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Open AccessArticle

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

1
Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
Department of Computer and Communication System Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Processes 2019, 7(10), 704; https://doi.org/10.3390/pr7100704
Received: 30 July 2019 / Revised: 28 August 2019 / Accepted: 30 August 2019 / Published: 5 October 2019
(This article belongs to the Special Issue Chemical Process Design, Simulation and Optimization)
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. View Full-Text
Keywords: tapioca; response surface methodology; artificial neural network; carbon dots; hydrothermal; photoluminescence; organic tapioca; response surface methodology; artificial neural network; carbon dots; hydrothermal; photoluminescence; organic
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MDPI and ACS Style

Yahaya Pudza, M.; Zainal Abidin, Z.; Abdul Rashid, S.; Md Yasin, F.; Noor, A.S.M.; Issa, M.A. Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network. Processes 2019, 7, 704.

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