All-Solid-State Sodium-Selective Electrode with a Solid Contact of Chitosan/Prussian Blue Nanocomposite
Bibliographic record
Abstract
Conventional ion-selective electrodes with a liquid junction have the disadvantage of potential drift. All-solid-state ion-selective electrodes with solid contact in between the metal electrode and the ion-selective membrane offer high capacitance or conductance to enhance potential stability. Solution-casted chitosan/Prussian blue nanocomposite (ChPBN) was employed as the solid contact layer for an all-solid-state sodium ion-selective electrode in a potentiometric sodium ion sensor. Morphological and chemical analyses confirmed that the ChPBN is a macroporous network of chitosan that contains abundant Prussian blue nanoparticles. Situated between a screen-printed carbon electrode and a sodium-ionophore-filled polyvinylchloride ion-selective membrane, the ChPBN layer exhibited high redox capacitance and fast charge transfer capability, which significantly enhanced the performance of the sodium ion-selective electrode. A good Nernstian response with a slope of 52.4 mV/decade in the linear range from 10−4–1 M of NaCl was observed. The stability of the electrical potential of the new solid contact was tested by chronopotentiometry, and the capacitance of the electrode was 154 ± 4 µF. The response stability in terms of potential drift was excellent (1.3 µV/h) for 20 h of continuous measurement. The ChPBN proved to be an efficient solid contact to enhance the potential stability of the all-solid-state ion-selective electrode.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".