Local O2 concentrating boosts the electro-Fenton process for energy-efficient water remediation.

The electro-Fenton process is a state-of-the-art water treatment technology used to remove organic contaminants. However, the low O2 utilization efficiency (OUE, <1%) and high energy consumption remain the biggest obstacles to practical application. Here, we propose a local O2 concentrating (LOC) approach to increase the OUE by over 11-fold compared to the conventional simple O2 diffusion route. Due to the well-designed molecular structure, the LOC approach enables direct extraction of O2 from the bulk solution to the reaction interface; this eliminates the need to pump O2/air to overcome the sluggish O2 mass transfer and results in high Faradaic efficiencies (~50%) even under natural air diffusion conditions. Long-term operation of a flow-through pilot device indicated that the LOC approach saved more than 65% of the electric energy normally consumed in treating actual industrial wastewater, demonstrating the great potential of this system-level design to boost the electro-Fenton process for energy-efficient water remediation.

Bioinspired zero-energy thermal-management device based on visible and infrared thermochromism for all-season energy saving.

Radiative thermal management provides a zero-energy strategy to reduce the demands of fossil energy for active thermal management. However, whether solar heating or radiative cooling, one-way temperature control will exacerbate all-season energy consumption during hot summers or cold winters. Inspired by the Himalayan rabbit's hair and Mimosa pudica's leaves, we proposed a dual-mode thermal-management device with two differently selective electromagnetic spectrums. The combination of visible and infrared "thermochromism" enables this device to freely switch between solar heating and radiative cooling modes by spontaneously perceiving the temperature without any external energy consumption. Numerical prediction shows that a dual-mode device exhibits an outstanding potential for all-season energy saving in terms of thermal management beyond most static or single-wavelength, range-regulable, temperature-responsive designs. Such a scalable and cost-efficient device represents a more efficient radiative thermal-management strategy toward applying in a practical scenario with dynamic daily and seasonal variations.

Aging, generational shifts, and energy consumption in urban China.

China is recognized as the largest energy consumer and is also the country with the largest and fastest-aging population. Ongoing demographic changes may reshape China's household-based energy consumption patterns because of the large gap in consumption behavior between the elderly and the young as well as varying attitudes toward the environment among generations. However, when the impact of China's aging population on energy consumption is projected, the heterogeneous cognitive norms of generations in the process of demographic transition are not well understood. In this study, we assessed the future impact of China's demographic transition on energy consumption using a proposed theoretical framework to distinguish between age and generational effects. Specifically, we used age-period-cohort (APC) detrended analysis to estimate age and generational effects based on China's urban household survey data from 1992 to 2015. The results indicated large differences in energy use propensity across ages and generations. The elderly and younger generations tended to be energy-intensive consumers, resulting in higher energy consumption in this aging society. Our results consequently show that future changes in China's elderly population will result in a substantial increase in energy consumption. By 2050, the changing consumption share of the elderly population will account for ∼17 to 26% of total energy consumption in the residential sector, which is close to 115 million tons of standard coal (Mtce). These findings highlight the need to interlace environmental education policies and demographic transitions to promote energy conservation behavior in children and youth for low-carbon, sustainable development.

Reconfigurable aJ-Level Ferroelectric Transistor-Based Boolean Logic for Logic-in-Memory.

Logic-in-memory (LIM) architecture holds great potential to break the von Neumann bottleneck. Despite the extensive research on novel devices, challenges persist in developing suitable engineering building blocks for such designs. Herein, we propose a reconfigurable strategy for efficient implementation of Boolean logics based on a hafnium oxide-based ferroelectric field effect transistor (HfO2-based FeFET). The logic results are stored within the device itself (in situ) during the computation process, featuring the key characteristics of LIM. The fast switching speed and low power consumption of a HfO2-based FeFET enable the execution of Boolean logics with an ultralow energy of lower than 8 attojoule (aJ). This represents a significant milestone in achieving aJ-level computing energy consumption. Furthermore, the system demonstrates exceptional reliability with computing endurance exceeding 108 cycles and retention properties exceeding 1000 s. These results highlight the remarkable potential of a FeFET for the realization of high performance beyond the von Neumann LIM computing architectures.

Calcium-induced upregulation of energy metabolism heats neurons during neural activity.

