Preparation of Polycarbazole Nanofibers Using an Electric Field and the Investigation of Its Electrical Conductivity
Bibliographic record
Abstract
In conventional chemical and electrochemical oxidation methods, it is very difficult to control the active centers, and the average prepared polymers are short and wide. The use of an electric field creates the most stable intermediate form of active centers, as well as permitting a longer half-life. Therefore, this increases the physical resistance and electrical conductivity of the polymer. In this paper, polycarbazole nanofibers were prepared using an electric field, reporting on its influences on the polymerization of carbazole. Therefore, its electrical conductivity and some physical properties were investigated. We observed the nanofibers’ shape, increasing electrical conductivity, thermal resistance and a higher molecular weight with the synthesized polycarbazole under an electric field compared to the polymer synthesized in the same conditions in the absence of an electric field. First, we chemically synthesized polycarbazole at different times. Additionally, to find the optimizing conditions, we changed certain parameters, such as the ratio of the obtained molar of initiator to monomer, the oxidant, initiator and solvent, separately, and compared the obtained results. Then, we repeated this reaction in the best conditions and under different electric fields in constant time, allowing us to characterize the shape, mass and conductivity. Next, the polymerization was carried out at the best electric field in different times. Finally, the best time and amount of electric field for polymerization were determined. The electrical conductivity of polycarbazoles was studied with the four-probe method. The conductivity of the films oxidized using FeCl3 (dry) and protonated with p-toluenesulfonic acid (PTSA) at 3 h was higher than 8.9 × 10−4 S/cm under a 12 KV/m electric field. Additionally, the results showed an enhanced thermal resistance to ageing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".