The robot can perform basic chemistry experiments without human intervention.
A pair of students from Carnegie Mellon University (CMU) are pushing to modernize scientific research by automating laboratory processes using robots. The robot developed by mechanical engineering Ph.D. student Morgan Chen and Masters student in Machine Learning Ari Fiorino is capable of conducting basic chemistry experiments without the need for human intervention. The technology is designed to make repetitive laboratory work and experimentation more efficient, allowing researchers to allocate more time towards creative tasks. By using automated systems, researchers can improve experimental processes and lower laboratory costs.
According to Chen, the shift to remote laboratory work due to the COVID-19 pandemic saw a need for more automation technology. Cost and specificity are the primary challenges to laboratory automation. To address this, Chen took inspiration from the modular design created by Daniel Salley of the Cronin Group at the University of Glasgow. Salley’s modular wheel platform (MWP) is a liquid-handling device that allows for the addition and modification of functions as needed. The automation of these synthetic processes, controlled by digital code and combined with the plug-and-play functionality of the platform, is designed to provide the MWP with wide-ranging utility.
With no access to advanced material synthesis equipment, Chen constructed a similar device using commercial parts available online. Meanwhile, Fiorino developed the algorithms that were used in the robot. Chen stressed how through innovation and the use of modular design, this kind of technology can be accessible to more laboratories.
“If I can build this during a pandemic, then it’s definitely affordable and doable by the average scientist,” Chen shared with CMU’s College of Engineering.
Laboratory automation is aimed at using lower quantities of reagents while producing higher throughput of experiments. In addition to that, it can increase data reliability and accuracy. Since human error and variability can occur during each stage of the experimental process, automation can ensure increased consistency—particularly when it comes to repetitive, time-consuming tasks.
“Robots with the ability to handle monotonous benchmark research that is inherent in material science will be a tool to complement the human researcher,” says Chen.
The students are currently working with Reeja Jayan, an associate professor of mechanical engineering, in collaboration with the Air Force Research Laboratory (AFRL) through the Data-Driven Discovery of Optimized Multifunctional Material System (D3OM2S) Center of Excellence. They will be using this robot to further explore autonomous laboratory experimentation in the AFRL center. The robot has already successfully completed a range of tasks such as pH optimization and synthesizing gold nanoparticles. Chen is hoping that it will eventually be able to autonomously conceive and run intelligent experiments in line with his groups’ research goals, such as understanding the effects of electromagnetic radiation on materials on an atomic scale.
Student innovation projects such as this encourage collaboration and creative problem-solving, enhancing their understanding of concepts beyond the classroom. This way, students are exposed to the real-life application and implementation of various subjects. According to the Future of Jobs Report 2020 from the World Economic Forum, industries identified analytical thinking and innovation, complex problem solving, and creativity as top skills of 2025.
Thanks to advances in technology and the ever-competitive engineering job market, universities need to adapt just as quickly to offer students an education that will prepare them for the inevitable challenges and opportunities they will face after graduation. Engaging students in research projects such as the one spearheaded by Chen can further equip them with the necessary skills and experiences needed for their future careers.