Theses and Dissertations

ORCID

https://orcid.org/0000-0003-0422-3458

Advisor

Rai, Neeraj

Committee Member

Kundu, Santanu

Committee Member

Gwaltney, Steven

Committee Member

Meng, Dong

Date of Degree

8-13-2024

Original embargo terms

Embargo 1 year

Document Type

Dissertation - Open Access

Major

Chemical Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Dave C. Swalm School of Chemical Engineering

Abstract

Organic conjugated polymers (CPs) are emerging materials for advanced electronic applications such as organic photovoltaics (OPVs), field-effect transistors (OFETs), light-emitting diodes (OLEDs), flexible and wearable electronics, and biomedicals. High-spin donor-acceptor CPs have been investigated for their potential applications in magnetic and spintronic devices. Inter-chain charge transfer among these high-spin polymers mainly depends on the nature of the local structure of the thin film and pi-stacking between the polymer chains. However, the microscopic structural details of high-spin polymeric materials are rarely studied, especially in the liquid phase, and remain largely unexplored. This study examined the effects of oligomer chain length, side chain, and processing temperature on the organization of the high-spin cyclopentadithiophene-benzobisthiadiazole donor-acceptor conjugated polymer in chloroform solvent. We have found that the oligomers exhibit ordered aggregation based on chain length, with an average pi-stacking distance of 3.38±0.03 (angstrom), aligning well with the experiment. During the solution processing of CPs, smart polymers are widely used for controlling the device performance. Polymethyl-methacrylate (PMMA) is a smart polymer exhibiting solvation behavior in aqueous alcohol mixtures that is different from individual solvents. However, a thorough understanding of the microscopic details underlying PMMA cosolvency remains elusive, which is essential for tailoring smart polymers for advanced applications. Using molecular dynamics simulation, this study elucidates the PMMA's cosolvency behavior in a binary mixture of aqueous tert-butanol and successfully captures the re-collapsing behavior observed experimentally. We have observed that the excess hydrogen bonding between PMMA and water mimics the re-collapsing pattern, suggesting a key role in PMMA's cosolvency. Efforts to discover new organic molecules are hindered by the vast chemical space. Machine learning (ML) approaches offer a promising solution to accelerate the development of new materials. However, predicting properties accurately with ML models typically involves high computational costs and complexity. This study employs a first-generation ML model, ridge regression, to predict the electronic properties — electronic gap, HOMO, LUMO, and singlet-triplet gap of pi-conjugated organic molecules. This research provides molecular-level insights to control device performance by managing aggregation in thin films and reduces the effort required to screen for desirable organic CPs. These findings will facilitate the development of new organic molecules tailored for advanced electronic devices.

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