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Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
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高维序列参数和神经网络分类器应用于无形冰.

Zoé Faure Beaulieu1, Volker L Deringer1, Fausto Martelli2,3

  • 1Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3QR, United Kingdom.

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本研究探讨了无形冰相,包括低密度 (LDA) 和中密度 (MDA) 的无形冰. 研究人员发现,由于结构相似,MDA很难与LDA区分.

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科学领域:

  • 凝聚物质物理学 凝聚物质物理学
  • 材料科学是一种材料科学.
  • 物理化学 物理化学

背景情况:

  • 无形冰相是水复杂结构的关键组成部分.
  • 了解这些阶段对于理解水在各种条件下的行为至关重要.
  • 水中的多态性涉及不同无形状态之间的过渡.

研究的目的:

  • 研究水的多态性质,重点研究低密度无形冰 (LDA),高密度无形冰 (HDA) 和中密度无形冰 (MDA).
  • 用先进的顺序参数来描述无形冰中的局部氧气环境.
  • 评估MDA在无形冰结构景观中的独特性.

主要方法:

  • 利用旋转不变的,高维的顺序参数来描述局部对称性.
  • 使用神经网络对局部氧气环境进行分类.
  • 分析了无形冰相之间的结构相似性和差异.

主要成果:

  • 开发了一种方法来描述无形冰中的局部环境.
  • 确定了MDA和LDA之间显著的结构相似之处.
  • 根据目前的结构分析,强调区分MDA和LDA的挑战.

结论:

  • 无形冰的结构景观很复杂,阶段之间有微妙的差异.
  • 当前的方法在明确区分MDA和LDA时面临挑战.
  • 开发的方法提供了分析其他无序材料的潜力.