The power devices in artificial intelligence computing equipment and new energy vehicles require substrates that possess both electrical insulation and heat dissipation capabilities. The heat dissipation performance of polycrystalline aluminum nitride (AlN) is influenced by both the preparation of the ceramic and the packaging structure. This article compares the results and limitations of related studies along four paths: powder oxygen control and debinding, sintering additives and grain boundary phase regulation, sintering and post-processing, and packaging interface. Existing research indicates that reducing lattice oxygen requires considering densification; additives and sintering processes also change pores, second phases, and mechanical properties, so it is not advisable to directly compare thermal conductivity values without considering the raw materials and test conditions. After entering the power module, the thermal conductivity of the substrate or the strength of the joint may increase, but this cannot alone prove that the module junction temperature has decreased; thermal cycling may also cause cumulative deformation. The selection of high-power chip substrates requires a comprehensive evaluation of the ceramic matrix properties, interface thermal resistance, module junction temperature, and joint stability.

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Because its theoretical capacity is significantly higher than that of traditional graphite, silicon is a desirable anode material for high-energy lithium-ion batteries. However, severe volume change, particle fracture, repeated disruption of the solid-electrolyte interphase, low electrical conductivity, and significant irreversible lithium loss limit its practical application. This paper investigates how nanostructure engineering and associated interface strategies address these coupled mechanical and electrochemical constraints. Stress accommodation, transport behavior, interfacial stability, and electrode packing are compared between low-dimensional silicon, porous and hollow particles, and core-shell or yolk-shell architectures. The complementary methods of surface coatings, artificial interphases, silicon/carbon composites, binder and electrolyte design, and prelithiation are then evaluated. Although areal loading, tap density, volumetric energy density, manufacturing yield, and compatibility with current electrode-processing techniques also affect practical performance, representative studies show that nanoscale control can significantly improve cycling stability, Coulombic efficiency, and rate capability. The analysis suggests that a multiscale design incorporating controlled internal void space, stable interfaces, conductive carbon networks, and manufacturable secondary particles is more promising than relying on a single nanostructure. First-cycle lithium loss, electrode swelling, process complexity, and scale-up are the remaining issues.

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Pore structure affects ion transport and interfacial reactions in sodium-ion battery anodes and provides space to accommodate volume changes. This paper focuses on hard carbon, alloy anode and conversion composite materials, discusses the role of pore size, connectivity, pore opening and internal voids in the sodium storage process, and analyzes the advantages and limitations of different pore structures. The article also compares the synthesis strategies such as template method, activation method, carbonization control, dealloying and composite precursor derivatization, and explains how these methods shape the pore structure. Studies have shown that the sodium storage process and failure mechanism of different anode materials are very different, and the functional requirements of the pore structure are also different. The formation of pores is often accompanied by changes in phase composition. The contribution of pore structure cannot be evaluated only by capacity. The evaluation should also consider the sodium storage mechanism and failure process of the material under comparable electrode preparation and test conditions.

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Perovskite oxides with the ABO₃ structure are core candidates for dielectric energy-storage capacitors, whose recoverable energy density is governed by the dielectric constant, the breakdown field strength, and the nonlinear polarization response. This review surveys recent machine-learning studies on predicting these properties of oxide perovskites, covering three representative quantities—the dielectric constant, the breakdown field strength, and the ferroelectric Curie temperature—together with direct performance indicators such as recoverable energy density and efficiency. The works are compared along the evolution of methods, from manual descriptors and ensemble learning to graph neural networks, small-sample strategies, and generative inverse design, with dataset size, verification level, and accuracy documented. Three insights emerge: physical descriptor models remain competitive and interpretable under small-sample conditions; equivariant graph neural networks offer advantages for tensorial and finite-temperature tasks at higher data and computational cost; and generative inverse design is emerging, with preliminary success at the ferroelectric mesoscopic scale, but remains limited by scarce multimodal data and incomplete experimental-validation loops, which constitute the main challenges ahead.

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