Electrical, Optical Conductivity of Pentacene and the Effect of Adding the Extracted Dye on the Electrical Properties of Pentacene
Bibliographic record
Abstract
This investigation explores the impact of natural dye extract from the Lanata Kamra plant on the electrical conductivity of pentacene (PE) polymer thin films.Films of pure PE were spin-coated with a 1:1 dye ratio, and their characteristics were examined using Xray diffraction.The results highlighted the presence of a dominant PE peak in the 22th-28th range, a grain size of 0.341 nm for the pure PE, and 0.491 nm for the dye-doped polymer.The uniformity of the membrane surfaces suggested a regular arrangement of polymer units.The morphology of the films was further investigated with Scanning Electron Microscopy (SEM) at 200 nm and 500 nm resolutions, revealing the formation of surface fractures due to polymer shrinkage during production.The SEM analysis also indicated strong inter-chain interactions during membrane formation, resulting in spherically shaped crystals.Electrical properties, including current-voltage characteristics, were assessed using a Keithley Series 2400 source meter, operating within a voltage range of 1-100 V and a temperature of 30℃.The results demonstrated an increase in electrical conductivity with increased dye doping.Optical properties were examined over a wavelength range of 300-780 nm, suggesting the potential applicability of this model to photosensitization processes.
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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".