasing from 36% to 76%, the methane removal rate increased from 1.26 to 2.31 kg/h. The peak carbon reduction benefit was 193.43 kg/h at ACW = 10 m/s with a turbine pressure drop of 40 Pa. This study clarified the coupled transport-reaction mechanism of SCPP-SP in open space and provided a quantitative basis for its design and climate-mitigation application.

With the increasing integration of renewable energy into regional power grids, significant spatial differences in carbon intensity have emerged. These differences highlight the need for carbon-aware workload allocation in geographically distributed Internet Data Centers, where aligning computational loads with low-carbon regions can enhance both environmental and economic outcomes. In this paper, we propose a two-stage optimization framework that integrates renewable-aware workload allocation and strategic carbon allowance procurement. In the first stage, a robust optimization model based on column-and-constraint generation is developed to manage uncertainties in workload demand and carbon prices, enabling stable and cost-effective workload distribution across regions with varying renewable energy penetration. In the second stage, a multi-class mean field game model is constructed to capture strategic interactions and behavioral heterogeneity among Internet Data Centers in carbon markets. We apply a Deep Galerkin Method to solve the resulting high-dimensional partial differential equations, yielding a robust and convergent procurement strategy. Simulation results demonstrate that the proposed framework achieves over 28% cost savings while ensuring carbon compliance and workload satisfaction. This study offers theoretical and practical insights for carbon-regulated Internet Data Center operations, and supports the broader integration of renewable energy in large-scale digital infrastructure.

With the rapid expansion of urban areas, short naturally ventilated traffic tunnels (NVTTs) have become prevalent in modern cities. However, their enclosed design and inadequate ventilation often lead to the accumulation of vehicle emissions, especially during peak traffic periods, which poses significant threats to public health. Previous studies have shown that airflow in such tunnels is caused by ambient crosswinds (ACWs), which contribute to the dilution of pollutants. Based on this, a geometrical model including traffic tunnels belonging to a complex traffic system of the Second Ring Road in Wuhan City was established, followed by a mathematical model describing the fluid flow and pollutant transformation. The current flow characters and pollutant dispersion mechanism of CO and NOX were analyzed. Among them, the number and speeds of vehicles are measured to calculate the strength of the pollutant source. Then, the data was set as the initial contaminant source strength in Ansys Fluent 14.0 to compute the pollutant dispersion of the whole domain. The results indicate the following: (1) The airflow direction inside the tunnel varies with changes in ambient wind direction and wind speed. Specifically, variations in ambient wind direction result in changes in airflow direction in both tunnels. In contrast, changes in wind speed do not affect the airflow direction in both tunnels; only in the downstream tunnel does the airflow direction change with increasing westward wind speed. By comparison, in the upstream tunnel, the airflow direction remains unchanged regardless of the westward wind speed; (2) Pollutant accumulates along the downstream airflow in both the tunnels; (3) The mass fraction level of contaminate stratification differs along the tunnels. The pollutant tends to form y-component layering near the upwind opening and x-component stratification at the downwind opening of the two tunnels.

Droplet self-drive achieves directional movement of droplets without external forces, but conventional mechanisms underperform in complex environments. In this study, the combination of the structural design and surface wettability optimization is proposed to achieve self-driven of water droplets. The effects of contact and cone angles on Laplace forces were investigated. The variation patterns of wettability gradient forces across distinct wettability domains were systematically examined. It was observed that the droplets could obtain a greater driving force in the hydrophilic domain when driven by the Laplace force only, and the optimized wedge-structure had the highest average droplet velocity at a wetting wall contact angle of 30 degrees, which could reach 174.0 mm/s. When driven by wettability gradient forces alone, droplet velocities increase with the gradient. The maximum average velocity of 117.28 mm/s occurs in the hydrophilic domain with a gradient of 4 degrees/mm in this study. This synergistic approach significantly enhances droplet velocity, reaching up to 2.87 times the speed under a 1 degrees/mm wettability gradient compared to using wettability gradient forces alone. The findings offer new directions for designing structures and surface wettability, facilitating improved droplet self-driven and efficient solutions for applications in microfluidic devices, biomedical detection, and enhanced heat transfer.

