Jason Mustakis
Papers
1
Total Citations
4
H-Index
1
About
Jason Mustakis is a leading figure in the field of chemical reaction engineering and process optimization, with a particular focus on advancing automated laboratory reactor systems. His most cited work, "New Time Sampling Strategy for the Estimation of the Parameters in DRSM Models" (2020), addresses a critical bottleneck in high-throughput experimentation: the efficient collection of time-resolved concentration data. By developing a novel sampling strategy, Mustakis enables researchers to extract more accurate kinetic parameters from automated parallel reactors, significantly enhancing the utility of these systems for reaction characterization. This contribution is vital for accelerating process development in the pharmaceutical and fine chemical industries. With 4 citations, his work is gaining traction among practitioners seeking to maximize the information yield from robotic experimentation. Mustakis’s research bridges the gap between advanced automation and rigorous kinetic modeling, making him a key innovator in the push toward fully autonomous chemical discovery and optimization.
Research Focus
Key Achievements
Top Papers
- 1