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相关概念视频

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...
377
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,...
2.1K
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...
2.2K
Chromatography: Introduction01:10

Chromatography: Introduction

4.3K
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...
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Chromatographic Resolution01:15

Chromatographic Resolution

460
In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...
460
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...
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相关实验视频

Updated: Jun 23, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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一种基于物理信息的神经网络的染色分离过程的参数估计方法.

Tao Zou1, Tomoyuki Yajima1, Yoshiaki Kawajiri2

  • 1Department of Materials Process Engineering, Nagoya University, Furo-cho 1, Chikusa, Nagoya, Aichi, 464-8603 Japan.

Journal of chromatography. A
|June 16, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的物理信息神经网络 (PINN) 模型,用于染色分离过程. 与传统方法相比,PINN方法显著减少了计算时间和参数拟合错误.

关键词:
机器学习 机器学习参数估计的参数估计.基于物理学的神经网络.准备性色谱学 准备性色谱学

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 计算科学 计算科学

背景情况:

  • 染色分离过程通常使用部分微分方程 (PDEs) 建模,以捕捉复杂的吸附平衡和动力学.
  • 在这些PDE模型中,参数识别是计算密集且耗时的.

研究的目的:

  • 开发和验证一种新的物理信息神经网络 (PINN) 模型,用于染色分离中的参数估计.
  • 与传统方法相比,评估PINN方法的准确性,效率和稳定性.

主要方法:

  • 一个基于物理的神经网络 (PINN) 模型被开发用于二进制组件色谱系统.
  • 对PINN模型的数值准确性进行了与有限元素方法 (FEM) 相比的验证.
  • 模型参数估计使用从列出口数据的PINN,包括杂的实验数据.

主要成果:

  • 该PINN模型表现出高的数值准确性,与FEM模拟相美.
  • 与传统方法相比,参数拟合误差减少了高达35.0%.
  • 使用PINN方法,计算时间减少了高达95%.

结论:

  • 开发的PINN模型为染色分离中的参数估计提供了一个计算效率高,准确的替代方案.
  • PINN模型在处理杂的实验数据方面表现出稳健性.
  • 这种方法有可能加速染色体学过程建模和优化.