Is one performing the treatment data of adsorption kinetics correctly?

动力学 吸附 非线性系统 热力学 动能 数学 曲线拟合 应用数学 反应速率常数 线性模型 常量(计算机编程) 化学 统计 物理 物理化学 计算机科学 经典力学 量子力学 程序设计语言
作者
Éder C. Lima,Farooq Sher,Ashish Guleria,Mohammad Reza Saeb,Ioannis Anastopoulos,Hai Nguyen Tran,Ahmad Hosseini‐Bandegharaei
出处
期刊:Journal of environmental chemical engineering [Elsevier BV]
卷期号:9 (2): 104813-104813 被引量:267
标识
DOI:10.1016/j.jece.2020.104813
摘要

In the literature, the linear form of the pseudo-first-order (PFO) and pseudo-second-order (PSO) models are often applied for fitting the data of adsorption kinetics. Many authors have applied the linear form of the PSO model and concluded that such a kinetics is better fitted, based on the values of adsorption capacity at the equilibrium (qe) and the high value (which should be close to 1.0) of the coefficient of determination (R2). The linearized PFO model is usually ruled-out because the values of qe and R2 are worse than those obtained by the linearized PSO. On the other hand, the nonlinear fitting of data is highly recommended for the use of equations that are not typically linear such as kinetics data. In this communication, the data of 52 articles (containing 225 experiments of adsorption kinetics) were collected, and the kinetic data were treated using the linear and nonlinear PFO and PSO models. Results indicated that the values of k2 (the rate constant of the PSO model) calculated from the nonlinear fitting method were quite different from those acquired from the linear one. However, the values of qe2 (adsorption capacity at the equilibrium of the PSO model) are in complete agreement, which induces users to an erroneous decision. Using a linearized kinetic model, all the 225 values of R2 of the PSO model were closer to 1.0 than PFO. However, when nonlinearized fitting of the data was used, 122 out of 225 cases (54.22%) showed that the nonlinear PFO is better fitted than the PSO kinetic model.
最长约 10秒,即可获得该文献文件

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
QQQQ发布了新的文献求助10
刚刚
刚刚
打打的应助被淡定的路人采纳,获得10
刚刚
我是老大的应助被10086wm采纳,获得10
1秒前
AlexiaCY完成签到,获得积分10
1秒前
动人的发布了新的文献求助10
1秒前
赘婿的应助被小竹笋采纳,获得10
2秒前
2秒前
桐桐的应助被zz采纳,获得10
2秒前
xiaoXu完成签到,获得积分10
3秒前
songchaohui完成签到,获得积分10
3秒前
李健的应助被11采纳,获得10
3秒前
笨笨衫完成签到,获得积分20
3秒前
Owen的应助被超级笨比采纳,获得10
4秒前
怕孤独的天蓝完成签到,获得积分10
4秒前
花开富贵1完成签到,获得积分10
4秒前
bkagyin的应助被堀沢采纳,获得10
4秒前
淡然寒蕾发布了新的文献求助10
5秒前
5秒前
小小雪完成签到 ,获得积分10
5秒前
漂亮天思发布了新的文献求助30
5秒前
美羊羊发布了新的文献求助10
5秒前
6秒前
6秒前
LING发布了新的文献求助10
6秒前
6秒前
Jasper的应助被kwi采纳,获得10
6秒前
6秒前
花海发布了新的文献求助10
6秒前
7秒前
yaozhr完成签到,获得积分10
7秒前
DJ国完成签到,获得积分10
7秒前
烟花的应助被ne采纳,获得10
8秒前
Whalefall的应助被whuhustwit采纳,获得10
8秒前
丘比特的应助被陈静123采纳,获得30
8秒前
8秒前
无花果的应助被handsomer07采纳,获得10
9秒前
9秒前
9秒前
DMPK发布了新的文献求助10
9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
Encyclopedia of Modern Artificial Intelligence 1000
Das Matthäusevangelium: Kapitel 1:1–17 1000
IRTSD: Open-Source Data and Toolset for Electromagnetic Transient Analysis of Disturbances and IBR Control Malfunctions in Transmission Systems 1000
Transcriptional Profiling Shows that Gcn4p Is a Master Regulator of Gene Expression during Amino Acid Starvation in Yeast 750
Entre Praga y Madrid : los contactos checoslovaco-españoles (1948-1977) 600
A REVIEW OF THE V-22 DYNAMICS VALIDATION PROGRAM 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8126653
求助须知:如何正确求助?哪些是违规求助? 9529702
关于积分的说明 21580738
捐赠科研通 7707017
什么是DOI,文献DOI怎么找? 3416836
关于科研通互助平台的介绍 2542490
邀请新用户注册赠送积分活动 2457976