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

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 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.

Modified election algorithm in hopfield neural network for optimal randomksatisfiability representation

“…Several studies [16,28,[33][34][35][36][37][38] defined t j = 0 to verify that the HNN always leads to a decrease in energy monotonically. Each time neuron was connected with U jk , the value of the synaptic connection will be preserved as a stored pattern in an interconnected vector where U ð1Þ ¼ ½U ð1Þ jk n  n and U ð2Þ ¼ ½U [16,20] that the constraint of synaptic weight matrix U (1) and does not allow self-loop neuron connection U jk . The HNN energy dynamics function and CAM offers a versatile system with high capacity, error tolerance, rapid memory recovery and partial inputs [20,31,32].…”

“…Artificial neural networks (ANNs) belong to the family of the computational architectural-based model, viewed as equivalent to a brains' programming by imitating its design and attempts to mimic nervous system activity through which information is handled by the brains [1]. It consists of many basic processing components (called artificial neurons) that are loosely based on biological neurons.…”

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

“…Several studies [28,[42][43][44][45][46][47][48] defined 0 j  = to verify that the HNN always leads to a decrease in energy monotonically. Each time neuron was connected with jk U ,the value of the connection will be preserved as a stored pattern in an interconnected vector where [36,60] that the constraint of synaptic weight matrix (1) U and does not allow self-loop neuron connection…”

“…The ICA consists of two primary phases, the movement of the colonies and imperialist competi tion. The ICA begins by generating a random population of countries in the solution space in the form of a variable vector i S where ( ) [ 1,1] i St− is initiated. Select best countries in the solution space to serve as imperialists and the remaining becomes the colonies of the imperialist.…”

Special Issue on “Chemical Process Design, Simulation and Optimization”

“…By original process, we mean processes that are rarely studied from a process point of view and/or that recently emerged. Among these, the process synthesis of carbon dots studied by Pudza et al [16] can be ranked. A part of the article deals with the chemical synthesis of these carbon dots and has little connection with process engineering.…”