Eumelanin thin films: solution-processing, growth, and charge transport properties
Bibliographic record
Abstract
Eumelanin pigments show hydration-dependent conductivity, broad-band UV-vis absorption, and chelation of metal ions. Solution-processing of synthetic eumelanins opens new possibilities for the characterization of eumelanin in thin film form and its integration into bioelectronic devices. We investigate the effect of different synthesis routes and processing solvents on the growth, the morphology, and the chemical composition of eumelanin thin films using atomic force microscopy and X-ray photoelectron spectroscopy. We further characterize the films by transient electrical current measurements obtained at 50% to 90% relative humidity, relevant for bioelectronic applications. We show that the use of dimethyl sulfoxide is preferable over ammonia solution as processing solvent, yielding homogeneous films with surface roughnesses below 0.5 nm and a chemical composition in agreement with the eumelanin molecular structure. These eumelanin films grow in a quasi layer-by-layer mode, each layer being composed of nanoaggregates, 1-2 nm high, 10-30 nm large. The transient electrical measurements using a planar two-electrode device suggest that there are two contributions to the current, electronic and ionic, the latter being increasingly dominant at higher hydration, and point to the importance of time-dependent electrical characterization of eumelanin films.
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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".