Bibliographic record
Abstract
This project addresses the development of low band gap organic materials with high intrinsic conductivity which remains a big challenge for many conducting polymer based applications. New bridged bithiophene and fluorene systems were investigated. Results proved that the linkage of two bridged bithiophene moieties by a double bond, and bridging fluorene moieties by sulphur atoms can produce low band gap materials with low-lying LUMO energy levels. This new approach to the design of low band gap materials compliments the strategy of introducing electron withdrawing groups at an sp² carbon bridging a bithienyl precursor for producing n-type conjugated low band gap systems. Advanced polymerization techniques such as copolymerization (polymerization from solution of two monomers), homopolymerization of a comonomer (a molecule containing both monomer units) and working electrode rotation during polymer growth were developed to tune band gaps and enhance intrinsic conductivities. Investigations of copolymers with conjugated bridged bithiophene and fluorene systems support the donor acceptor strategy for band gap reduction. Hence, by advanced synthetic strategies, the band gap of a polythiophene derivative was tuned from 1.0 e V to 0.1 e V and its intrinsic conductivity was improved by an order of magnitude. Further, a variety of polyfluorene derivatives with band gaps ranging from 1.2 eV to 0.8 eV were synthesized and structureconductivity relationships were studied. These materials may have potential uses in electrochromic displays and related applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".