Studies of DTF and TTFV-based donor-acceptor systems and redox-active polymer thin films
Bibliographic record
Abstract
1,4-Dithiafulvene (DTF) is a five-member heterocycle that has been frequently used as a redox-active molecular building block in various organic electronic materials. The combination of two DTF groups via an exo-ring C=C bond leads to formation of well-known tetrathiafulvalene (TTF), which has been extensively studied since the first discovery of its metallic conductivity. Previous research has demonstrated that DTF and tetrathiafulvalene vinylogue (TTFV)-based conjugated molecules and polymers show favored intermolecular interactions (e.g., π–π stacking and chargetransfer interactions) with electron-deficient nitroaromatic compounds (NACs), owing to the electron-donating nature of DTF and TTFV groups. Such properties can be utilized in the design of chemical sensors for detection of NACs, which are an important class of pollutants in the environment. To further understand the interplay between NACs and DTF/TTFV-containing π-systems, a group donor–acceptor ensembles containing nitrophenyl-substituted DTF and TTFV moieties have been investigated in this thesis work. Detailed synthetic methods and structure-property relationships will be discussed in the first chapter. In particular, the structural, electronic, and electrochemical redox properties were systematically examined by Xray single crystallographic, UV-Vis absorption, and cyclic voltametric analyses, in conjunction with density functional theory (DFT) modeling. With the fundamental properties characterized and understood, a new type of TTFV-based redox-active polymer was next designed and prepared. In the second part of this project, a strategy of double-layer polymer film will be introduced. With this method, robust and redox-active TTFV polymer thin films could be efficiently generated on the surface of glassy carbon electrodes. These modified electrodes were found to show sensitive responses to various phenolic compounds at low concentrations (10−8 to 10−7M), suggesting promising application in rapid electrochemical sensing of phenol derivatives and related chemicals.
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 | 0.000 |
| 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) | 0.001 | 0.000 |
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".