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相关概念视频

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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Atomic Force Microscopy01:08

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Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
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FAIR数据为材料研究提供了新的视野

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让研究数据可查找,可访问,可互操作和可重复使用 (FAIR) 对于推进材料科学至关重要. 为人工智能 (AI) 分析准备数据将改变科学发现.

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科学领域:

  • 凝聚物质物理
  • 材料科学
  • 化学学

背景情况:

  • 社会进步依赖于材料科学的进步,影响能源,健康和IT领域.
  • 每天都会产生大量的研究数据,
  • 目前的数据实践限制了这些研究数据的实用性,阻碍了知识提取.

研究的目的:

  • 讨论材料科学需要一个FAIR数据基础设施.
  • 探索如何将原始研究数据转化为有价值的知识.
  • 准备材料科学领域使用人工智能进行数据驱动的发现.

主要方法:

  • 讨论FAIR数据管理的原则.
  • 突出数据分析和人工智能的作用.
  • 提出材料科学数据"可查找和AI准备"的策略.

主要成果:

  • FAIR数据基础设施对于释放研究数据的价值至关重要.
  • 数据分析和人工智能可以将研究数据精制成可操作的知识.
  • 未来的科学工作需要采取积极的数据准备方法.

结论:

  • 实施FAIR数据原则对于材料科学创新至关重要.
  • 让数据"可查找和人工智能准备就绪"将彻底改变科学研究.
  • 该领域必须适应以数据为中心的新方法来继续进步.