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<p class="MsoNormal"><span style="font-family:"Aptos",sans-serif"><img width="600" height="171" style="width:6.25in;height:1.7812in" id="Picture_x0020_2" src="cid:image001.png@01DBAE0B.07FAECC0" alt="Thesis Defense Announcement at the Cullen College of Engineering"></span><span style="font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span lang="EN-IN" style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E">Self-Driving Laboratories for Autonomous Discovery of New Materials<o:p></o:p></span></b></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none"><o:p> </o:p></span></b></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none">Utkarsh Prabhakar Gupta</span></b><span style="font-size:11.0pt;font-family:"Times New Roman",serif;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">April 29, 2025, 12:30 p.m. to 2:00 p.m. (CST)<br>
Location: D N328<o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Xiaonan Shan, Ph.D. </span><span style="font-size:11.0pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Jiefu Chen, Ph.D. | Jiming Bao, Ph.D. </span><span style="font-size:10.5pt;font-family:"Aptos",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"><b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
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<span lang="EN-IN" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">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.<o:p></o:p></span></p>
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<span lang="EN-IN" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">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.<o:p></o:p></span></p>
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<span lang="EN-IN" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">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.<o:p></o:p></span></p>
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<span lang="EN-IN" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">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.<o:p></o:p></span></p>
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<span lang="EN-IN" style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p> </o:p></span></p>
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