Dynamic detection of soluble intermediates during methanol electrooxidation
Bibliographic record
Abstract
Methanol electrooxidation at a Pt band electrode is studied in a microfluidic flow cell (MFFC) with simultaneous electrochemical detection of formic acid at a down-stream sensor (detector) Pd electrode. Detection of formic acid at the Pd electrode is possible through either a fast voltammetry or a potential step procedure. In both cases, maximizing the detection signal (oxidation current) requires Pd oxide formation and reduction before detection. Pd oxide formation is needed to oxidize surface-bound carbonaceous species while Pd oxide reduction is needed to reactivate the electrode surface. Although formic acid alone is easily detected and provides a stable detection signal at the Pd sensor electrode, simultaneous formation of formaldehyde during methanol oxidation degrades the detection signal at the Pd electrode over time. We show that dynamic detection of sub-millimolar formic acid concentrations in 2 M methanol is possible at a time resolution down to 2 s. Dynamic detection during methanol oxidation at platinum shows that production of soluble intermediates occurs at all potentials where an oxidation current is observed. Furthermore, a higher faradaic efficiency to formic acid occurs during the main methanol oxidation peak in the positive-going sweep compared to the negative-going sweep, and is higher still at potentials above 1 V. This work shows that microfluidic flow cells can be used for fast and dynamic detection of soluble reaction intermediates by using down-stream electrodes with custom potential step or sweep sequences. • A double channel electrode flow cell is used to study methanol oxidation at Pt. • Two potential sequences are tailored to selectively detect formic acid at a Pd electrode. • Formic acid is dynamically detected at sub-millimolar concentration. • Formic acid is detected at all potentials where methanol electrooxidation occurred. • A higher formic acid faradaic efficiency is observed in the positive-going sweep.
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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) | 0.001 | 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".