Good Readings Come in Threes: Understanding Electrodeposited Iridium Oxide for a Reproducible pH Microsensor Performance
Bibliographic record
Abstract
Electrodeposited iridium oxide has been widely acknowledged as an effective pH sensing material of full pH sensing range, fast response time, and good chemical stability. However, a lack of fundamental understanding of the in-flask chemistry, electrodeposition chemistry, and storage chemistry limits the reproducibility of iridium oxide-based pH microsensors. Here, the iridium oxide films on pH microsensors are investigated from three perspectives: solution chemistry, electrodeposition chemistry, and film storage chemistry. We demonstrate that a high deposition efficiency is achieved with a high concentration of multi-Ir(IV)-center oligomers; the geometrical confinement of the iridium oxide film on microelectrode is enabled by lower electrodeposition potential; and the degradation of iridium complexes during film storage leads to time-dependent pH reading drift. The pH reading drift can be alleviated by laser-induced localized annealing of the iridium oxide films, which shows great potential for further fabrication and electrode patterning of iridium oxide microsensors and sensor arrays.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".