Cellular temperature affects every biochemical reaction, underscoring its critical role in cellular functions. In neurons, temperature not only modulates neurotransmission but is also a key determinant of neurodegenerative diseases. Considering that the brain consumes a disproportionately high amount of energy relative to its weight, neural circuits likely generate a lot of heat, which can increase cytosolic temperature. However, the changes in temperature within neurons and the mechanisms of heat generation during neural excitation remain unclear. In this study, we achieved simultaneous imaging of Ca2+ and temperature using the genetically encoded indicators, B-GECO and B-gTEMP. We then compared the spatiotemporal distributions of Ca2+ responses and temperature. Following neural excitation induced by veratridine, an activator of the voltage-gated Na+ channel, we observed an approximately 2 °C increase in cytosolic temperature occurring 30 s after the Ca2+ response. The temperature elevation was observed in the non-nuclear region, while Ca2+ increased throughout the cell body. Moreover, this temperature increase was suppressed under Ca2+-free conditions and by inhibitors of ATP synthesis. These results indicate that Ca2+-induced upregulation of energy metabolism serves as the heat source during neural excitation.

Scaling Supercapacitive Swing Adsorption of CO2 Using Bipolar Electrode Stacks.

Supercapacitive swing adsorption (SSA) modules with bipolar stacks having 2, 4, 8, and 12 electrode pairs made from BPL 4 × 6 activated carbon are constructed and tested for carbon dioxide capture applications. Tests are performed with simulated flue gas (15%CO2 /85%N2) at 2, 4, 8, and 12 V, respectively. Reversible adsorption with sorption capacities (≈58 mmol kg-1) and adsorption rates (≈38 µmol kg-1 s-1) are measured for all stacks. The productivity scales with the number of cells in the module, and increases from 70 to 390 mmol h-1 m-2. The energy efficiency and energy consumption improve with increasing number of bipolar electrodes from 67% to 84%, and 142 to 60 kJ mol-1, respectively. Overall, the results show that SSA modules with bipolar electrodes can be scaled without reducing the adsorptive performance, and with improvement of energetic performance.

Attojoule Hexagonal Boron Nitride-Based Memristor for High-Performance Neuromorphic Computing.

In next-generation neuromorphic computing applications, the primary challenge lies in achieving energy-efficient and reliable memristors while minimizing their energy consumption to a level comparable to that of biological synapses. In this work, hexagonal boron nitride (h-BN)-based metal-insulator-semiconductor (MIS) memristors operating is presented at the attojoule-level tailored for high-performance artificial neural networks. The memristors benefit from a wafer-scale uniform h-BN resistive switching medium grown directly on a highly doped Si wafer using metal-organic chemical vapor deposition (MOCVD), resulting in outstanding reliability and low variability. Notably, the h-BN-based memristors exhibit exceptionally low energy consumption of attojoule levels, coupled with fast switching speed. The switching mechanisms are systematically substantiated by electrical and nano-structural analysis, confirming that the h-BN layer facilitates the resistive switching with extremely low high resistance states (HRS) and the native SiOx on Si contributes to suppressing excessive current, enabling attojoule-level energy consumption. Furthermore, the formation of atomic-scale conductive filaments leads to remarkably fast response times within the nanosecond range, and allows for the attainment of multi-resistance states, making these memristors well-suited for next-generation neuromorphic applications. The h-BN-based MIS memristors hold the potential to revolutionize energy consumption limitations in neuromorphic devices, bridging the gap between artificial and biological synapses.

Caffeine: a potential mechanism for anti-obesity.

Obesity refers to the excessive accumulation of fat caused by a long-term imbalance between energy intake (EI) and energy expenditure (EE). Over recent years, obesity has become a major public health challenge. Caffeine is a natural product that has been demonstrated to exert anti-obesity effects; however, the mechanisms responsible for the effect of caffeine on weight loss have yet to be fully elucidated. Most obesity-related deaths are due to cardiovascular disease. Recent research has demonstrated that caffeine can reduce the risk of death from cardiovascular disease; thus, it can be hypothesized that caffeine may represent a new therapeutic agent for weight loss. In this review, we synthesize data arising from clinical and animal studies over the last decade and discuss the potential mechanisms by which caffeine may induce weight loss, focusing particularly on increasing energy consumption, suppressing appetite, altering lipid metabolism, and influencing the gut microbiota. Finally, we summarize the major challenges associated with caffeine and anti-obesity research and highlight possible directions for future research and development.

Savings in CT Net Scan Energy Consumption: Assessment Using Dose Report Metrics and Comparison With Savings in Idle State Energy Consumption.

