Cationic Dye Modified Sawdust as Electrode Modifier for Electrochemical Detection of Anions
Bibliographic record
Abstract
Abstract Because of its chemical properties, sawdust displays poor anionic exchange capacity. Here we demonstrate that sawdust modification with methylene blue (MB) dye represents an interesting and facile alternative to render this natural biomaterial capable to accumulate anionic species. MB adsorption onto sawdust was monitored by cyclic voltammetry and experimental parameters carefully optimized. Under the ideal experimental conditions (composition of accumulation and desorption solution, accumulation and desorption time and the nature of the electrolytic solution), the adsorbed MB showed poor mobility, which results in the absence of the characteristic electrochemical signal of MB. The ability of the material to accumulate anionic species was thus evaluated using Fe(CN)63− as a model anions. The slow Fe(CN)63−/4− system recorded onto the electrode modified by pristine sawdust (P/SFE) become fast and reversible after immobilization of MB onto P/SFE (MB/SFE). Electrochemical impedance spectroscopy confirms this result through the spectacular decrease of charge transfer resistance after MB adsorption (from 83 kΩ on P/SFE to 637 Ω on MB/SFE). MB/SFE was applied to the electroanalysis of nitrites and a sensitivity of 7.4 μA mM−1 was obtained. Although this sensitivity was less important compared to that obtained on glassy carbon electrode (9.4 μA mM−1), the dye modified electrode displays by far the best reproducibility even at higher nitrite concentration.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".