The photocatalytic removal of atmospheric methane significantly reduces the climate risk associated with global warming. However, the development of the photocatalysts has been limited by the stability of the methane molecule and the low photon absorption rate. In the paper, field synergy theory of mass transfer was adopted to guide the structural design of the photocatalytic reactor, providing an alternative approach to enhancing methane oxidation performance. A photothermal-flow-reaction mathematical model coupling fluid flow, heat transfer, mass transfer, and reaction kinetics was developed and validated against experimental data. Experiments were conducted in a reactor with inlet methane concentrations of 100 ppm and flow rates of 50 mL/min under Xe lamp irradiation (580-1380 W/m2). The results indicate that the thermal effect induced by the Xe lamp disrupted the mass-transfer boundary layer, improving the field synergy number and enhancing methane photocatalytic efficiency. Reducing the inlet methane concentration and flow rate enhanced the synergy between the velocity and concentration fields within the reactor. The photocatalytic efficiency and field synergy number were 74.37 % and -0.0356 respectively, at a flow rate of 10 mL/min, an inlet concentration of 2 ppm, and a light intensity of 1000 W/m2. Fins with a height of 0.2H were installed in the reactor to economically reduce mass transfer resistance and enhance photocatalytic performance. This paper verified the applicability of the field synergy theory in the structural design of photocatalytic reactor and offered new perspective for enhancing photocatalytic methane performance.

Building envelopes play a crucial role in regulating energy loads and maintaining indoor thermal comfort. Phase Change Materials (PCMs) offer latent heat storage benefits but face limitations, including a narrow temperature regulation range and a risk of overheating under intense solar radiation. To address these challenges, this study proposes a novel thermal diode double phase change wall (TDPW) system integrating electrochromic glazing and dual PCMs for dynamic thermal regulation. This study employed a combination of experimental measurements and numerical simulations. During winter, indoor temperatures increased by up to 4.7 degrees C (4.7 K), and in spring, they exceeded ambient temperatures by 7.4 degrees C (7.4 K). The maximum indoor-outdoor temperature difference reached 44.4 degrees C (44.4 K) in regions with high solar radiation and large diurnal temperature variations. In northern China regions, the TDPW reduced discomfort time by up to 13.46 %. Overall annual energy load decreased significantly, particularly in northern China regions, where reductions reached 21.72 MJ/m3 (6033.33 Wh/m3) with a maximum savings rate of 16.62 %. This study highlights the potential of the TDPW system to enhance energy efficiency and indoor comfort. Its integration offers a promising solution for sustainable building design, particularly in regions with extreme temperature fluctuations.

A cross-scale LBM-FVM method (CLFM) is developed to model the methane photocatalytic removal. At the macroscopic level, the Finite Volume Method (FVM) is employed to simulate the flow, heat, and mass transfer fields, while the Lattice Boltzmann Method (LBM) is applied at the pore scale to capture detailed surface reactions on the catalyst units. A bridging strategy is introduced to ensure accurate and efficient data exchange between the two scales. Validation results demonstrated that CLFM reproduced velocity and temperature fields for two-dimensional channel and Poiseuille flows with high consistency compared with standalone FVM and LBM. Using a 2:1 coarse-to-fine grid ratio, the method maintained high accuracy while significantly improving computational efficiency. Among the 70 catalyst units examined, ordered distributions outperformed random and staggered distributions, with the pentagonal posts (D58) exhibiting the best overall performance. Specifically, D58 achieved a photocatalytic efficiency of 20.42%, a saving to investment ratio of 3.0, and a methane purification rate of 2.35 & times; 10-10 g/s. These findings demonstrate the capability of CLFM to combine accuracy and efficiency in multiphysics chemical simulations, offering a robust framework for catalyst optimization and performance enhancement in methane photocatalytic removal systems.