BACKGROUND. CT scanners' net scan state (i.e., image acquisition period) represents a potential target for energy savings through protocol adjustments. However, gauging CT energy savings is difficult without installing costly energy monitors. OBJECTIVE. The purpose of this article was to assess correlations between CT dose report metrics and energy consumption during the system net scan state and to compare theoretic energy savings from matching percentage reductions in energy consumption during net scan and idle system states. METHODS. Current sensors were installed on a single CT scanner. A phantom was scanned at varying kilovoltage settings and effective tube current-rotation time settings. A retrospective assessment was performed in 32 patients (mean age, 61.2 ± 17.9 [SD] years; 17 men, 15 women) who underwent 32 single-energy noncontrast abdominopelvic CT examinations from September 22, 2021, to September 27, 2021, on the same scanner. Correlations between dose report metrics and net scan energy consumption were assessed in the phantom and clinical scans, and equations were generated to derive net scan energy consumption from DLP. An additional retrospective assessment was performed in 1355 patients (mean age, 59.3 ± 16.9 years; 663 men, 692 women) who underwent 1728 single-energy noncontrast abdominopelvic CT examinations from January 1, 2021, through December 31, 2021, on the same scanner to estimate net scan energy consumption per examination. This information was integrated with literature-derived values to compare estimated annual national energy savings resulting from 20% reductions in net scan and idle state energy consumption. RESULTS. Net scan energy consumption in the phantom scans showed high linear correlation with DLP (R2 = 0.87), and, in the clinical scans, high linear correlation with CTDIvol (R2 = 0.89) and very high linear correlation with DLP (R2 = 0.92). When combining mean DLP in examinations performed in the 1-year interval, an equation relating DLP and net scan energy consumption and literature values estimated that annual national energy savings was 14.9 times greater (40,437,870 kWh/2,704,000 kWh) by targeting the idle state rather than net scan state. CONCLUSION. CT net scan energy savings can be inferred from reductions in dose report metrics. However, targeting net scan energy consumption has modest impact relative to targeting idle state energy consumption. CLINICAL IMPACT. Environmental sustainability efforts should target the idle state energy consumption of CT.

Interventional Imaging Systems in Radiology, Cardiology, and Urology: Energy Consumption, Carbon Emissions, and Electricity Costs.

BACKGROUND. The energy demand of interventional imaging systems has historically been estimated using manufacturer-provided specifications rather than directly measured. OBJECTIVE. The purpose of this study was to investigate the energy consumption of interventional imaging systems and estimate potential savings in the carbon emissions and electricity costs of such systems through hypothetical operational adjustments. METHODS. An interventional radiology suite, neurointerventional suite, radiology fluoroscopy unit, two cardiology laboratories, and two urology fluoroscopy units were equipped with power sensors. Power measurement logs were extracted for a single 4-week period for each radiology and cardiology system (all between June 1, 2022, and November 28, 2022) and for the 2-week period from July 31, 2023, to August 13, 2023, for each urology system. Power statuses, procedure time stamps, and fluoroscopy times were extracted from various sources. System activity was divided into off, idle (no patient in room), active (patient in room for procedure), and net-imaging (active fluoroscopic image acquisition) states. Projected annual energy consumption was calculated. Potential annual savings in carbon emissions and electricity costs through hypothetical operational adjustments were estimated using published values for Switzerland. RESULTS. Across the seven systems, the mean power draw was 0.3-1.1, 0.7-7.4, 0.9-7.6, and 1.9-12.5 kW in the off, idle, active, and net-imaging states, respectively. Across systems, the off state, in comparison with the idle state, showed a decrease in the mean power draw of 0.2-6.9 kW (relative decrease, 22.2-93.2%). The systems had a combined projected annual energy consumption of 115,684 kWh (range, 3646-26,576 kWh per system). The systems' combined projected energy consumption occurring outside the net-imaging state accounted for 93.3% (107,978/115,684 kWh) of projected total energy consumption (range, 89.2-99.4% per system). A hypothetical operational adjustment whereby all systems would be switched from the idle state to the off state overnight and on weekends (versus being operated in idle mode 24 hours a day, 7 days a week) would yield the following potential annual savings: for energy consumption, 144,640 kWh; for carbon emissions, 18.6 metric tons of CO2 equivalent; and for electricity costs, US$37,896. CONCLUSION. Interventional imaging systems are energy intensive, having high consumption outside of image acquisition periods. CLINICAL IMPACT. Strategic operational adjustments (e.g., powering down idle systems) can substantially decrease the carbon emissions and electricity costs of interventional imaging systems.

Cerium-MOF-Derived Composite Hierarchical Catalyst Enables Energy-Efficient and Green Amine Regeneration for CO2 Capture.

