已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Theoretical considerations and experimental verification

化学 生物系统 校准 线性回归 色谱法 稳健性(进化) 算法 数学 统计 生物化学 生物 基因
作者
Yucheng Chen,Shan‐Jing Yao,Dong‐Qiang Lin
出处
期刊:Journal of Chromatography A [Elsevier]
卷期号:1680: 463418-463418 被引量:18
标识
DOI:10.1016/j.chroma.2022.463418
摘要

Ion exchange chromatography (IEC) is one of the most widely-used techniques for protein separation and has been characterized by mechanistic models. However, the time-consuming and cumbersome model calibration hinders the application of mechanistic models for process development. A new methodology called "parameter-by-parameter method (PbP)" was proposed with mechanistic derivations of the steric mass action (SMA) model of IEC. The protocol includes four steps: (1) first linear regression (LR1) for characteristic charge; (2) second linear regression (LR2) for equilibrium coefficient; (3) linear approximation (LA) for shielding factor; (4) inverse method (IM) for kinetic coefficient. Four SMA parameters could be one-by-one determined in sequence, reducing the number of unknown parameters per species from four to one, and predicting almost consistent retention. Numerical single-component experiments were investigated firstly, and the PbP method showed excellent agreement between experiments and simulations. The effects of loadings on the PbP and Yamamoto methods were compared. It was found that the PbP method had higher accuracy and robustness than the Yamamoto method. Moreover, a five-experiment strategy was suggested to implement the PbP method, which is straightforward to reduce the cost of calibration experiments. Finally, a real-world multi-component separation was challenged and further confirmed the feasibility of the PbP method. In general, the proposed method can not only reliably estimate the SMA parameters with comprehensive physical understanding but also accurately predict retention over a wide range of loading conditions.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
可爱的函函应助wvv采纳,获得10
刚刚
Hart发布了新的文献求助10
刚刚
qaz发布了新的文献求助10
2秒前
lerrygg发布了新的文献求助210
2秒前
NexusExplorer应助lucky采纳,获得10
3秒前
科研通AI6.3应助ywslby采纳,获得10
4秒前
标致的幼菱完成签到,获得积分10
4秒前
机智豆发布了新的文献求助10
5秒前
5秒前
zhqiao发布了新的文献求助10
5秒前
桐桐应助wanan采纳,获得10
5秒前
6秒前
纯真忆安发布了新的文献求助10
6秒前
8秒前
cc发布了新的文献求助10
8秒前
Doris发布了新的文献求助10
8秒前
9秒前
11秒前
qaz完成签到,获得积分20
11秒前
毕光发布了新的文献求助10
12秒前
13秒前
小李完成签到 ,获得积分10
14秒前
15秒前
CodeCraft应助欣慰的热狗采纳,获得10
15秒前
Akim应助cc采纳,获得10
15秒前
15秒前
风趣的寻凝完成签到 ,获得积分10
15秒前
赘婿应助Lucia_yx采纳,获得10
15秒前
FashionBoy应助qaz采纳,获得10
15秒前
16秒前
kwj关注了科研通微信公众号
17秒前
18秒前
Adrenaline发布了新的文献求助10
18秒前
18秒前
顾矜应助wxtlzzdp采纳,获得10
19秒前
19秒前
vivian发布了新的文献求助10
19秒前
20秒前
Tiger完成签到,获得积分10
21秒前
lifeboast发布了新的文献求助10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Principles of town planning : translating concepts to applications 500
Wearable Exoskeleton Systems, 2nd Edition 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6057765
求助须知:如何正确求助?哪些是违规求助? 7890548
关于积分的说明 16295204
捐赠科研通 5202834
什么是DOI,文献DOI怎么找? 2783696
邀请新用户注册赠送积分活动 1766369
关于科研通互助平台的介绍 1647012