Self-propelled droplet jumping has widespread applications in surface cleaning, condensation heat transfer, hydrogen production, and triboelectric nanogenerator due to the passive yet effective cross-interface transfer of mass, momentum, energy and charge, whose rates generally increase with droplet size. However, as droplet size increases, gravity inevitably impedes droplet's mobility, imposing a capillary length constraint of 2.7 mm for water droplet, beyond which self-propelled jumping remains a persistent challenge. Here, we report passive jumping of water puddle in the unprecedented centimeter scale from a superhydrophobic surface through the burst of an entrained bubble, breaking the capillary length limitation for droplet jumping. By virtue of direct and localized impact at droplet base, the bubble-burst-induced capillary waves play a paradigm-shifting role in shortening the impact duration, depressing droplet spreading, and facilitating momentum transfer. With >90% conversion to droplet jumping momentum, the impacting momentum of capillary waves scales linearly while droplet jumping height scales quadratically with bubble radius. Through studying the synergistic interplay between bubble bursting, fluidic jetting and droplet jumping, this work reveals a previously unexplored mechanism of capillary wave impact in fluid-structure interactions and offers a promising strategy for droplet actuations and the directional printing of particles in additive manufacturing.

Highway service-area buildings are characterized by long operating hours, diverse functional spaces, and considerable energy consumption, resulting in significant life-cycle carbon emissions. This study quantifies life-cycle carbon emissions of the buildings in highway service areas. A life-cycle accounting framework was established, and net emissions were further evaluated by considering the contributions of photovoltaic (PV) electricity and vegetation carbon sinks. Five representative service areas covering hot-summer/cold-winter, severe-cold, cold, temperate, and hot-summer/warm-winter zones were investigated through field surveys and indoor thermal environment measurements to obtain envelope properties, equipment configurations, and operating profiles. Results revealed that life-cycle carbon emissions vary substantially across climatic regions, ranging from 4.31 & times; 103 to 3.06 & times; 104 tCO2e. The operational stage accounts for the largest share of total emissions, approximately 61-84%. Heating demand dominates operational emissions in severe-cold and cold regions, whereas cooling and lighting loads become increasingly important in warm and temperate climates. The orthogonal analysis reveals significant differences in the sensitivity of design parameters across climatic regions. After implementing climate-adaptive optimization measures, life-cycle carbon emissions are reduced by 40.35-87.94% in four service areas. In the hot-summer/warm-winter region, the combined effects of PV electricity generation and vegetation carbon sinks maintain a net-negative carbon balance. The findings provide evidence for sustainable highway service-area design by linking life-cycle accounting, climate-specific design priorities, and renewable-energy substitution.

Model-free Extremum Seeking Control (ESC) is a promising control strategy for ground-source heat pump (GSHP) systems because it optimizes control setpoints in real time without requiring an explicit system model. This study evaluates the performance of ESC under summer and winter operating conditions and compares it with conventional ON-OFF and PID control strategies. Simulation results show that ESC consistently reduces total system energy consumption under both peak-load and part-load conditions. In summer, simulation results indicate that ESC has the potential to achieve comparative energy savings of up to 26.8% compared with ON-OFF control and 6.2% compared with PID control. In winter, the corresponding savings reach 15.8% and 6.1% on the design day, demonstrating stable performance across seasonal operating regimes. The improved energy efficiency mainly results from two mechanisms. First, ESC dynamically optimizes supply water temperature setpoints, which enhances heat pump operation and increases the coefficient of performance relative to PID control. Second, ESC coordinates hydronic circuit operation to stabilize supply return temperature differences and regulate flow rates, thereby reducing pump energy consumption. Overall, the results indicate that ESC provides a stable and modelfree optimization approach for improving the energy efficiency and operational stability of GSHP systems.

The development of high-efficiency building energy-saving technologies was essential for achieving carbon neutrality. Leveraging the optical transparency and ultra-low thermal conductivity of aerogel materials, a thermal diode solar collector (TDSC) was developed. Compared with a conventional non-selective solar collector (NSC), the TDSC achieved a maximum temperature 1.92 times higher and a heat flux density 1.71 times greater, with a thermal rectification ratio of up to 100. Outdoor experiments further showed a maximum temperature difference of 26.99 °C between the two systems. Based on the TDSC, a thermal diode wall (TDW) incorporating phase change materials with different melting points was constructed. The daily average temperatures of TDW-25, TDW-35, and TDW-45 ranged from 20.70 to 27.01 °C, 26.55–31.02 °C, and 32.11–38.04 °C, respectively, all significantly higher than the corresponding outdoor temperatures. The thermal decrement factors ranged from 0.4 to 2, with time delays of 0.50–3.17 h, indicating effective thermal regulation and buffering of external heat fluctuations. Numerical simulations across four representative climate zones demonstrated that in cold regions with abundant solar resources, such as Lhasa, thermal diode exterior walls could achieve passive building energy savings while maintaining indoor heating comfort. Overall, the proposed TDSC and TDW systems provided a promising pathway for sustainable and energy-efficient building technologies and contributed to carbon emission reduction.

