Cocrystallization and Predictions of Cocrystal Structure Based on Electronegativity Calculations
School Name
Governor's School for Science and Mathematics
Grade Level
12th Grade
Presentation Topic
Chemistry
Presentation Type
Mentored
Oral Presentation Award
1st Place
Abstract
In this research, the electronegativity of several compounds was calculated by a computer program and cocrystals were successfully grown. The compounds were assembled in the program, which output the information with which predictions were made pertaining to the structure of the cocrystals before their growth. Two observable crystals were created: one of 1,3-bis(pyridine-4-ylmethyl)urea and (E)-3-(perfluorophenyl)acrylic acid, and one of 1,3-bis(pyridine-4-ylmethyl)urea and 4,4’-dihydroxybenzophenone. The general predictions made by the group proved to be accurate, thus validating the predictions. Further research can be done to pre-emptively predict structures in more complex scenarios.
Recommended Citation
Richburg, Thomas, "Cocrystallization and Predictions of Cocrystal Structure Based on Electronegativity Calculations" (2018). South Carolina Junior Academy of Science. 27.
https://scholarexchange.furman.edu/scjas/2018/all/27
Location
Neville 106
Start Date
4-14-2018 11:45 AM
Presentation Format
Oral and Written
Cocrystallization and Predictions of Cocrystal Structure Based on Electronegativity Calculations
Neville 106
In this research, the electronegativity of several compounds was calculated by a computer program and cocrystals were successfully grown. The compounds were assembled in the program, which output the information with which predictions were made pertaining to the structure of the cocrystals before their growth. Two observable crystals were created: one of 1,3-bis(pyridine-4-ylmethyl)urea and (E)-3-(perfluorophenyl)acrylic acid, and one of 1,3-bis(pyridine-4-ylmethyl)urea and 4,4’-dihydroxybenzophenone. The general predictions made by the group proved to be accurate, thus validating the predictions. Further research can be done to pre-emptively predict structures in more complex scenarios.
