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相关概念视频

High-Performance Liquid Chromatography: Elution Process01:05

High-Performance Liquid Chromatography: Elution Process

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In High-Performance Liquid Chromatography (HPLC), the elution process is critical to the separation of analytes and the quality of chromatographic results. Elution describes how compounds move through the column and separate based on their interactions with the mobile and stationary phases. This process determines the resolution, peak shape, and retention times in the chromatogram, which are essential for identifying and quantifying components in complex mixtures. Understanding the elution...
309
Capillary Electrophoresis: Applications01:30

Capillary Electrophoresis: Applications

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Capillary electrophoretic separations offer various modes, each with unique applications. These modes include capillary zone electrophoresis, capillary gel electrophoresis, capillary array electrophoresis, capillary isoelectric focusing, capillary isotachophoresis, micellar electrokinetic chromatography, and capillary electrochromatography.
Capillary zone electrophoresis (CZE) separates ionic components based on their electrophoretic mobility. It has been used to separate proteins, amino acids,...
293
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

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Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
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High-Performance Liquid Chromatography: Introduction01:11

High-Performance Liquid Chromatography: Introduction

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High-performance liquid chromatography(HPLC), formerly referred to as High-pressure liquid chromatography, is a powerful technique used to separate, identify, and quantify components in complex mixtures. The term "high pressure" refers to using high pressure to push the liquid mobile phase through the tightly packed columns.
In HPLC, two phases play a critical role in the separation process:
556
Size-Exclusion Chromatography01:08

Size-Exclusion Chromatography

421
In size-exclusion chromatography (SEC), also known as molecular-exclusion or gel-permeation chromatography, molecules are separated based on their sizes. This technique is important for separating large molecules such as polymers and biomolecules. The two classes of micron-sized stationary phases encountered in SEC are silica particles and cross-linked polymer resin beads. Both materials are porous, but their pore sizes vary significantly.
Silica particles offer advantages such as rigidity,...
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Overview Of Cell Separation And Isolation01:20

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Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
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用机器学习和高通量技术进行分离过程的溶剂选.

Justin P Edaugal1, Difan Zhang2, Dupeng Liu1

  • 1Advanced Biofuels and Bioproducts Process Development Unit, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Emeryville, California 94608, United States.

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概括
此摘要是机器生成的。

人工智能 (AI) 加快了绿色溶剂的发现,比如离子液体 (IL) 和深溶剂 (DES). 机器学习模型和自动化平台是预测性能和设计可持续化学过程的关键.

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科学领域:

  • 绿色化学 绿色化学
  • 计算化学的计算化学
  • 化学工程是化学工程的重要组成部分.

背景情况:

  • 化学工业正在向可持续实践过渡,需要取代传统的化石衍生溶剂.
  • 离子液体 (ILs) 和深溶剂 (DESs) 正在成为有希望的环保替代品.
  • 人工智能 (AI) 在开发新型溶剂和绿色化学过程方面越来越重要.

研究的目的:

  • 审查人工智能辅助溶剂查的最新进展.
  • 专注于机器学习 (ML) 模型,用于预测物理化学性质和设计分离过程.
  • 为突出自动化高通量 (HT) 平台用于溶剂选的进展.

主要方法:

  • 关于AI应用在溶剂发现和设计中的最新文献的审查.
  • 对用于物理化学性质预测的机器学习模型的分析.
  • 对IL和DES的自动化高通量选平台的检查.

主要成果:

  • 人工智能,特别是机器学习,显著提高了溶剂性质的预测和分离过程的设计.
  • 自动化HT平台正在加速新型绿色溶剂的选和识别.
  • 将ML与HT战略集成为绿色溶剂设计提供了一种强大的方法.

结论:

  • 人工智能驱动的溶剂选对于推进可持续化学实践至关重要.
  • 机器学习模型和自动化HT平台是发现和优化绿色溶剂的重要工具.
  • 在ML驱动的HT策略的进一步发展将塑造化学和分离过程的未来.