Supramolecular Organization-Electrical Properties Relation in Nanometric Organic Films
Bibliographic record
Abstract
The need to improve the performance of electronic devices based on organic materials has been at the center of structure–property relation research, where the main objective is to develop low cost and flexible electronic components in large-scale. Important contributions to the performance of such devices have been made based on studies of the supramolecular organization of small organic molecules, primarily those with π-conjugated systems. Here we examine the relationship of supramolecular organization and the electrical properties of a substituted perylene tetracarboxylic diimido in nanometric films fabricated by physical vapor deposition (PVD). The morphology of the thin solid films is probed with optical and electron microscopy and the supramolecular characterization includes vibrational and electronic spectroscopies. The electrical properties are studied using AC and DC measurements with interdigitated electrodes and diode-like structures. A higher conductivity is observed when measured with the diode-like structure. It seems to be associated with a perpendicular orientation of the electric field with respect to the π–π molecular stacking, favoring the charge transport through these π aggregates. The results enhance the understanding of organic electronics, helping surface engineering to harness supramolecular organization to improve performance of thin film 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".