I am interested in studying the underlying chemical mechanisms governing the conversion of waste to energy. Through the design and implementation of semi-batch reactor systems, I aim to develop microkinetic models describing bio-oil formation from food and green wastes. 

Through of the use of analytical chemistry techniques such as GC-MS, GCxGC, FT-IR, FT-ICR-MS, and others, I work to obtain the most complete picture of the biocrude and aqueous phase chemical identity for further understanding and process development.

Climate change is one of the most pressing issues in society today. The Earth's hottest summer on record ever is 2023! I believe that the application of green chemistry principles to engineering solutions is paramount to a sustainable future. Some of the key principles my research relates to are:

• Atom Economy- ensuring all parts of a reactant are used

• Safer Solvents & Auxiliaries- limiting use of additional chemicals and choosing the least hazardous solvents

• Design for Energy Efficiency- utilizing LCA and TEA metrics to assess the sustainability of the process

• Use of Renewable Feedstocks- waste and biomass are the feedstocks of the future! Carbon-rich, low impact.

• Catalysis- selective catalysts can reduce energy inputs and increase atom economy

• Real-time Monitoring for Pollution Control- using in situ and operando techniques for analysis of produced hazards

The future lies in continuous processes! I aim to study the transition from batch to flow due to:

• Improved process safety (i.e. low volumes, reduced solvent use)

• More efficient energy use

• Reduced heat & mass transfer limitations

• Potential for enhanced process tunability

Continuous systems also can be combined with in situ analytical techniques to obtain enhanced microkinetic data. My research aims to develop continuous processes with green chemistry in mind.

Science and engineering must adapt and utilize evolving technology. I aim to use computational models and machine learning to enhance and inform experimental work. By using large data sets to train machine learning algorithms, the number of required experiments can be reduced and enhanced reaction spaces identified. Models can be used to predict:

• Reaction kinetics

• Product yields

• Reaction conditions (i.e. temperature, pressure, residence time)

• Selectivity

• Chemical structure

• Chemical pathways