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The combination of silicon and carbon materials which effectively relieve the volume expansion of silicon and improve the overall electrical conductivity is becoming one of the hot and widespread concern topics. In this research, the pitch was used as a carbon source to load a carbon layer on the surface of silicon, and a porous silicon-carbon anode material was prepared by etching the carbon layer and silicon with sodium hydroxide. The structure and electrochemical performance of porous silicon-carbon composite are further regulated by changing the carbonization temperature and softening point of pitch. The results revealed that appropriate porous structure and stability, as well as a high elastic carbon layer, contribute to the structural stability of the material after long cycles. The change in carbonization temperature regulates the graphitization degree and elasticity of the pitch-derived carbon layer, which would further affect the etching rate. High-softening point pitch has good thermal stability and forms a uniform and stable carbon layer. The porous Si-C composites with an optimized carbon layer exhibit a capacity retention rate of 89.4% after 300 cycles at a current density of 0.2 A g−1, showing excellent cycling stability. [ABSTRACT FROM AUTHOR]

The low-cost and scalable printable mesoporous perovskite solar cells (p-MPSCs) face significant challenges in regulating perovskite crystal growth due to their nanoscale mesoporous scaffold structure, which limits the improvement of device power conversion efficiency (PCE). In particular, the most commonly used solvents, N,N-dimethylformamide (DMF) and dimethyl sulfoxide (DMSO), have a single chemical interaction with the precursor components and high volatility, which is insufficient to self-regulate the perovskite crystallization process, leading to explosive nucleation and limited growth within mesoporous scaffolds. Here, we report a mixed solvent system composed of methylamine formaldehyde (MAFa)-based ionic liquid and acetonitrile (ACN) with the strong C=O–Pb coordination and N–H⋯I hydrogen bonding with perovskite components. We found that the mixed solvent system is beneficial for the precursor solution to homogeneously penetrate into the mesoporous scaffold, and the strong C=O–Pb coordination and N–H⋯I hydrogen bonding interaction can promote the oriented growth of perovskite crystals. This synergistic effect increased the PCE of the p-MPSCs from 17.50% to 19.21%, which is one of the highest records for p-MPSC in recent years. Additionally, the devices exhibit positive environmental stability, retaining over 90% of the original PCE after 1,200 h of aging under AM 1.5 illumination conditions at 55 °C and 55% humidity.

WiFi shows great promise for integrated sensing and communication (ISAC), but its wider application is hampered by two pivotal challenges: the low-quality channel state information (CSI) and inadequate hardware capabilities. This article introduces a paradigm shift with high-coherence hardware capabilities to fundamentally overcome these two challenges. Our approach is realized through four key break-throughs. First, we uncover a novel category of CSI error sources, termed CODEs, which pose core challenges for WiFi ISAC. The CODEs stem from the WiFi hardware design, and addressing them requires hardware-level modifications. Then, we propose a list of high-coherence hardware capabilities aimed at eliminating these errors. Our analysis indicates that the majority of these capabilities can be implemented at the firmware level, with a minority requiring chip-level changes. Next, we introduce two robust and synergistic incentives to encourage vendors to integrate these capabilities into their hardware: the 802.11bf standard and its certification program, and the large-scale carrier-grade purchasing. Finally, we present two demonstrations: a sub-nanosecond level time-of-flight (ToF) estimation system and a WiFi-based phased array. Both demonstrations show that remarkable sensing precision is achievable with the proposed high-coherence capabilities.

