Preparation of high‐efficient ethylene‐vinyl acetate‐based thermal management materials by reducing interfacial thermal resistance with the assistance of polydopamine

Flexible Ethylene-vinyl Acetate Copolymer/Fluorographene Composite Films with Excellent Thermal Conductive and Electrical Insulation Properties for Thermal Management

ES Materials & Manufacturing ◽

10.30919/esmm5f523 ◽

2021 ◽

Author(s):

He Han ◽

◽

Haoyang Sun ◽

Fan Lei ◽

Jintao Huang ◽

...

Keyword(s):

Thermal Management ◽

Vinyl Acetate ◽

Composite Films ◽

Ethylene Vinyl Acetate ◽

Electrical Insulation ◽

Acetate Copolymer ◽

Ethylene Vinyl Acetate Copolymer ◽

Electrical Insulation Properties

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Significant Reduction of Interfacial Thermal Resistance and Phonon Scattering in Graphene/Polyimide Thermally Conductive Composite Films for Thermal Management

Research ◽

10.34133/2021/8438614 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Kunpeng Ruan ◽

Yongqiang Guo ◽

Chuyao Lu ◽

Xuetao Shi ◽

Tengbo Ma ◽

...

Keyword(s):

Thermal Resistance ◽

Thermal Management ◽

Polymer Composite ◽

Thermal Conduction ◽

Phonon Scattering ◽

Composite Films ◽

Conductive Polymer ◽

Interfacial Thermal Resistance ◽

Conductive Composite ◽

Thermally Conductive

The developing flexible electronic equipment are greatly affected by the rapid accumulation of heat, which is urgent to be solved by thermally conductive polymer composite films. However, the interfacial thermal resistance (ITR) and the phonon scattering at the interfaces are the main bottlenecks limiting the rapid and efficient improvement of thermal conductivity coefficients (λ) of the polymer composite films. Moreover, few researches were focused on characterizing ITR and phonon scattering in thermally conductive polymer composite films. In this paper, graphene oxide (GO) was aminated (NH2-GO) and reduced (NH2-rGO), then NH2-rGO/polyimide (NH2-rGO/PI) thermally conductive composite films were fabricated. Raman spectroscopy was utilized to innovatively characterize phonon scattering and ITR at the interfaces in NH2-rGO/PI thermally conductive composite films, revealing the interfacial thermal conduction mechanism, proving that the amination optimized the interfaces between NH2-rGO and PI, reduced phonon scattering and ITR, and ultimately improved the interfacial thermal conduction. The in-plane λ (λ∥) and through-plane λ (λ⊥) of 15 wt% NH2-rGO/PI thermally conductive composite films at room temperature were, respectively, 7.13 W/mK and 0.74 W/mK, 8.2 times λ∥ (0.87 W/mK) and 3.5 times λ⊥ (0.21 W/mK) of pure PI film, also significantly higher than λ∥ (5.50 W/mK) and λ⊥ (0.62 W/mK) of 15 wt% rGO/PI thermally conductive composite films. Calculation based on the effective medium theory model proved that ITR was reduced via the amination of rGO. Infrared thermal imaging and finite element simulation showed that NH2-rGO/PI thermally conductive composite films obtained excellent heat dissipation and efficient thermal management capabilities on the light-emitting diodes bulbs, 5G high-power chips, and other electronic equipment, which are easy to generate heat severely.

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The Role of Interfacial Thermal Resistance in Li-Ion Battery Thermal Management

ASME 2019 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems ◽

10.1115/ipack2019-6594 ◽

2019 ◽

Author(s):

Chuanbo Yang ◽

Lei Cao

Keyword(s):

Heat Transfer ◽

Steady State ◽

Thermal Resistance ◽

Thermal Management ◽

Thermal Runaway ◽

Interfacial Thermal Resistance ◽

Modeling Framework ◽

Battery Packs ◽

Flash Diffusivity ◽

Battery Module

Abstract Temperature critically affects the performance, life and safety of lithium-ion batteries. Therefore, it is essential to understand heat generation and dissipation within individual battery cells and battery packs to plan a proper thermal management strategy. One of the key challenges is that interfacial heat transfer of a battery unit is difficult to quantify. The steady-state absolute method and the transient laser-flash-diffusivity method were employed to measure heat conductivities of battery layer stacks and individual battery layer separately. Results show flash diffusivity method gives higher thermal conductivity at both cross-plane and in-plane directions. The difference is primarily caused by interfacial thermal resistance so that it can be estimated by steady-state and transient measurements. To investigate the effects of interfacial thermal transport beyond individual cell level, a multiphysics battery model is used. The model is built upon a multi-scale multi-domain modeling framework for battery packs that accounts for the interplay across multiple physical phenomena. Benefits of a battery module using thermal management materials are quantified through numerical experiments. During a thermal runaway event, it is found interfacial thermal resistance can mitigate thermal runaway in a battery module by significantly reducing heat transfer between cells.

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Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Scientific Reports ◽

10.1038/s41598-020-80795-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xiaojuan Tian ◽

Mingguang Chen

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Thermal Resistance ◽

Thermal Management ◽

Material Selection ◽

Gaussian Process Regression ◽

Interfacial Thermal Resistance ◽

Learning Methods ◽

Machine Learning Methods ◽

Descriptor Selection

AbstractInterfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.

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