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Halogen‐free flame‐retardant ethylene‐vinyl acetate/polyethylene/magnesium hydroxide systems are widely used in cable insulations and in sheath materials. The surface modification of magnesium hydroxide is one of the key methods to improve its dispersion and mechanical properties. The effects of different types of interfaces, formed by four surface modifications, namely KH550, KH570, stearic acid, and titanate, on the flame retardancy of composite systems have been investigated in the present study. The results showed that the four surface modifiers formed chemical bonds with the surface of magnesium hydroxide, which promoted the dispersion of particles in the matrix. However, due to the different effects of the various surface modifiers and polymer molecular chains, the interfaces exhibited different chemical structures and physical strengths, which affected the combustion of the composite materials into carbon. Thus, the composites demonstrated different flame‐retardant properties. The surface modifiers KH550, KH570 and titanate can form chemical bonds with the ethylene‐vinyl acetate (EVA) copolymer and have, thereby, a strong effect on the interfaces, which resulted in a slow ignition of the materials and the formation of a graphite carbon structure that is advantageous for heat transfer and oxygen insulation. This is conducive to the flame‐retardant performance of the composite systems. [ABSTRACT FROM AUTHOR]

The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRPTWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indicate that the suggested MODRL-SIA algorithm outperforms other algorithms in every computational instance, further confirming its efficacy in lowering overall distribution expenses, carbon emissions, and the depletion of fresh produce in the supply chain of fresh agricultural products. [ABSTRACT FROM AUTHOR]

Feature selection is to select the optimal or relatively optimal feature subsets from the original feature set of the data set to speed up classification and improve classification accuracy. An improved shuffled binary grass-hopper optimization feature selection algorithm is proposed in this paper. By introducing a binary transformation strategy that uses step size to guide individual position change, the blindness of the binary conversion is reduced, and the search performance of the algorithm in solution space is improved. By introducing shuffled complex evolution, the grasshopper population is divided into subgroups and evolved independently, which improves the diversity of algorithm and reduces the probability of premature convergence. The improved algorithm is used to select features of some data sets of UCI, and K-NN (K-nearest neighbor) classifier is used to classify and evaluate the feature subset. Experimental results show that compared with the basic binary grasshopper optimization algorithm, binary particle swarm optimization algorithm and binary gray wolf optimization algorithm, the improved algorithm has better search performance, convergence performance and strong robustness, and can obtain better feature subsets and better classification effect.

Fast charge transfer and lithium-ion transport in the electrodes are necessary for high performance Li–S batteries. Herein, a N-doped carbon-coated intercalated-bentonite (Bent@C) with interlamellar ion path and 3D conductive network architecture is designed to improve the performance of Li–S batteries by expediting ion/electron transport in the cathode. The interlamellar ion pathways are constructed through inorganic/organic intercalation of bentonite. The 3D conductive networks consist of N-doped carbon, both in the interlayer and on the surface of the modified bentonite. Benefiting from the unique structure of the Bent@C, the S/Bent@C cathode exhibits a high initial capacity of 1,361 mA h g−1 at 0.2C and achieves a high reversible capacity of 618.1 m Ah g−1 at 2C after 500 cycles with a sulfur loading of 2 mg cm−2. Moreover, with a higher sulfur loading of 3.0 mg cm−2, the cathode still delivers a reversible capacity of 560.2 mA h g−1 at 0.1C after 100 cycles.

Conductive hydrogels have great potential for application in the field of flexible wearable devices due to their excellent stretchability and biocompatibility. In this work, double-network hydrogels were prepared by ion crosslinking and chemical crosslinking, and the resulting hydrogel sensors exhibited excellent performance in terms of stretchability and sensitivity. Specifically, the elongation at break reached 900%, with a fast response time (157 ms), high conductivity (1.47 S m−1), high sensitivity (gauge factor = 5.42, strain of 900%), and high fatigue resistance (1800 cycles of testing). Based on these excellent properties, the hydrogels were assembled into strain sensors, successfully detecting signal changes in human motion states, and recognizing different gestures. Therefore, the prepared hydrogels can be used as soft sensors for motion monitoring, electronic skin, and other fields. [ABSTRACT FROM AUTHOR]

Besides the adjustment of the active centres, the precisely designed microstructures of the carbon hosts also play a significant role in improving the battery performance. Herein, MOF-derived Fe3O4@NCs were prepared through a molten salt-assisted calcination method at different carbonization temperatures. Compared with the materials obtained at 700 °C, LK450 calcined at a lower temperature of 450 °C maintains suitable pore sizes and more N-doping and exhibits excellent Li-ion transport performance. Thus, the S/LK450 cathode can achieve an outstanding rate performance of up to 5 C (∼528 mA h g−1) and an extremely low capacity decay of 0.037% per cycle after 500 cycles at 1C. Notably, even with a high sulfur loading (4.0 mg cm−2), the S/LK450 cathode can still deliver a high capacity of 673 mA h g−1 at 0.2C after 100 cycles. Briefly, this work demonstrates the superiorities to prepare the samples at relatively low carbonization temperatures, which guarantee a better ion path structure and sufficient N-doping in the carbon skeleton. [ABSTRACT FROM AUTHOR]