Abstract:

This work explores using high-throughput methods, real-time data capture, and automated analysis to enhance the student learning experience in applied college-level biochemical engineering lab classes. In the past, engineering laboratory classes were structured with a pre-lab lecture, one uniform assigned experiment, and data analysis done at a later date and then reported back. Additionally, traditional equipment setups such as shake flasks and bioreactors offer little real-time information and experimental setup can require a large amount of time and materials. Furthermore, data collection from traditional setups such as shake flasks often disturb the system under study.

In this work, we present three methods for the use of high-throughput systems and parallel experiments with online sensors to enable fresh approaches to teaching the applied college-level biochemical engineering lab. The use of high-throughput micro-bioreactor systems facilitates multiple simultaneous experiments that require tenfold smaller amounts of consumables and reduces set-up and sampling time by two-fold, while also increasing the amount of valuable data obtained. Additionally, these methods allow for students to engage directly in the experimental process design and to learn through an iterative and collaborative design process, therefore enhancing student engagement and facilitating the development of higher-order thinking. The results to date indicate substantial student engagement, higher-order process design understanding, and enhanced technical laboratory skills.

Keywords:

High-throughput, real-time, data analysis, learning.