This workflow performs principal component analysis (PCA) on a large operando X-ray powder diffraction (OXRPD) dataset stored in JSON format. The dataset contains over 92,000 diffraction patterns collected from multiple contributing institutions and is designed to facilitate the exploration of structural relationships, phase evolution, and dominant diffraction features within a large-scale diffraction database. The analysis begins by extracting the dataset and importing all diffraction patterns. Each pattern is interpolated onto a common 2θ grid spanning 5° to 80° with a step size of 0.05° to ensure consistency across samples. Intensities are normalized to their maximum values, allowing comparisons that emphasize structural characteristics rather than absolute signal magnitude. Samples containing missing values after interpolation are removed prior to multivariate analysis. Principal Component Analysis (PCA) is then applied to the processed diffraction matrix to reduce dimensionality while retaining the major sources of structural variation. The workflow generates cumulative variance-exained plots, PCA score plots, and trajectory visualizations that reveal similarities and differences among diffraction patterns and enable investigation of structural evolution across the dataset. To identify possible phase transitions or major structural changes, changepoint analysis is performed on the first principal component (PC1) using the Pruned Exact Linear Time (PELT) algorithm. Detected changepoints partition the dataset into distinct phase regions that are subsequently visualized in PCA space. This provides an automated approach for identifying transitions in diffraction behavior across the sequence of samples. The workflow further examines PCA loadings to determine which diffraction angles contribute most strongly to the observed variance. Peaks with the largest absolute PC1 loadings are extracted and reported as candidate diffraction features associated with structural transformations. Results are exported as CSV files containing PCA scores and the most influential diffraction peak positions for downstream statistical analysis and interpretation. Outputs generated by the workflow include: Cumulative variance explained by principal components PCA score plots colored by source institution Structural evolution trajectories in PCA space Phase-transition detection using changepoint analysis PC1 loading profiles identifying influential diffraction features CSV files containing PCA scores and important diffraction peak positions Identification of diffraction peaks contributing most strongly to structural variation This workflow provides a scalable framework for exploring large OXRPD datasets and facilitates the identification of structural trends, phase evolution pathways, and diffraction features associated with material transformations. Dataset citation: OXRPD Dataset, Zenodo Record 15298026, accessed June 2026. Zenodo Record 15298026

This report presents an R-based workflow for processing GSAS XYE diffraction data and applying multivariate analysis to detect phase transitions. The pipeline includes data import, normalization using incident intensity and channel width corrections, and automated batch processing of multiple datasets. Principal Component Analysis (PCA) is used to capture structural variation across diffraction patterns, and change-point detection is applied to identify phase transitions. This approach enables scalable and data-driven analysis of large diffraction datasets.

The objective of this study is to optimize the ethanol content produced from fresh banana stems (Musa paradisiaca Linn) using Box–Behnken Design (BBD) coupled with Response Surface Methodology (RSM). The study aims to determine the optimal combination of fermentation parameters specifically commercial dry yeast loading (g/L), fermentation time (days), and fermentation temperature (°C) that maximizes ethanol yield. Prior to this optimization, the hydrolysis stage was independently optimized by evaluating different sulfuric acid concentrations, and the optimal condition was selected and held constant throughout the fermentation experiments. Under these controlled hydrolysis conditions, the fermentation process is systematically investigated to quantify the main, interaction, and quadratic effects of the selected variables on ethanol production. A sequential experimental design approach was employed. Initially, key process parameters were identified and preliminary conditions optimized. Subsequently, a Box–Behnken design (BBD) within the framework of response surface methodology (RSM) was applied to evaluate the effects of yeast loading, fermentation time, and temperature on ethanol production. This approach enabled the modeling of quadratic and interaction effects for process optimization.

This R Markdown-generated report documents the statistical analysis and optimization of clay brick tensile strength using Box–Behnken Design and Response Surface Methodology. The workflow includes experimental design generation, factor level transformation, model development, analysis of variance, and identification of optimal process parameters.