Abstract

2D materials, such as transition metal dichalcogenides (TMDCs), have garnered considerable attention in recent years due to their unique properties and wide-ranging potential applications. Among them, molybdenum disulfide (MoS2) stands out for its remarkable electronic, optical, and mechanical characteristics. This study aims to optimize the synthesis of liquid-phase exfoliated MoS2 using ultrasound, focusing on absorbance in the UV–Vis spectrum and the increase in the direct bandgap. The variables studied in this research include ultrasound power and time, as well as the mass of MoS2, while the response variables involve the area under the curve (absorbance) of excitonic transitions A–D from UV–Vis spectra and the direct bandgap values of MoS2 A–D excitons obtained through Tauc-Mott models. To predict the optical properties of exfoliated MoS2, we developed Artificial Neural Network (ANN) algorithms, which were subsequently optimized using a Genetic Algorithm (GA). The performance of the ANN models was assessed using Root Mean Square Error (RMSE) and Standard Error of Prediction (SEP). The results demonstrate that the combined GA-ANN model serves as a valuable tool for predicting the optical properties of exfoliated MoS2 nanosheets under various experimental conditions. The selected treatments from the optimization process were further characterized using Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy, providing additional insights into and correlating with the optical properties. Characterizations through TEM and SEM confirmed the effectiveness of ultrasonic exfoliation in reducing the size of MoS2 particles and generating smaller particles with varied shapes, including thin flakes. The XRD and Raman spectroscopy analyses revealed changes in the crystalline structure, particle size distribution, and molecular composition of exfoliated MoS2 selected samples.

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