Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery

This study used response surface methodology (RSM) and artificial neural network (ANN) to predict and optimize lithium polysulfide (LiP) adsorption on nitrogen-doped activated carbon (NDAC). Firstly, the NDAC production from palm kernel shell was optimized using RSM, where statistical analysis indic...

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Published in:JOURNAL OF ENERGY STORAGE
Main Authors: Zaini, Mohd Saufi Md; Ali, Ab Malik Marwan; Long, Xiangyi; Syed-Hassan, Syed Shatir A.
Format: Article
Language:English
Published: ELSEVIER 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001286581400001
author Zaini
Mohd Saufi Md; Ali
Ab Malik Marwan; Long
Xiangyi; Syed-Hassan
Syed Shatir A.
spellingShingle Zaini
Mohd Saufi Md; Ali
Ab Malik Marwan; Long
Xiangyi; Syed-Hassan
Syed Shatir A.
Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
Energy & Fuels
author_facet Zaini
Mohd Saufi Md; Ali
Ab Malik Marwan; Long
Xiangyi; Syed-Hassan
Syed Shatir A.
author_sort Zaini
spelling Zaini, Mohd Saufi Md; Ali, Ab Malik Marwan; Long, Xiangyi; Syed-Hassan, Syed Shatir A.
Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
JOURNAL OF ENERGY STORAGE
English
Article
This study used response surface methodology (RSM) and artificial neural network (ANN) to predict and optimize lithium polysulfide (LiP) adsorption on nitrogen-doped activated carbon (NDAC). Firstly, the NDAC production from palm kernel shell was optimized using RSM, where statistical analysis indicated the best conditions to be an impregnation ratio (IR) of 2.0, an activation temperature of 880 degrees C, and an activation time of 80 min-with IR having the most significant impact on LiP adsorption. Experimental results from the RSM were then used to train the predictive capabilities of the ANN for LiP adsorption. Although both approaches effectively predicted the adsorption process, ANN exhibited a superior prediction accuracy, characterized by a higher coefficient of determination (R2) 2 ) and a lower mean square error (MSE). The NDAC synthesized under optimized conditions was subsequently made into a cathode composite with sulfur (NDAC/S) and evaluated for its performance in a lithium-sulfur (Li-S) battery. Experimental data indicated that the Li-S coin cell battery consisting of NDAC/S had a remarkable initial specific capacity of 1054.96 mAh/g and maintained a favorable capacity retention of 66 % after 100 cycles at 0.1C. This outstanding electrochemical performance is attributed to the synergistic effect of a hierarchical pore structure, large surface area, substantial pore volume, and the presence of doped nitrogen that provides strong chemical bonding with LiP.
ELSEVIER
2352-152X
2352-1538
2024
98

10.1016/j.est.2024.113141
Energy & Fuels

WOS:001286581400001
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001286581400001
title Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
title_short Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
title_full Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
title_fullStr Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
title_full_unstemmed Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
title_sort Optimizing the preparation of palm kernel shell activated carbon for lithium polysulfide adsorption using response surface methodology and artificial neural network for high performance lithium-sulfur battery
container_title JOURNAL OF ENERGY STORAGE
language English
format Article
description This study used response surface methodology (RSM) and artificial neural network (ANN) to predict and optimize lithium polysulfide (LiP) adsorption on nitrogen-doped activated carbon (NDAC). Firstly, the NDAC production from palm kernel shell was optimized using RSM, where statistical analysis indicated the best conditions to be an impregnation ratio (IR) of 2.0, an activation temperature of 880 degrees C, and an activation time of 80 min-with IR having the most significant impact on LiP adsorption. Experimental results from the RSM were then used to train the predictive capabilities of the ANN for LiP adsorption. Although both approaches effectively predicted the adsorption process, ANN exhibited a superior prediction accuracy, characterized by a higher coefficient of determination (R2) 2 ) and a lower mean square error (MSE). The NDAC synthesized under optimized conditions was subsequently made into a cathode composite with sulfur (NDAC/S) and evaluated for its performance in a lithium-sulfur (Li-S) battery. Experimental data indicated that the Li-S coin cell battery consisting of NDAC/S had a remarkable initial specific capacity of 1054.96 mAh/g and maintained a favorable capacity retention of 66 % after 100 cycles at 0.1C. This outstanding electrochemical performance is attributed to the synergistic effect of a hierarchical pore structure, large surface area, substantial pore volume, and the presence of doped nitrogen that provides strong chemical bonding with LiP.
publisher ELSEVIER
issn 2352-152X
2352-1538
publishDate 2024
container_volume 98
container_issue
doi_str_mv 10.1016/j.est.2024.113141
topic Energy & Fuels
topic_facet Energy & Fuels
accesstype
id WOS:001286581400001
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001286581400001
record_format wos
collection Web of Science (WoS)
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