Immobilization of the Alcohol Dehydrogenase Enzyme on TiO<sub>2</sub> Nanotubes for Application in Microfluidic Fuel Cell
Bibliographic record
Abstract
Microfluidic fuel cells are a low-power demand fuel cells which work without the need of a physical membrane and have a wide range of potential applications. Biological catalysts such as immobilized enzymes can be used in electrodes to carry out the oxidation processes of different organic fuels using the immobilization technology of enzymes; they are carried out under moderate conditions of pH and temperature, and the specificity of the catalytic reactions. The alcohol dehydrogenase enzyme has been used for the development of bioanodes for the oxidation of different alcohols such as ethanol and methanol. In this work, the immobilization of this enzyme was carried out using nanotubes of titanium dioxide for the development of electrodes for an air-breathing microfluidic fuel cell. TiO2 nanotubes has excellent properties such as pH resistance, superior mechanical strength, good biocompatibility making it a great candidate for the process of immobilization of the enzyme alcohol dehydrogenase. TiO2 nanotubes were fabricated by electrochemical anodic oxidation on Ti foils. The foils were pre-treated with sand-paper, thereafter immersed in ethanol, placed in ultrasonic bath, dried under N2 flow. An ethylene glycol-based solution with a concentration of 0.1M NH4F (96% purity, Alfa-Aesar) and 2% w/w deionized water was used as electrolyte. The anodization times used was 1 h with a voltage step of 60 V via a power source. Using this nanostructure, the enzymatic electrode was constructed using a catalityc ink with enzyme, tetrabutylammonium bromide and Nafion to carry out the immobilization process. We carried out the characterization of the electrode developed by means of different electrochemical and kinetic techniques, where it was demonstrated that the enzyme is present and active in the electrode, as well as its capacity to oxidize ethanol. Then the bioanode was evaluated in the microfluidic device also using an inorganic cathode of Pt / C, obtaining a good performance over an open circuit potential greater than 0.93V.
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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) | 0.000 | 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".