Decoding thermal properties in polymer-inorganic heat dissipators: a data-driven approach using pyrolysis mass spectrometry.

IF 7.4 3区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Science and Technology of Advanced Materials Pub Date : 2024-06-13 eCollection Date: 2024-01-01 DOI:10.1080/14686996.2024.2362125
Yusuke Hibi, Yasuhiro Tsuyuki, Satoshi Ishii, Eiichi Ide, Masanobu Naito
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引用次数: 0

Abstract

Polymeric materials can boost their performances by strategically incorporating inorganic substances. Heat dissipators are a representative class of such composite materials, where inorganic fillers and matrix polymers contribute to high thermal conductivity and strong adhesion, respectively, resulting in excellent heat dissipation performance. However, due to the complex interaction between fillers and polymers, even slight differences in structural parameters, e.g. dispersion/aggregation degree of fillers and crosslink density of polymers, may significantly impact material performance, complicating the quality management and guidelines for material developments. Therefore, we introduce pyrolysis mass spectra (MS) as material descriptors. On the basis of these spectra, we construct prediction models using a data-driven approach, specifically focusing on thermal conductivity and adhesion, which are key indicators for heat dissipating performance. Pyrolysis-MS observes thermally decomposable polymers, which occupy only 0.1 volume fraction of the heat dissipators; nevertheless, the physical states of non-decomposable inorganic fillers are implicitly reflected in the pyrolyzed fragment patterns of the matrix polymers. Consequently, pyrolysis-MS provides sufficient information to construct accurate models for predicting heat dissipation performance, simplifying quality management by substituting time-consuming performance evaluations with rapid pyrolysis-MS measurements. Furthermore, we elucidate that higher crosslinking density of the matrix polymers enhances thermal conductivity. This data-driven method promises to streamline the identification of key functional factors in complex composite materials.

解码聚合物-无机散热器的热特性:利用热解质谱法的数据驱动方法。
聚合物材料可以通过战略性地加入无机物来提高其性能。散热器就是这类复合材料的代表,无机填料和基质聚合物分别具有高导热性和强粘合性,因而散热性能极佳。然而,由于填料和聚合物之间存在复杂的相互作用,即使结构参数(如填料的分散/聚集程度和聚合物的交联密度)存在细微差别,也会对材料性能产生重大影响,从而使材料开发的质量管理和指导方针变得更加复杂。因此,我们引入了热解质谱(MS)作为材料描述符。在这些光谱的基础上,我们采用数据驱动方法构建了预测模型,特别关注热导率和附着力,它们是散热性能的关键指标。热解质谱法可观察到可热分解的聚合物,它们只占散热器的 0.1 体积分数;然而,不可分解的无机填料的物理状态隐含地反映在基体聚合物的热解碎片模式中。因此,热解质谱提供了足够的信息来构建预测散热性能的精确模型,用快速的热解质谱测量取代了耗时的性能评估,从而简化了质量管理。此外,我们还阐明了基体聚合物的交联密度越高,导热性越强。这种数据驱动的方法有望简化复杂复合材料中关键功能因素的识别。
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来源期刊
Science and Technology of Advanced Materials
Science and Technology of Advanced Materials 工程技术-材料科学:综合
CiteScore
10.60
自引率
3.60%
发文量
52
审稿时长
4.8 months
期刊介绍: Science and Technology of Advanced Materials (STAM) is a leading open access, international journal for outstanding research articles across all aspects of materials science. Our audience is the international community across the disciplines of materials science, physics, chemistry, biology as well as engineering. The journal covers a broad spectrum of topics including functional and structural materials, synthesis and processing, theoretical analyses, characterization and properties of materials. Emphasis is placed on the interdisciplinary nature of materials science and issues at the forefront of the field, such as energy and environmental issues, as well as medical and bioengineering applications. Of particular interest are research papers on the following topics: Materials informatics and materials genomics Materials for 3D printing and additive manufacturing Nanostructured/nanoscale materials and nanodevices Bio-inspired, biomedical, and biological materials; nanomedicine, and novel technologies for clinical and medical applications Materials for energy and environment, next-generation photovoltaics, and green technologies Advanced structural materials, materials for extreme conditions.
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