Application of ANN and RSM for Rhodamine B and Safranine-O Decolorization on Zinc-Carbon Battery Waste Derived Ag/CoFe-LDH/rGO Catalyst.

IF 3.7 2区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Langmuir Pub Date : 2024-09-17 Epub Date: 2024-09-04 DOI:10.1021/acs.langmuir.4c02876
Ümit Ecer, Berdan Ulaş, Şakir Yılmaz
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引用次数: 0

Abstract

The present work is first aimed at recovering graphite from carbon rods of waste zinc-carbon (Zn-C) batteries for applications such as wastewater treatment, in order to contribute to the development of a sustainable environment. Then, a composite material, cobalt-iron layered double hydroxide combination with reduced graphene oxide, and with subsequent Ag nanoparticles deposition via NaBH4 reduction method (Ag/CoFe-LDH/rGO) was prepared for the catalytic activity of Rhodamine B (RhB) and Safranine-O (SO) as model contaminants from aquatic media. The catalytic activity of RhB and SO by Ag/CoFe-LDH/rGO in the presence of NaBH4 was studied to model and optimize the process parameters (NaBH4 amount, reaction time, initial dye concentration (Co), and catalyst dosage) via central composite design (CCD)-response surface methodology (RSM). Also, an artificial neural network (ANN) model was developed to estimate the catalytic activity of each dye using an RSM data set. The catalytic activities of 99.54% and 99.96% were obtained for RhB and SO dyes, respectively, under the optimal conditions: NaBH4 amount 12.32 mM, reaction time 3.19 min, Co 33.46 mg/L, and catalyst dosage 1.24 mg/mL for RhB dye; NaBH4 amount 16.76 mM, reaction time 3.06 min, Co 15.10 mg/L, and catalyst dosage 1.46 mg/mL for SO dye. The optimum conditions of process parameters by ANN with gray wolf optimizer (GWO) were in good agreement with the points determined the RSM-CCD. These results demonstrate that RSM and ANN approaches can be applied practically and efficiently to maximize the catalytic activity of RhB and SO by Ag/CoFe-LDH/rGO in the existence of NaBH4. On the other hand, from the kinetic and thermodynamic studies, the positive activation enthalpy, ΔH# and the negative activation entropy, ΔS# values for each dye demonstrated that the catalytic performance was endothermic and less random at the solid/liquid interface.

Abstract Image

应用 ANN 和 RSM 在锌碳电池废料衍生的 Ag/CoFe-LDH/rGO 催化剂上进行罗丹明 B 和 Safranine-O 脱色。
本研究首先旨在从废旧锌碳(Zn-C)电池的碳棒中回收石墨,用于废水处理等应用,为可持续环境的发展做出贡献。然后,制备了一种复合材料,即钴铁双层氢氧化物与还原氧化石墨烯的结合,并随后通过 NaBH4 还原法沉积了银纳米粒子(Ag/CoFe-LDH/rGO),用于催化水介质中的模型污染物罗丹明 B(RhB)和赛凡宁-O(SO)。研究了 Ag/CoFe-LDH/rGO 在 NaBH4 存在下对 RhB 和 SO 的催化活性,并通过中心复合设计(CCD)-响应面方法(RSM)对工艺参数(NaBH4 用量、反应时间、初始染料浓度(Co)和催化剂用量)进行建模和优化。此外,还开发了一个人工神经网络(ANN)模型,利用 RSM 数据集估算每种染料的催化活性。在最佳条件下,RhB 和 SO 染料的催化活性分别达到 99.54% 和 99.96%:对于 RhB 染料,NaBH4 用量为 12.32 mM,反应时间为 3.19 min,Co 用量为 33.46 mg/L,催化剂用量为 1.24 mg/mL;对于 SO 染料,NaBH4 用量为 16.76 mM,反应时间为 3.06 min,Co 用量为 15.10 mg/L,催化剂用量为 1.46 mg/mL。采用灰狼优化器(GWO)的 ANN 法得出的最佳工艺参数条件与 RSM-CCD 法确定的参数点十分吻合。这些结果表明,在 NaBH4 存在的情况下,RSM 和 ANN 方法可以实际有效地应用于 Ag/CoFe-LDH/rGO 对 RhB 和 SO 催化活性的最大化。另一方面,从动力学和热力学研究来看,每种染料的正活化焓 ΔH# 和负活化熵 ΔS# 值表明催化性能是内热的,在固/液界面上的随机性较小。
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来源期刊
Langmuir
Langmuir 化学-材料科学:综合
CiteScore
6.50
自引率
10.30%
发文量
1464
审稿时长
2.1 months
期刊介绍: Langmuir is an interdisciplinary journal publishing articles in the following subject categories: Colloids: surfactants and self-assembly, dispersions, emulsions, foams Interfaces: adsorption, reactions, films, forces Biological Interfaces: biocolloids, biomolecular and biomimetic materials Materials: nano- and mesostructured materials, polymers, gels, liquid crystals Electrochemistry: interfacial charge transfer, charge transport, electrocatalysis, electrokinetic phenomena, bioelectrochemistry Devices and Applications: sensors, fluidics, patterning, catalysis, photonic crystals However, when high-impact, original work is submitted that does not fit within the above categories, decisions to accept or decline such papers will be based on one criteria: What Would Irving Do? Langmuir ranks #2 in citations out of 136 journals in the category of Physical Chemistry with 113,157 total citations. The journal received an Impact Factor of 4.384*. This journal is also indexed in the categories of Materials Science (ranked #1) and Multidisciplinary Chemistry (ranked #5).
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