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Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network

Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get...

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Main Authors: Pudza, Musa Yahaya, Zainal Abidin, Zurina, Abdul Rashid, Suraya, Md. Yasin, Faizah, Muhammad Noor, Ahmad Shukri, Issa, Mohammed Abdullah
Format: Article
Language:English
Published: MDPI 2019
Online Access:http://psasir.upm.edu.my/id/eprint/38243/1/38243.pdf
http://psasir.upm.edu.my/id/eprint/38243/
https://www.mdpi.com/2227-9717/7/10/704
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spelling my.upm.eprints.382432020-05-04T16:06:24Z http://psasir.upm.edu.my/id/eprint/38243/ Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network Pudza, Musa Yahaya Zainal Abidin, Zurina Abdul Rashid, Suraya Md. Yasin, Faizah Muhammad Noor, Ahmad Shukri Issa, Mohammed Abdullah Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route. MDPI 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38243/1/38243.pdf Pudza, Musa Yahaya and Zainal Abidin, Zurina and Abdul Rashid, Suraya and Md. Yasin, Faizah and Muhammad Noor, Ahmad Shukri and Issa, Mohammed Abdullah (2019) Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network. Processes, 7 (10). art. no. 704. pp. 1-19. ISSN 2227-9717 https://www.mdpi.com/2227-9717/7/10/704 10.3390/pr7100704
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route.
format Article
author Pudza, Musa Yahaya
Zainal Abidin, Zurina
Abdul Rashid, Suraya
Md. Yasin, Faizah
Muhammad Noor, Ahmad Shukri
Issa, Mohammed Abdullah
spellingShingle Pudza, Musa Yahaya
Zainal Abidin, Zurina
Abdul Rashid, Suraya
Md. Yasin, Faizah
Muhammad Noor, Ahmad Shukri
Issa, Mohammed Abdullah
Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
author_facet Pudza, Musa Yahaya
Zainal Abidin, Zurina
Abdul Rashid, Suraya
Md. Yasin, Faizah
Muhammad Noor, Ahmad Shukri
Issa, Mohammed Abdullah
author_sort Pudza, Musa Yahaya
title Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
title_short Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
title_full Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
title_fullStr Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
title_full_unstemmed Sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
title_sort sustainable synthesis processes for carbon dots through response surface methodology and artificial neural network
publisher MDPI
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/38243/1/38243.pdf
http://psasir.upm.edu.my/id/eprint/38243/
https://www.mdpi.com/2227-9717/7/10/704
_version_ 1665895967502630912
score 13.211869

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