Development of Gold Electrodes for Microbial Fuel Cells
Bibliographic record
Abstract
The microbial fuel cell (MFC) has been an important subject of study in the last decades because of its technological significance that one can produce hydrogen or electricity by wastewater treatment (bio-remediation). One of the main issues for the application of these devices on large scale is the processes and materials for the electrode fabrication. The cathode for MFC requires a catalyst to perform the reduction reaction and this work presents a simple technique to obtain thin layers of gold (TLG) supported on glass. This technique was employed to obtain TLG with different thicknesses from 848 nm to the thinnest of 137 nm. Since the gold of the TLGs presented adherence issues, a successful thermal treatment with different temperatures from 150-300 ºC was developed to avoid the gold detachment. The TLGs were tested as cathodes in a MFC and a maximum Voc of 431 mV and an Isc of 10 × 10−2 mA were obtained. The process to obtain TLGs presented here has probed to be a good option for this application since the thickness obtained and the accessible material (glass) employed as support offers a solution to the costs and the scaling issues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".