Phase change materials (PCMs) are regarded as promising candidates for realizing zero‐energy thermal management of electronic devices owing to their high thermal storage capacity and stable working temperature. However, PCM‐based thermal management always suffers from the long‐standing challenges of low thermal conductivity and liquid leakage of PCMs. Herein, a dual‐encapsulation strategy to fabricate highly conductive and liquid‐free phase change composites (PCCs) for thermal management by constructing a polyurethane/graphite nanoplatelets hybrid networks is reported. The PCM of polyethylene glycol (PEG) is first infiltrated into the cross‐linked network of polyurethane (PU) to synthesize hybridized semi‐interpenetrated composites (PEG@PU), and then incorporated with reticulated graphite nanoplatelets (RGNPs) via pressure‐induced assembly to fabricate highly conductive PCCs (PEG@PU‐RGNPs). The hybrid networks enable the PCCs to show excellent mechanical strength, liquid‐free phase change, and stable thermal property. Notably, the dual‐encapsulated PCCs exhibit high thermal and electrical conductivities up to 27.0 W m−1 K−1 and 51.0 S cm−1, superior to the state‐of‐the‐art PEG‐based PCCs. Furthermore, the PCC‐based energy device is demonstrated for efficient battery thermal management toward versatile demands of active preheating at a cold environment and passive cooling at a hot ambient. Overall, this work provides a promising route for fabricating highly conductive and liquid‐free PCCs toward thermal management.

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In this paper, the problem of reducing the bandwidth of sparse matrices by permuting rows and columns is addressed and solved with a new hybrid heuristic which combines the Particle Swarm Optimization method with Hill Climbing (PSO-HC). This hybrid approach exploits a compact PSO in order to generate high-quality renumbering which is then refined by means of an efficient implementation of the HC local search heuristic. Computational experiments are carried out to compare the performance of PSO-HC with that of the well-known GPS algorithm, as well as some recently proposed methods, including WBRA, Tabu Search, and GRASP PR. PSO-HC proves to be extremely stable and reliable in finding good solutions to the bandwidth minimization problem, outperforming the currently known best algorithms in terms of solution quality, in reasonable computational time.

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