Principal component analysis for automatic extraction of solid-state kinetics from combined in situ experiments.

Area of Science:

• Solid-state chemistry
• Materials science
• Chemical kinetics

Background:

• In situ experiments using multiple techniques are common for studying solid-state reactivity.
• Traditional analysis involves complex, time-consuming protocols prone to errors in reaction coordinate extraction and model selection.
• Existing methods lack unbiased approaches for selecting the best kinetic model.

Purpose of the Study:

• To develop a simplified and automated procedure for kinetic analysis of in situ solid-state reaction data.
• To overcome the limitations of traditional methods, including time consumption and potential for manual bias.
• To effectively manage and analyze large datasets from multi-technique in situ experiments.

Main Methods:

• A novel procedure based on principal component analysis (PCA) is proposed.
• PCA is applied to in situ data, treating it holistically rather than relying on individual peak intensity variations.
• The method was validated using in situ X-ray powder diffraction and Raman spectroscopy data for molecular complex formation.

Main Results:

• The PCA-based approach significantly simplifies kinetic parameter calculation, reducing it to two or even one step.
• The new procedure offers a high degree of automation, suitable for managing large in situ datasets.
• It eliminates biases associated with manual reaction coordinate extraction, providing a more robust analysis.

Conclusions:

• The proposed PCA-based method offers a more efficient, accurate, and automated approach to solid-state reaction kinetic analysis.
• This technique enhances the management and interpretation of complex in situ multi-technique experimental data.
• It provides a general and unbiased framework for kinetic analysis in solid-state chemistry.