Strong Magnetic Field Annealing for Improved Phthalocyanine Organic Thin‐Film Transistors
Bibliographic record
Abstract
Thin-film microstructure, morphology, and polymorphism can be controlled and optimized to improve the performance of carbon-based electronics. Thermal or solvent vapor annealing are common post-deposition processing techniques; however, it can be difficult to control or destructive to the active layer or substrates. Here, the use of a static, strong magnetic field (SMF) as a non-destructive process for the improvement of phthalocyanine (Pc) thin-film microstructure, increasing organic thin-film transistor (OTFTs) mobility by twofold, is demonstrated. Grazing incident wide-angle X-ray scattering (GIWAXS), X-ray diffraction (XRD), and atomic force microscopy (AFM) elucidate the effect of SMF on both para- and diamagnetic Pc thin-films when subjected to a magnetic field. A SMF is found to increase the concentration of oxygen-induced radical species within the Pc thin-film, lending a paramagnetic character to ordinarily diamagnetic metal-free Pc and resulting in magnetic field induced changes to its thin-film microstructures. In a nitrogen environment, without competing degradation effects of molecular oxygen, SMF processing is found to favorably improve charge transport characteristics and increase OTFT mobility. Thus, post-deposition thin-film annealing with a magnetic field is presented as an alternative and promising technique for future thin-film engineering applications.
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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".