Automation, Data Processing, and Validation of Spectral Measurements in Molecular Degradation Research

This project develops automated pipelines for processing Raman and UV-Vis spectral data collected during studies of molecular degradation. The focus is on improving reproducibility, accuracy, and efficiency in monitoring antioxidants such as BHT and PAN derivatives. The workflows will perform spectral baseline correction, peak fitting, smoothing, and feature extraction, enabling rapid interpretation of large experimental datasets. Validation against quantum chemical simulations ensures consistency between experimental observations and theoretical models. Automated routines will allow scalable, high-throughput analysis, reducing manual errors and accelerating discovery. Outcomes include standardized tools for processing spectroscopic measurements, integration with quantum-derived reference spectra, and improved reliability of degradation monitoring in industrial oils and complex matrices. Students will gain experience in coding, algorithm development, and data–experiment integration, strengthening their skills in spectroscopy, automation, and applied sensing technologies.

Faculty Supervisor:

Ronald Miller

Student:

Partner:

National University of Kharkiv

Discipline:

Physics

Sector:

Nanotechnology; Quantum Science; Technology

University:

Carleton University

Program: