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 Author: Md Zaini M.S.; Ali A.M.M.; Long X.; Syed-Hassan S.S.A.
Format: Article
Language:English
Published: Elsevier Ltd 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199874847&doi=10.1016%2fj.est.2024.113141&partnerID=40&md5=8f55dcbcf5a680d40e154460e616f2e2
id 2-s2.0-85199874847
spelling 2-s2.0-85199874847
Md Zaini M.S.; Ali A.M.M.; Long X.; Syed-Hassan S.S.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
2024
Journal of Energy Storage
98

10.1016/j.est.2024.113141
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199874847&doi=10.1016%2fj.est.2024.113141&partnerID=40&md5=8f55dcbcf5a680d40e154460e616f2e2
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 °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) 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. © 2024 Elsevier Ltd
Elsevier Ltd
2352152X
English
Article

author Md Zaini M.S.; Ali A.M.M.; Long X.; Syed-Hassan S.S.A.
spellingShingle Md Zaini M.S.; Ali A.M.M.; Long X.; Syed-Hassan S.S.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
author_facet Md Zaini M.S.; Ali A.M.M.; Long X.; Syed-Hassan S.S.A.
author_sort Md Zaini M.S.; Ali A.M.M.; Long X.; Syed-Hassan S.S.A.
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
publishDate 2024
container_title Journal of Energy Storage
container_volume 98
container_issue
doi_str_mv 10.1016/j.est.2024.113141
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199874847&doi=10.1016%2fj.est.2024.113141&partnerID=40&md5=8f55dcbcf5a680d40e154460e616f2e2
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 °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) 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. © 2024 Elsevier Ltd
publisher Elsevier Ltd
issn 2352152X
language English
format Article
accesstype
record_format scopus
collection Scopus
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