Predicting Sorbent-Air Partition Coefficients for Terpenoids at Multiple Temperatures

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Physical Chemistry

Background:

• Partition coefficients are crucial for designing air samplers, particularly the sorbent-air partition coefficient.
• Predicting partitioning behavior across various temperatures is essential for accurate air sampling.
• Plant-derived terpenes and terpenoids are important volatile organic compounds with partitioning behavior influenced by their chemical structure.

Purpose of the Study:

• To develop a predictive model for the polydimethylsiloxane (PDMS)/air partition coefficient (K_PDMS/AIR) of terpenes and terpenoids.
• To incorporate temperature dependence into the prediction of K_PDMS/AIR using a group contribution method and the van't Hoff equation.
• To validate the model's accuracy against experimental data for a range of compounds and temperatures.

Main Methods:

• Developed a group contribution model that explicitly includes the van't Hoff equation.
• Trained the model on 360 compounds containing carbon, hydrogen, and oxygen.
• Validated predictions against literature data for 50 C10 compounds, including terpenes and terpenoids, at 100 °C and 25 °C.

Main Results:

• The model achieved a strong correlation (R² > 0.987) for training compounds across temperatures from 60 °C to 200 °C.
• Validation against literature data at 100 °C yielded an average relative error of 3.1%.
• The model demonstrates good predictive capability for K_PDMS/AIR at various temperatures, including 25 °C.

Conclusions:

• The developed group contribution model provides accurate predictions of K_PDMS/AIR for terpenes and terpenoids.
• This modeling approach is highly beneficial for estimating partitioning properties when experimental data is scarce.
• The method facilitates improved design and performance of air sampling devices for volatile organic compounds.