The Design of Low Pt Loading Electrodes for Use in Fuel Cell-Based Breath Alcohol Sensors
Bibliographic record
Abstract
Modern breath alcohol sensors (BrAS) employ an electrochemical sensor based upon fuel cell technology. These devices closely mimic power generating fuel cell technology from 30 years ago, with each electrode containing massive amounts of Pt black catalyst (∼10−20 mg cm−2). Here we report low-loading gas diffusion electrodes (GDE) fabricated using 40% Pt/C and studied the impact of Pt loading on sensor performance. The optimal loading was determined to be ca. 1 mgPt cm−2, which gives the optimal balance between Pt utilization and ethanol sensitivity. The ethanol sensitivity performance achieved with the GDE paired with a Nafion membrane was similar to that achieved with a commercial MEA that employs a Pt loading of 13.7 mg cm−2 and a PVC membrane. When paired with porous-PVC membranes our GDEs showed even greater sensitivity, readily exceed that of the commercial MEA despite the fact it employs 92% less Pt. The highest sensitivity was achieved when the GDE was paired to a gold-coated PVC membrane (Au-PVC), where the thin layer of gold is believed to enhance the membrane∣electrode interface. Thus, this sensor composition is proposed as a viable lower-cost alternative to the high-loading Pt black electrodes currently used in commercial BrAS technology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".