Melanin-Based Thin Films for Sustainable Electronic Devices
Bibliographic record
Abstract
Melanin is a natural (bio-sourced) and bio compatible material, attracting attention because of its physicochemical properties. Melanin is a family of pigments and the brown-black eumelanin pigment is part of this. It can be found in skin, hair, eyes, and brain [1-2]. Considering its quinone-based and conjugated molecular structure, eumelanin is an interesting candidate for bioelectronics. Transport physics in eumelanin-based thin films and pellets need to be better understood to optimize its technological applications. The main challenge for the use of eumelanin in bioelectronics is to increase the conductivity and detailed study is required to clarify and quantify the electronic and protonic contribution to charge transport. High quality thin films of eumelanin material on substrates such as ITO (Indium Tin Oxide) and FTO (Fluorine Tin Oxide) substrates were prepared to study the electronic transport properties of the biopigment. The electrodeposition method, spray pyrolysis, and spin coating were used for the preparation of uniform thin films. The structural, optical, and morphological properties of these thin films were studied using X-ray diffraction (XRD), atomic force microscopy (AFM), field emission electron microscopy (FESEM) and transmission electron microscopy (TEM). For the metal contacts both top contacts using a through shadow masking and photolithography were used. Preliminary results on the dependence of electronic transport on temperature, relative humidity, and interelectrode distance range were collected. Reference [1] Julia Wunsche, Yingxin Deng, Prajwal Kumar, Eduardo Di Mauro, Erik Josberger, Jonathan Sayago, Alessandro Pezzella, Francesca Soavi, Fabio Cicoira, Marco Rolandi, and Clara Santato, Chem. Mater. 2015,27, 436−442. [2] Prajwal Kumar, Eduardo Di Mauro, Shiming Zhang, Alessandro Pezzella, Francesca Soavi, Clara Santato and Fabio Cicoira, J. Mater. Chem. C, 2016, 4, 9516.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".