Enhanced Charge Transport and Stability Conferred by Iron(III)‐Coordination in a Conjugated Polymer Thin‐Film Transistors
Bibliographic record
Abstract
Abstract A high performance diketopyrrolopyrrole (DPP)–based semiconducting polymer is modified with ligands to enable metal coordination, and its subsequent effect as field‐effect transistors is investigated. In specific, pyridine‐2,6‐dicarboxamide (PDCA) units are incorporated in a DPP–based polymer backbone with a content from 0 to 30 mol%, and the resulting polymers are then mixed with Fe(II) ions. The coordination and spontaneous oxidation converts Fe(II) to Fe(III) ions to result in Fe(III)‐containing metallopolymers. The resulting metallopolymers are observed to show good solubility in organic solvents and can be easily processed as thin films. The charge transport characteristics are subsequently investigated through the fabrication of field–effect transistor devices, in which an enhanced charge carrier mobility with the Fe(III)‐containing metallopolymers is observed. In specific, an almost twofold improvement in the charge carrier mobility is obtained for the 20% PDCA‐containing polymer after Fe coordination (from 0.96 to 1.84 cm 2 V −1 s −1 ). Furthermore, the operation stability of the metallopolymer‐based devices is found to be significantly improved with low bias stress. Its superior electrical characteristics are attributed to the doping effect of the Fe ions. This study indicates that incorporation of appropriate metallic ions to polymer presents a viable approach to enhance the performance of polymer–based transistor devices.
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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) | 0.001 | 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".