Voltammetric pH sensor based on electrochemically modified pseudo-graphite
Bibliographic record
Abstract
A nanocrystalline graphite-like amorphous carbon (graphite from the University of Idaho thermolyzed asphalt reaction, GUITAR) shares morphological features with classical graphites, including basal and edge planes (BP, EP). However, unlike graphites and other sp2-hybridized carbons, GUITAR has fast heterogenous electron transfer (HET) across its basal planes, and resistance to corrosion similar to sp3-C and boron-doped diamond electrodes. In this contribution, quinoid modified BP-GUITAR (q-GUITAR) is examined as a sensor for pH determination. This modification is performed by applying 2.0 V (vs. Ag/AgCl) for 150 seconds followed by 15 cyclic voltammetric scans from -0.7 to 1.0 V at 50 mV s-1 in 1.0 M H2SO4. The quinoid surface coverage of q-GUITAR is 1.35 × 10-9 mol cm-2, as measured by cyclic voltammetry. X-ray photoelectron spectroscopy analysis also confirms the high surface coverage. The quinoid surface concentration ranks highest in literature when compared with other basal plane graphitic materials. This yields a sensor that responds through a square wave voltammetric reduction peak shift of 63.3 mV per pH over a pH range from 0 to 11. The response on q-GUITAR is stable for >20 measurements and no surface re-activation is required between the measurements. The common interferents, Na+, K+ and dissolved oxygen, have no effect on the response of the q-GUITAR-based pH sensor.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".