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相关概念视频

Chromatographic Methods: Classification01:12

Chromatographic Methods: Classification

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Chromatographic techniques are classified in three ways: the classification is based on the physical state of the stationary and mobile phases, how the mobile phase and the stationary phase contact each other, or through the chemical or physical processes that isolate the components of the sample. Typically, the mobile phase is either a liquid or gas, while the stationary phase is either a solid or a liquid layer applied to a solid surface.
Chromatographic techniques are typically named by...
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Chromatographic Methods: Terminology01:18

Chromatographic Methods: Terminology

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Chromatography is an analytical technique widely used in fields such as chemistry, biology, environmental science, and pharmaceuticals to separate the components of a mixture and identify substances between them. The process of chromatography is based on the interactions between two distinct phases: the stationary phase and the mobile phase. The stationary phase is fixed in place by a supporting material, while the mobile phase moves over it, carrying the solutes. As the mobile phase travels,...
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Chromatography: Introduction01:10

Chromatography: Introduction

4.4K
Chromatography is a technique used to separate compounds based on differences of partitioning between two phases, the stationary phase and the mobile phase.
The phase in which the compounds linger or on which the compounds adsorb is called the stationary phase, whereas the mobile phase is the solvent that carries the solutes to be analyzed. In traditional column chromatography, the mixture flows through the stationary phase, and the compounds partition between the stationary and mobile phases...
4.4K
Principles Of Column Chromatography01:13

Principles Of Column Chromatography

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The chromatography technique was first invented in 1901 by Michael S. Tswett, a Russian botanist, to separate plant pigments using organic solvents. Further, in 1941, Archer John Porter Martin and R. L. M. Synge modified the technique by packing silica gel into a column. A mixture of amino acids was then separated on the packed column using chloroform and water mixture as the mobile phase. This was the first report on column chromatography. At present, column chromatography is a widely used...
7.0K
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

2.1K
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:
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使用人工智能和机器学习进行染色体预测的当前趋势.

Yash Raj Singh1, Darshil B Shah1, Mangesh Kulkarni2

  • 1Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India.

Analytical methods : advancing methods and applications
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人工智能 (AI) 和机器学习 (ML) 在染色学中提供更快,更准确的预测. 这些方法,特别是人工神经网络 (ANN),在预测色谱特征和保留时间方面表现优于传统模型.

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科学领域:

  • 分析化学 分析化学
  • 计算化学的计算化学

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 越来越多地被用于它们在各种科学领域的预测能力,准确性和速度.
  • 在染色学中,AI和ML对于方法开发特别有价值,为预测染色学特征提供高效和准确的解决方案.

研究的目的:

  • 审查用于确定色谱特征的各种AI和ML模型.
  • 探索人工神经网络 (ANN) 技术及其在液体染色学中的优势与经典线性模型相比.
  • 突出将模糊系统与ANN集成的好处,并将AI / ML与定量结构-保留关系 (QSRR) 结合起来,以提高预测.

主要方法:

  • 对AI和ML在染色学中的应用现有文献的审查.
  • 对基于人工神经网络 (ANN) 的技术进行分析,用于染色学预测.
  • 研究混合方法,包括与ANN和QSRR结合ANN的模糊系统.

主要成果:

  • 与经典线性模型相比,与ANN相关的技术显示出更高的准确性和预测色谱特征的潜力.
  • 模糊系统与ANN的整合提供了更有效,更准确的染色学预测方法.
  • 将AI/ML算法与QSRR结合起来,可以显著提高目标分子保留预测的准确性.

结论:

  • 人工智能和机器学习算法,特别是ANN和混合模型,为推进色谱方法开发和预测提供了强大的工具.
  • 这些先进的计算方法显示出克服分析化学挑战的巨大潜力,从而实现更精确,更有效的分析.
  • 人工智能/ML与QSRR的整合代表了在液体染色学中准确预测保留的有希望的方向.