The excessive energy consumed restricts the application of traditional postcombustion CO2 capture technology and limits the achievement of carbon-neutrality goals. Catalytic-rich CO2 amine regeneration has the potential to accelerate proton transfer and increase the energy efficiency in the CO2 separation process. Herein, we reported a Ce-metal-organic framework (MOF)-derived composite catalyst named HZ-Ni@UiO-66 with a hierarchical structure, which can increase the CO2 desorbed amount by 57.7% and decrease the relative heat duty by 36.5% in comparison with the noncatalytic monoethanolamine (MEA) regeneration process. The composite catalyst of the CeO2 coating from the UiO-66 precursor on the HZ-Ni carrier shows excellent stability with a long lifespan. The HZ-Ni@UiO-66 catalyst also shows a universal catalytic effect in typical blended amine systems with a large cyclic capacity. The HZ-Ni@UiO-66 catalyst effectively decreases the energy barrier of the CO2 desorption reaction to reduce the time required to reach thermodynamics, consequently saving the energy consumption generated by water evaporation. This research provides a new avenue for advancing amine regeneration with less heat duty at low temperatures.

Mixing due to Solution Switch Limits the Performance of Electrosorption for Desalination.

Electrosorption (ES) is a research frontier in electrochemical separation, with proven potential applications in desalination, wastewater treatment, and selective resource extraction. However, due to the limited adsorption capacity of film electrodes, ES requires short circuiting or circuit reversal, accompanied by a solution switch between the feed solution and receiving solution, to sustain desalination over many charge-discharge cycles. In previously reported studies, solution switches have been commonly ignored to simplify experimental procedures, and their impacts on separation performance are thus not well understood. This study aims to provide a quantitative analysis of the impacts of mixing due to a solution switch on the performance of ES-based desalination. A numerical model of ES has been employed to evaluate the adverse effects of the solution switch on the desalination performance in three commonly used operation modes. The analysis reveals that the impacts of mixing due to solution-switch are more severe with a larger concentration difference between the desalinated water and the brine and provides insights into the effectiveness of increasing electrode loading or specific capacity in mitigating the detrimental impacts of mixing. Even with state-of-the-art systems, producing freshwater from seawater or even brackish water with medium-to-high salinity is practically challenging due to the presence of solution switch.

Coupling of Electric and Flow Fields to Enhance Ion Transport for Energy-Efficient Electrochemical Tap-Water Softening.

Electrochemical-induced precipitation is a sustainable approach for tap-water softening, but the hardness removal performance and energy efficiency are vastly limited by the ultraslow ion transport and the superlow local HCO3-/Ca2+ ratio compared to the industrial scenarios. To tackle the challenges, we herein report an energy-efficient electrochemical tap-water softening strategy by utilizing an integrated cathode-anode-cathode (CAC) reactor in which the direction of the electric field is reversed to that of the flow field in the upstream cell, while the same in the downstream cell. As a result, the transport of ions, especially HCO3-, is significantly accelerated in the downstream cell under a flow field. The local HCO3-/Ca2+ ratio is increased by 1.5 times, as revealed by the finite element numerical simulation and in situ imaging. In addition, a continuous flow electrochemical system with an integrated CAC reactor is operated for 240 h to soften tap water. Experiments show that a much lower cell voltage (9.24 V decreased) and energy consumption (28% decreased) are obtained. The proposed ion-transport enhancement strategy by coupled electric and flow fields provides a new perspective on developing electrochemical technologies to meet the flexible and economic demand for tap-water softening.

Moderating AC Usage Can Reduce Thermal Disparity between Indoor and Outdoor Environments.

In the context of escalating urban heat events due to climate change, air conditioning (AC) has become a critical factor in maintaining indoor thermal comfort. Yet the usage of AC can also exacerbate outdoor heat stress and burden the electricity system, and there is little scientific knowledge regarding how to balance these conflicting goals. To address this issue, we established a coupled modeling approach, integrating the Weather Research and Forecasting model with the building energy model (WRF_BEP + BEM), and designed multiple AC usage scenarios. We selected Chongqing, China's fourth-largest megacity, as our study area due to its significant socioeconomic importance, the severity of extreme heat events, and the uniqueness of its energy infrastructure. Our analysis reveals that AC systems can substantially reduce indoor temperatures by up to 18 °C; however, it also identifies substantial nighttime warming (2-2.5 °C) and a decline in thermal comfort. Particularly for high-density neighborhoods, when we increase 2 °C indoors, the outdoor temperature can be alleviated by up to 1 °C. Besides, despite the limited capacity to regulate peak electricity demand, we identified that reducing the spatial cooled fraction, increasing targeted indoor temperature by 2 °C, and implementing temporal AC schedules can effectively lower energy consumption in high-density neighborhoods, especially the reduction of spatial cooled fraction (up to 50%). Considering the substantial demand for cooling energy, it is imperative to carefully assess the adequacy and continuity of backup energy sources. The study underscores the urgency of reassessing energy resilience and advocates for addressing the thermal equity between indoor and outdoor environments, contributing to the development of a sustainable and just urban climate strategy in an era of intensifying heat events.

