Bibliographic record
Abstract
In this thesis, I describe my effort in the synthesis of selenophene- and thiophene-based conjugated polymers and the development of organogels and hydrogels from them. In Chapter 1, I provide a general introduction on what are conjugated polymers and their applications. Then, a brief overview of programmable self-assembly of conjugated polymers including covalent programming and solid-state programming is introduced. It provides the background, importance, and recent works on the development of conjugated polymer gels. In Chapter 2, I describe the formation and characterization of selenophene- and thiophene-based conjugated homopolymer organogels. I demonstrate the first reported polyselenophene organogels and the result shows they are promising for the development of stretchable electronics. I have also developed a cycle-doping method to successfully dope the bulk gel films and offer the merit of being able to monitor the change in the film conductivity as the doping cycle increases at the same time. And this method can be applied to dope bulk films in general. In Chapter 3, I further expand the types of conjugated polymer organogels to selenophene- and thiophene-based statistical copolymers. I investigate the influence of molecular weight on the electrical performance of those statistical copolymers. I report the self-assembly behaviour of both the statistical copolymer thin films and their gels at low, medium, and high degrees of polymerization. And the doping mechanism of statistical copolymer gels is also determined. In Chapter 4, I examine the concept of forming hydrogels from full conjugated block copolymers. I demonstrate the synthesis of an amphiphilic block copolymer and the successful development of hydrogel from it. In Chapter 5, I summarize the main conclusions and offer outlooks for my thesis work. Future directions in the development of conjugated polymer organogels and hydrogels are described, and they could offer a better understanding and higher impact in the field of conjugated polymer gels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".