Efficient thermal management is critical for the safety and performance of lithium-ion battery (LIB) systems, particularly under high C-rate charge–discharge cycling. Here, we investigate two classes of polymer composite thermal interface materials (TIMs): graphene-PLA (GPLA) fabricated via 3D printing and boron nitride nanoplatelets (BN)-loaded thermoplastic polyurethane (TPU) composites with 20 and 40 wt.% BN content. To understand cooling dynamics, we developed a simple analytical model based on Newtonian heat conduction, predicting an inverse relationship between the cooling rate and the TIM thermal diffusivity. We validated this model experimentally using a six-cell LIB module equipped with active liquid cooling, and complemented it with finite-element simulations in COMSOL Multiphysics incorporating experimentally derived parameters. Across all approaches, analytical, numerical, and experimental, we observed excellent agreement in predicting the temperature decay profiles and inter-cell temperature differentials ($\mathrm{\Delta }T$). Charge–discharge cycling studies at varying C-rates demonstrated that high-diffusivity TIMs enable faster cooling but require careful design to minimize lateral thermal gradients. Our results establish that an ideal TIM must simultaneously support rapid vertical heat sinking and effective lateral thermal diffusion to ensure thermal uniformity. Among the studied materials, the 40% BN–60% TPU composite achieved the best overall performance, highlighting the potential of BN filler-engineered polymer composites for scalable thermal management in next-generation battery systems.

Many diverse technological applications, such as soft robotics and flexible electronics, demand the development of intelligent sensors that can simultaneously detect different physical parameters. Taking advantage of plasmonic structures, which can experience minute variations in physical parameters upon close contact, herein, a dual channel based silver nanostructure of concentric square rings and disks on an SiO₂ substrate is proposed for the synchronized detection of magnetic field (H) and temperature (T). The thermometric polydimethylsiloxane (PDMS) and ferromagnetic Fe₃O₄ were placed in two channels of the nanostructure, forming the sensor. The structure modeling and electromagnetic study were carried out using the finite element method (FEM). The simultaneous detection of H and T was realized through the sensing matrix, which solved the problem of cross-sensitivity caused by a variation in temperature. Furthermore, the impact of structural asymmetry on the performance of the sensor was studied by tuning its geometrical parameters, such as disk length and ring length, separately and together. Asymmetry and the channel size significantly enhanced the performance, where disk optimization increased the temperature and magnetic field sensitivity by about 760 and 8319 times using 70% and 80% asymmetric systems, respectively. Also, the smallest ΔW (5 nm) provided a sufficiently high channel separation factor of about 7.47 μm during multi-parameter sensing. In addition, asymmetric sensing toward a single parameter was tested by placing PDMS/Fe₃O₄ on both channels. Multiple peaks were displayed with high sensitivity and CH-factor, making the detection more specific. Thus, the system possessing a combination of narrow channels and unique channel asymmetry exhibited excellent multi- and single-sensing for the detection of temperature and magnetic field.