The collaborative path planning and scheduling can overcome the limitations of single vehicle intelligence to obtain a globally optimal decision strategy in cognitive Internet of Vehicles (CIoVs). The collaboration of vehicles necessitates the exchange of environmental and decision information, generating massive collaborative computing tasks with strict latency requirements. Leveraging mobile edge computing (MEC) technology, computing tasks can be processed near the vehicles to reduce latency. However, traffic congestion and computational load imbalance seriously affect traffic efficiency and computational latency. In hybrid driving scenarios, it is challenging to fulfill the diverse service requirements of vehicles with different intelligence levels. Moreover, non-collaborative tend to result in traffic congestion due to vehicle aggregation effects, while centralized solutions lack flexibility and have high computational complexity. To address these concerns, a distributed multi-agent reinforcement learning (DMARL) algorithm is proposed for collaborative path planning and scheduling in a blockchain-based collaboration framework. In this framework, we model the communication, traffic situation and task processing of the system and formulate a joint optimization problem to minimize both travel time and computation latency. Last, we convert the scheduling problem for different types of vehicles into Markov decision processes (MDPs) and propose Q-learning-based DMARL algorithm to achieve proactive load balancing of both road infrastructures and MEC nodes (MECNs). Simulation results demonstrate that the proposed approach outperforms the comparison schemes in terms of load balance indexes of roads and MECNs, travel time, and computation latency.

With the exponential growth of user equipment (UE) access in future networks, service operators are deploying many low-cost and low-power heterogeneous base stations. However, when considering cooperative multi-point (CoMP) based soft handover schemes, there will be a trade-off between user quality of service (QoS), system power consumption, and handover frequency. Therefore, in this letter, we proposed a CoMP-based energy-efficient intelligent handover algorithm, named CEH-PPO in heterogeneous cellular networks. The proposed scheme is based on the Proximal Policy Optimization (PPO) algorithm in deep reinforcement learning. The optimization objective is to maximize the average user rates and minimize energy consumption and handover frequency in the CoMP-based heterogeneous network. Thus, we can dynamically select the transmission base station set for UEs to accomplish the handover procedure. Simulation results demonstrate that the proposed CEH-PPO algorithm simultaneously improves the average QoS of UEs in the system and reduces the overall handover frequency while reducing the overall power consumption.

To investigate the formation mechanisms of soil, ice, and oil contamination between pavement layers, composite specimens containing soil, ice, and oil contamination were prepared separately and subjected to shear failure to obtain interlayer failure interfaces under different contamination conditions. Utilizing image recognition analysis, the coverage and distribution of soil and engine oil at the interlayer interface were determined. The process of ice adhesion at the interlayer interface was recorded using a strain gauge, and the formation of ice by water droplets at the interface was documented with a high-definition camera. The research results indicate that during the soil contamination process with a range of 0.3–0.9 kg/m2, the contamination level transitioned from mild soil contamination to severe soil contamination. The average contaminated area per zone gradually increased, reaching a maximum of 5 cm2for an individual contaminated area. Additionally, introducing the concept of bonding failure efficiency revealed that when the soil contamination reached 0.9 kg/m2, the interlayer bonding failure efficiency could reach a maximum of 40.66%. Shear failure tests revealed that the shear resistance of pre-formed ice specimens was weaker than that of naturally formed ice specimens. Observing the freezing process of water on the surface of the mixture based on the differences in thermal expansion coefficients between water/ice and the mixture, as well as the phase change expansion characteristics of water freezing, it was found that in low-temperature environments, water adhered to the mixture surface enters a supercooled state, resulting in a decrease in interface strain. As water begins to undergo phase change, the region where adhered water contacts the base surface freezes, leading to a rapid increase in interface strain. When the adhered water completely freezes into ice and the environmental temperature stabilizes, the interface stress at the ice-adhesion interface tends to stabilize. Based on the principles of the Lab mode, a detailed segmentation process of oil pollution and interlayer interfaces was obtained, and the distribution pattern of engine oil pollution at the interlayer interface was quantified using grayscale values. The defined modes of action for oil pollution at the interlayer were concentrated oil pollution areas and dispersed oil pollution areas. Studying the formation mechanisms of different contaminants between asphalt pavement layers contributes to elucidating the formation patterns of different contaminant factors at interfaces, providing theoretical support for pollution prevention in the engineering field.