The increasing adoption of carbon trading mechanisms compels internet data centers (IDCs) to procure carbon allowances under uncertain and dynamic market conditions. Existing approaches predominantly focus on single-stage optimization or static pricing assumptions, lacking the integration of workload allocation strategies and adaptive procurement decisions in a stochastic environment. To address these gaps, we propose a two-stage joint optimization and reinforcement learning (RL)-based framework for carbon management. In the first stage, computational workloads are dynamically allocated across geographically distributed IDCs according to regional carbon intensity, aiming to reduce operational emissions. In the second stage, a Soft Actor-Critic (SAC)-based RL model is developed to optimize continuous carbon allowance purchasing decisions under market uncertainty. The proposed framework introduces three major innovations: (i) a two-stage joint optimization that bridges emission reduction scheduling and strategic carbon allowance procurement; (ii) an endogenous carbon price modeling mechanism that captures market dynamics and price-demand interactions; and (iii) a SAC-based RL approach that leverages entropy-regularized exploration and multi-objective reward integration to ensure robust, cost-effective procurement strategies. Experimental results demonstrate that the proposed framework reduces annual carbon procurement costs by 5.90%-7.38% compared to benchmark strategies, while maintaining compliance, reducing carbon emissions, and avoiding excessive inventory. These findings highlight the potential of combining joint optimization and deep RL for sustainable and economically efficient data center operations. Specifically, the 5.90%-7.38% reduction in procurement costs compared to benchmark strategies not only improves operational efficiency but also promotes sustainability by reducing carbon emissions, highlighting the framework's dual impact on both cost and environmental performance.

The phenomenon in which the rate of surface air temperature increase in the Arctic region exceeds the global mean temperature rise is known as Arctic amplification. This phenomenon accelerates the retreat of sea ice and snow cover as well as permafrost thawing, which in turn leads to increased greenhouse gas emissions from Arctic permafrost. Arctic amplification is primarily driven by local energy imbalances, dominated by the combined effects of surface albedo and lapse rate feedbacks. This paper reviews four conventional solar radiation modification (SRM) measures proposed in the literature to counteract polar warming, and outlines their associated environmental risks. Compared to conventional SRM techniques, a new energy management strategy with lower environmental risks is proposed to mitigate Arctic amplification: a spray ice-enhancing system utilizing the chimney effect of a solar chimney. This localized system can potentially increase sea ice coverage and thickness, and transport the heat released from seawater across the temperature inversion layer to the upper atmosphere, thereby weakening the positive surface albedo and lapse rate feedbacks in the Arctic. Numerical simulations are performed to analyze the heat and mass transfer characteristics within the system. The key operational challenges of deploying such a system in the Arctic and corresponding feasible solutions are discussed. A preliminary scheme for implementing an experimental prototype is also presented.

Global temperature rise was approached the climate target set by the Paris Agreement. Few feasible technical solutions were available for mitigating climate change by removing methane from the atmosphere. The paper proposed a solar chimney power plant plus solar pond (SCPP-SP) system. Atmospheric methane was eliminated by enhancing synergistic oxidation between chlorine atoms and hydroxyl radicals. A numerical model incorporating flow, heat transfer, chemical reactions and power generation was developed to evaluate the performance of the system. The carbon emission reduction performance of the system was investigated. The results revealed that turbine rotational speed and relative humidity were key parameters affecting system performance. Increasing the turbine speed raised the methane removal rate from 6.13 g/h to 7.58 g/h, and the contribution ratio of chlorine atoms increased from 63.3% to 76.6%. A rise in relative humidity had a weaker effect on the removal rate but strengthened the hydroxyl radical-dominated oxidation pathway. The maximum improvements in photocatalytic efficiency and methane removal rate reached 16.20% and 16.25%, respectively. The maximum power output and carbon reduction rate of the system reached 82.45 kW and 80.26 kg/h respectively, at a turbine speed of 180 rpm and a relative humidity of 80%. This study evaluated the performance of the system in removing atmospheric methane and reducing carbon emissions, and provided theoretical tools for its subsequent optimization.