Reducing Chloride Ion Permeation during Seawater Electrolysis Using Double-Polyamide Thin-Film Composite Membranes.

Low-cost polyamide thin-film composite membranes are being explored as alternatives to expensive cation exchange membranes for seawater electrolysis. However, transport of chloride from seawater to the anode chamber must be reduced to minimize the production of chlorine gas. A double-polyamide composite structure was created that reduced the level of chloride transport. Adding five polyamide layers on the back of a conventional polyamide composite membrane reduced the chloride ion transport by 53% and did not increase the applied voltage. Decreased chloride permeation was attributed to enhanced electrostatic and steric repulsion created by the new polyamide layers. Charge was balanced through increased sodium ion transport (52%) from the anolyte to the catholyte rather than through a change in the transport of protons and hydroxides. As a result, the Nernstian loss arising from the pH difference between the anolyte and catholyte remained relatively constant during electrolysis despite membrane modifications. This lack of a change in pH showed that transport of protons and hydroxides during electrolysis was independent of salt ion transport. Therefore, only sodium ion transport could compensate for the reduction of chloride flux to maintain the set current. Overall, these results prove the feasibility of using a double-polyamide structure to control chloride permeation during seawater electrolysis without sacrificing energy consumption.

Optimization-enabled deep learning model for disease detection in IoT platform.

Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algorithm, named artificial flora optimization-based chameleon swarm algorithm (AFO-based CSA), is developed for optimal path finding. Here, data are collected by the sensors and transmitted to the base station (BS) using the proposed AFO-based CSA, which is derived by integrating artificial flora optimization (AFO) in chameleon swarm algorithm (CSA). This integration refers to the AFO-based CSA model enhancing the strengths and features of both AFO and CSA for optimal routing of medical data in IoT. Moreover, the proposed AFO-based CSA algorithm considers factors such as energy, delay, and distance for the effectual routing of data. At BS, prediction is conducted, followed by stages, like pre-processing, feature dimension reduction, adopting Pearson's correlation, and disease detection, done by recurrent neural network, which is trained by the proposed AFO-based CSA. Experimental result exhibited that the performance of the proposed AFO-based CSA is superior to competitive approaches based on the energy consumption (0.538 J), accuracy (0.950), sensitivity (0.965), and specificity (0.937).

Assessment of battery utilization and energy consumption in the large-scale development of urban electric vehicles.

Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter-spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking.

Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number.

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843-1864 (1960)] and [J. Sawada, D. S. Modha, "Synapse: Scalable energy-efficient neurosynaptic computing" in Application of Concurrency to System Design (ACSD) (2013), pp. 14-15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a [Formula: see text]-fold discrepancy between biological and lowest possible values of a neuron's computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

Construction and Application of Energy Footprint Model for Digital Twin Workshop Oriented to Low-Carbon Operation.

To address the difficulty of accurately characterizing the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in low-carbon workshops, a low-carbon-operation-oriented construction method of the energy footprint model (EFM) for a digital twin workshop (DTW) is proposed. With a focus on considering the fluctuations in equipment energy consumption and the correlation between multiple pieces of equipment at the workshop production process level (CBMEatWPPL), the EFM of a DTW is obtained to characterize the dynamic evolution of whole energy consumption in the workshop. Taking a production unit as a case, on the one hand, an EFM of the production unit is constructed, which achieved the characterization and visualization of the fluctuations in equipment energy consumption and the dynamic evolution of whole energy consumption in the production unit; on the other hand, based on the EFM, an objective function of workshop energy consumption is established, which is combined with the tool life, robot motion stability, and production time to formulate a multi-objective optimization function. The bee colony algorithm is adopted to solve the multi-objective optimization function, achieving collaborative optimization of cross-equipment process parameters and effectively reducing energy consumption in the production unit. The effectiveness of the proposed method and constructed EFM is demonstrated from the above two aspects.

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.