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

**Musa Yahaya Pudza 1,\*, Zurina Zainal Abidin 1,\*, Suraya Abdul Rashid 1, Faizah Md Yasin 1, Ahmad Shukri Muhammad Noor 2 and Mohammed A. Issa 1**


Received: 30 July 2019; Accepted: 30 August 2019; Published: 5 October 2019

**Abstract:** 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 ge<sup>t</sup> 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 agreemen<sup>t</sup> 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.

**Keywords:** tapioca; response surface methodology; artificial neural network; carbon dots; hydrothermal; photoluminescence; organic
