XRD and SEM Characterization and Investigation of Effective Agents of Medicinal Plants and Nanoparticles: Machine Learning (ML) Analysis

This study focuses on the characterization and investigation of effective agents in medicinal plants and nanoparticles, aiming to understand their potential applications. X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques were employed to analyze the structural and morphological properties of the samples. XRD provided valuable information on crystalline phases, crystal structure, and lattice parameters, while SEM revealed surface morphology, particle size distribution, and aggregation behavior. These techniques facilitated a comprehensive understanding of the physical and chemical properties, crucial for effective utilization. Machine Learning (ML) analysis was employed to uncover patterns and correlations within the data. ML algorithms were used to identify significant features, establish predictive models, and gain insights into the relationships between sample properties and effective agents. This enhanced understanding of the factors influencing efficacy, paving the way for targeted applications. The study encompassed two main research areas. Firstly, a ML was developed to estimate Z, P |Z|, and the 95% confidence interval by manipulating coefficients (COEF) and robust standard errors (ROBUST STD.ERR) in wider intervals compared to the experimental samples. The study revealed a direct relationship between coefficients and robust standard errors, with increasing coefficients leading to higher robust standard errors and an expanded 95% confidence interval. Additionally, the study emphasized the significance of income from Chinese medicinal materials in the financing process for growers, as income variations impacted their willingness to finance technology adoption. By exploring the connection between technology adoption and financing, the research aimed to enhance understanding and logical linkage, contributing to more effective and sustainable agricultural development.