December 1 - 6, 2024
Boston, Massachusetts

Event Supporters

2024 MRS Fall Meeting & Exhibit
MT02.03.03

Designing a MAP for Discovery of Solid Polymer Electrolytes

When and Where

Dec 2, 2024
4:15pm - 4:30pm
Hynes, Level 2, Room 209

Presenter(s)

Co-Author(s)

Pablo Quijano Velasco1,Chang Jie Leong1,Eleen Koay1,Hazel Lau1,Kedar Hippalgaonkar2,1,Jayce Cheng1

Institute of Materials Research and Engineering1,Nanyang Technological University2

Abstract

Pablo Quijano Velasco1,Chang Jie Leong1,Eleen Koay1,Hazel Lau1,Kedar Hippalgaonkar2,1,Jayce Cheng1

Institute of Materials Research and Engineering1,Nanyang Technological University2
Materials Accelerated Platforms (MAPs) relying on high-throughput automated laboratory equipment can produce the large amounts of high-quality data needed to reap the benefits of data-hungry machine learning algorithms to reduce the time required to discover and optimize the properties of novel materials. To build a MAP, manual workflows are automated by integrating hardware and software that can execute the tasks normally performed by a scientist at each step of the material development process<sup>1</sup>. The challenges of integrating automatic synthesis, fabrication and characterization equipment increase substantially with each additional target property measurement. For this reason, using the right strategies to seamlessly interface different synthesis and characterization equipment is key to create successful workflows. Here, we describe heuristics used in designing <i>Pescador</i> a MAP for the development and discovery of polymer electrolytes for electrochemical energy storage with the goal to optimize mechanical and electrochemical properties for structural battery applications.<br/><br/>In this talk we will cover how we use an object-oriented hardware approach to design automated equipment and labware that ensure seamless conversion from manual to automated lab workflows. Our approach uses multiscale modularity in sample, array, and equipment to ensure agility and flexibility in our workflows. <i>Pescador</i> is built by integrating <i>Qubots, </i>which are spatially and temporally deconflicted Cartesian robots in the workspace capable to perform a specific set of tasks in the materials development process (<i>e.g. </i>formulation, synthesis, characterization). Each of the tasks performed by the <i>Qubots </i>are orchestrated using <i>control-lab-ly, </i>an in-house developed code library that allows seamless integration of hardware and execution of workflows in different platforms with minimal editions. This approach allows us to interface automated stations that perform each step of polymer electrolyte development including accurate and fast viscous liquid handling for the electrolyte formulation, reaction stage for UV photocuring, and testing stages for mechanical and electrochemical characterization.<br/><br/>References<br/>1. M. Christensen, L. P. E. Yunker, P. Shiri, T. Zepel, P. L. Prieto, S. Grunert, F. Bork, J. E. Hein, <i>Chem. Sci.</i> <b>12</b>, 15473–15490 (2021).

Keywords

autonomous research

Symposium Organizers

Andi Barbour, Brookhaven National Laboratory
Lewys Jones, Trinity College Dublin
Yongtao Liu, Oak Ridge National Laboratory
Helge Stein, Karlsruhe Institute of Technology

Session Chairs

Andi Barbour
Yongtao Liu

In this Session