Chitosan-minerals-based composites for adsorption of caesium, cobalt and europium

吸附 环氧氯丙烷 材料科学 傅里叶变换红外光谱 复合材料 朗缪尔吸附模型 化学工程 化学 核化学 离子 有机化学 冶金 工程类
作者
Galina Lujanienė,Raman Novikau,Karolina Karalevičiūtė,Vidas Pakštas,Martynas Talaikis,Loreta Levinskaitė,Aušra Selskienė,Algirdas Selskis,Jonas Mažeika,Kęstutis Jokšas
出处
期刊:Journal of Hazardous Materials [Elsevier BV]
卷期号:462: 132747-132747 被引量:51
标识
DOI:10.1016/j.jhazmat.2023.132747
摘要

Currently, there is a growing interest in the use of natural materials in various fields of science, technology and environmental protection due to their availability, low-cost, non-toxicity and biodegradability. Chitosan, natural clay of local origin, montmorillonite, zeolite, cross-linking agents (epichlorohydrin, sodium tripolyphosphate, glutaraldehyde) and plasticisers (glycerol) were used to synthesise composites. The composites were characterised by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray diffraction analysis (XRD) and scanning electron microscope (SEM), tested for their antibacterial activity and used in batch experiments to study the adsorption of caesium, cobalt and europium ions. The maximum capacities for adsorption of caesium, cobalt and europium on the composites were 1400 mg/g, 900 mg/g and 18 mg/g, respectively. The experimental data fit better the Langmuir isotherm model and indicate favourable monolayer adsorption of Cs+, Co2+ and Eu3+ at homogeneous sites of the composites. The experimental data were in better agreement with the pseudo-second-order non-linear kinetic model for most elements and adsorbents. Adaptive neuro-fuzzy inference system proved to be a practical tool with good performance and generalisation capability for predicting the adsorption capacity of composites for caesium, cobalt, and europium ions. It was found that the predicted data were very close to the experimental data.
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
刚刚
1秒前
谨慎翎完成签到 ,获得积分10
1秒前
哟嚛发布了新的文献求助10
3秒前
3秒前
3秒前
周树人发布了新的文献求助10
4秒前
翁欣欣发布了新的文献求助10
5秒前
科研通AI6.4应助111111采纳,获得10
5秒前
5秒前
6秒前
铭铭子发布了新的文献求助10
6秒前
6秒前
6秒前
坚定谷蕊完成签到,获得积分10
7秒前
圈圈发布了新的文献求助10
7秒前
科研通AI6.2应助吉祥高趙采纳,获得10
9秒前
落后醉易完成签到,获得积分20
10秒前
酷波er应助墨菲采纳,获得10
10秒前
大大怪完成签到 ,获得积分10
11秒前
11秒前
Zjjj0812发布了新的文献求助10
11秒前
12秒前
复杂的鸿发布了新的文献求助10
12秒前
科研通AI6.2应助小蝴蝶采纳,获得10
14秒前
14秒前
16秒前
冷静无心发布了新的文献求助10
16秒前
贾山灵发布了新的文献求助10
17秒前
杭三问发布了新的文献求助10
17秒前
香蕉觅云应助lucky采纳,获得10
17秒前
完美世界应助哈哈采纳,获得10
17秒前
18秒前
19秒前
19秒前
20秒前
21秒前
铭铭子发布了新的文献求助10
23秒前
今后应助冷静无心采纳,获得10
23秒前
25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
Reducing Compassion Fatigue, Secondary Traumatic Stress and Burnout 600
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Mammalian Synthetic Biology 500
Auslegungsgeschichte 500
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7638299
求助须知:如何正确求助?哪些是违规求助? 9211617
关于积分的说明 19759396
捐赠科研通 7205313
什么是DOI,文献DOI怎么找? 3275838
关于科研通互助平台的介绍 2437432
邀请新用户注册赠送积分活动 2273029