[CCoE Notice] Thesis Announcement: Utkarsh Prabhakar Gupta, "Self-Driving Laboratories for Autonomous Discovery of New Materials"

[Greenwell, Stephen J]

[Thesis Defense Announcement at the Cullen College of Engineering]
Self-Driving Laboratories for Autonomous Discovery of New Materials

Utkarsh Prabhakar Gupta
April 29, 2025, 12:30 p.m. to 2:00 p.m. (CST)
Location: D N328
Committee Chair:
Xiaonan Shan, Ph.D.
Committee Members:
Jiefu Chen, Ph.D. | Jiming Bao, Ph.D.
Abstract
The discovery of efficient electrocatalysts is vital for enabling clean energy technologies such as water splitting and hydrogen production. However, conventional materials development methods are slow, labor-intensive, and unsuitable for navigating the complex design space of multi-metallic catalysts. This thesis presents the design and implementation of a fully automated self-driving laboratory platform for the autonomous synthesis and electrochemical evaluation of electrocatalysts.
The system integrates Python-controlled SCARA robotic arms with custom-designed mechanical and electronic components, including 3D-printed electrode holders and a lab-fabricated PCB. All experimental steps-solution preparation, electrode loading, electrochemical deposition, rinsing, and performance testing-are automated. A single-channel CHI 660E potentiostat performs cyclic voltammetry (CV), chronoamperometry (CP), and electrochemical impedance spectroscopy (EIS) to evaluate catalyst behavior.
Using this platform, binary and ternary metal catalysts such as NiFe, NiFeCo, and NiMoP have been synthesized and tested in alkaline media for both the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). A custom data processing pipeline applies iR correction, extracts key performance metrics (e.g., overpotential at 10 mA/cm²), and generates ternary visualizations of composition-performance trends. While catalyst compositions are currently user-defined, the system is structured for future integration with machine learning models to enable closed-loop optimization.
This work provides a scalable and reproducible framework to accelerate electrocatalyst discovery by reducing human intervention, increasing throughput, and enabling systematic exploration of complex chemical systems.

[Engineered For What's Next]



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