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Passive battery thermal management systems (BTMSs) are critical for mitigation of battery thermal runaway (TR). Phase change materials (PCMs) have shown promise for mitigating transient thermal challenges. Fluid leakage and low effective thermal conductivity limit PCM adoption. Furthermore, the thermal capacitance of PCMs diminishes as their latent load is exhausted, creating an unsustainable cooling effect that is transitory. Here, an expanded graphite/PCM/graphene composite that solves these challenges is proposed. The expanded graphite/PCM phase change composite eliminates leakage and increases effective thermal conductivity while the graphene coating enables radiative cooling for PCM regeneration. The composite demonstrates excellent thermal performance in a real BTMS and shows a 26% decrease in temperature when compared to conventional BTMS materials. The composite exhibits thermal control performance comparable with active cooling, resulting in reduced cost and increased simplicity. In addition to BTMSs, this material is anticipated to have application in a plethora of engineered systems requiring stringent thermal management.
Passive battery thermal management systems (BTMSs) are critical for mitigation of battery thermal runaway (TR). Phase change materials (PCMs) have shown promise for mitigating transient thermal challenges. Fluid leakage and low effective thermal conductivity limit PCM adoption. Furthermore, the thermal capacitance of PCMs diminishes as their latent load is exhausted, creating an unsustainable cooling effect that is transitory. Here, an expanded graphite/PCM/graphene composite that solves these challenges is proposed. The expanded graphite/PCM phase change composite eliminates leakage and increases effective thermal conductivity while the graphene coating enables radiative cooling for PCM regeneration. The composite demonstrates excellent thermal performance in a real BTMS and shows a 26% decrease in temperature when compared to conventional BTMS materials. The composite exhibits thermal control performance comparable with active cooling, resulting in reduced cost and increased simplicity. In addition to BTMSs, this material is anticipated to have application in a plethora of engineered systems requiring stringent thermal management.
Microfluidic droplets, with their unique properties and broad applications, are essential in in chemical, biological, and materials synthesis research. Despite the flourishing studies on artificial intelligence‐accelerated microfluidics, most research efforts have focused on the upstream design phase of microfluidic systems. Generating user‐desired microfluidic droplets still remains laborious, inefficient, and time‐consuming. To address the long‐standing challenges associated with the accurate and efficient identification, sorting, and analysis of the morphology and generation rate of single and double emulsion droplets, a novel machine vision approach utilizing the deformable detection transformer (DETR) algorithm is proposed. This method enables rapid and precise detection (detection relative error < 4% and precision > 94%) across various scales and scenarios, including real‐world and simulated environments. Microfluidic droplets identification and analysis (MDIA), a web‐based tool powered by Deformable DETR, which supports transfer learning to enhance accuracy in specific user scenarios is developed. MDIA characterizes droplets by diameter, number, frequency, and other parameters. As more training data are added by other users, MDIA's capability and universality expand, contributing to a comprehensive database for droplet microfluidics. The work highlights the potential of artificial intelligence in advancing microfluidic droplet regulation, fabrication, label‐free sorting, and analysis, accelerating biochemical sciences and materials synthesis engineering.
The rapid fluctuations in heat transfer rates make it challenging to determine the surface temperature history and the estimation of accurate heat generation in research applications such as IC engines, gas turbines, and highspeed space vehicles. Therefore, thin-film gauges (TFGs) are generally used to measure the heat flux in such applications due to their high sensitivity and quick response time. The present study demonstrates that increasing the annealing heat treatment temperature, will enhance the adhesion of the thin film and the capabilities of these hand-made thin-film gauges for transient measurements at low temperatures and for short periods. In the present work, TFG is fabricated in-house using platinum as a sensing element and Macor as an insulating substrate. The sensitivity (S) and temperature coefficient of resistance (TCR) are estimated using an oil batch calibration technique. At the same time, the performance of TFG is tested in a dynamic convective environment. The TFG is exposed to the convective environment using a designed calibration set-up, and their transient heat fluxes are computed by conducting several trials. Additionally, the numerical solution has been accomplished using various experimental parameters. In comparison to the outcomes of the experimental method, it is observed that the average fluctuating temperature and mean surface heat flux have an inaccuracy of 0.33% and 4.17% respectively.
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