Choolwe Mandona


Faculty Mentor: Jesse H. Kroll

Direct Supervisor: Anthony Carrasquillo

Home University: Miami Oxford University of Ohio

Major: Chemical Engineering



I am a senior studying Chemical Engineering with an Environmental Concentration at Miami Oxford University of Ohio. My research interests include environmental pollution and development of sustainable energy. My hobbies include cooking, watching movies, traveling, site-seeing and volunteering. My educational goal is to attend graduate school for a Master’s degree and possibly a Ph.D. in Environmental Engineering. I would like to focus on the environmental issues concerning air and water quality and eventually work with the United Nations Development Program addressing these environmental issues on a global scale.


Dissolution and Quantification of Gaseous Organic Compounds

Gas phase organic compounds in the earth’s atmosphere can play a major role in global climate and human health. Unfortunately, the large number and small concentrations of these gaseous compounds make it very difficult to collect and analyze, greatly limiting our understanding of their impacts. It is the goal of the project to design a means to concentrate and measure these species by continuous dissolution of the organic gases into solution using a simple bubbler setup. Initially, we developed a numerical model to estimate the concentrated mass as a function of Henry’s Law constants. The accuracy of this model’s predictability was tested using three compounds: 1-decanol, 1-heptanol and 1, 2-hexanediol. Gas-phase species were sampled through our bubbler and dissolved in water, following liquid-liquid extraction in hexane. The experimental mass of each compound in the water was quantified using GC-MS. The experimental data was then compared to model predictions which suggested an underestimation of the mass dissolved in the water in all cases. This comparison will be used in future research work to determine what parameters of both the model and bubbler should be modified in order for the experimental data to more closely fit the model.