Automated chemical synthesis for accelerated discovery of organic electronic materials

Jira, Edward R.

Permalink

https://hdl.handle.net/2142/115864 Copy

Description

Title

Automated chemical synthesis for accelerated discovery of organic electronic materials

Author(s)

Jira, Edward R.

Issue Date

2022-06-02

Director of Research (if dissertation) or Advisor (if thesis)

Schroeder, Charles M.

Doctoral Committee Chair(s)

Schroeder, Charles M.

Committee Member(s)

• Burke, Martin D.
• Kenis, Paul J. A.
• Sing, Charles E.

Department of Study

Chemical & Biomolecular Engr

Discipline

Chemical Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Automated Synthesis
• Organic Electronics
• Organic Photovoltaics
• Suzuki Coupling
• Biohybrid Materials
• Molecular electronics
• single-molecule conductance

Abstract

The development of next-generation organic electronic materials critically relies on understanding structure-function relationships in conjugated polymers. However, unlocking the full potential of organic materials requires access to their vast chemical space while efficiently managing the large synthetic workload required to screen new materials. In this dissertation, we report systematic investigations of structure-performance relationships for organic electronic materials enabled by the development and implementation of automated small-molecule synthesis platforms. First, we investigate length-dependent assembly properties of biohybrid, pi-conjugated peptides containing oligothiophene moieties. These molecules are designed with a peptide-pi-peptide architecture to direct assembly of oligothiophene cores into arrangements amenable to intermolecular charge transport by leveraging interactions between peptide flanks. We find that the assembly of these materials critically depends on oligothiophene structure with longer oligothiophene cores leading to disordered aggregation and shorter cores enabling assembly into highly ordered, 1-dimensional structures. This initial study highlights the dramatic effect chemical structure has on material performance, however, synthetic challenges remain the key bottleneck to further study of this relationship. In order to more fully explore structure-performance relationships for broad classes of materials, we developed an automated platform for high throughput synthesis of diverse organic compounds. The platform leverages iterative Suzuki coupling of N-methyliminodiacetic acid (MIDA) protected haloboronic acid building blocks to access diverse and precisely defined chemical structures from simple starting materials via robust reaction and purification techniques. Compared to previous efforts, our system improves material throughput through enhanced parallelization capabilities and drastically improves reaction yield and reproducibility through improved inert gas and vacuum systems. This platform overcomes synthetic limitations on materials research by providing a general, efficient, and easy to use platform for the preparation of diverse organic materials. Following from this, we demonstrate the first uses of automated Suzuki coupling for direct structure-performance investigation and materials discovery research. Specifically, we highlight a systematic study of the impact of solubilizing side chains on molecular conductance. Leveraging our synthesizer to generate a library of terphenyl molecules with varying side chain length and chemistry, we find that molecular junctions with long alkyl side chains exhibit a concentration-dependent bimodal conductance with an unexpectedly high conductance state that arises due to surface adsorption and backbone planarization. Extending this strategy, we next demonstrate the use of automated Suzuki coupling to enable AI-driven, accelerated materials discovery. Specifically, we target novel organic photovoltaic (OPV) materials with improved light harvesting efficiency and performance lifetimes. Towards this, we design and synthesize a haloboronic acid building block library for the synthesis of OPV donor molecules. These blocks are iteratively coupled in our automated platform to enable on-demand access to a chemical space of 2,200 possible organic photovoltaic donors. In order to navigate this space, automated synthesis is coupled with AI-guided property prediction and high-throughput characterization. AI-selected target OPVs, predicted to have highest efficiency and stability, are synthesized in 10 molecule batches. Once a batch of molecules is tested, the data obtained is used to update our AI algorithm and inform future predictions. As such, automated synthesis enables AI algorithms to ”learn” structure-performance relationships for functional materials. Overall, the work discussed in this dissertation provides a general framework for accelerated materials research enabled by automated chemical synthesis.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Edward Jira

Owning Collections