Hydrogen Peroxide Detection at Electrochemically and Sol‐Gel Derived Ir Oxide Films
Bibliographic record
Abstract
Abstract Ir oxide (IrOx) films, formed electrochemically on bulk Ir metal (Ir/IrOx) and also on sol‐gel (SG) derived non‐silica based nanoparticulate Ir, have been studied as material useful for the detection of hydrogen peroxide, with possible application as a glucose biosensor. H2O2 reduction and oxidation on Ir/IrOx and SG‐derived IrOx films, deposited on various substrates such as Pt, Ir and GC, have been compared to the H2O2 behavior at the bare substrate. It was found that H2O2 reduction proceeds on the underlying electrode substrate, while H2O2 oxidation is independent of the nature of the substrate, therefore occurring via the IrOx film. The reactivity of IrOx towards H2O2 oxidation is similar to that seen at Pt, although IrOx has the additional advantages of excellent stability, insensitivity to common interfering substances, biocompatibility and a linear range of detection, up to at least 12 mM H2O2. At micromolar concentrations of H2O2, a second mode of detection, involving the catalyzed growth of IrOx films at Ir substrates, can be employed. These two methods of H2O2 analysis (oxidation/reduction and enhanced IrOx growth) can also be employed for glucose detection using IrOx‐based glucose biosensors.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".