Silver Nanoparticle-Embedded Conductive Hydrogels for Electrochemical Sensing of Hydroquinone
Bibliographic record
Abstract
In this work, a conductive hydrogel was successfully synthesized, taking advantage of the high number density of active amino and hydroxyl groups in carboxymethyl chitosan and sodium carboxymethyl cellulose. These biopolymers were effectively coupled via hydrogen bonding with the nitrogen atoms of the heterocyclic rings of conductive polypyrrole. The inclusion of another biobased polymer, sodium lignosulfonate (LS), was effective to achieve highly efficient adsorption and in-situ reduction of silver ions, leading to silver nanoparticles that were embedded in the hydrogel network and used to further improve the electro-catalytic efficiency of the system. Doping of the system in the pre-gelled state led to hydrogels that could be easily attached to the electrodes. The as-prepared silver nanoparticle-embedded conductive hydrogel electrode exhibited excellent electro-catalytic activity towards hydroquinone (HQ) present in a buffer solution. At the optimum conditions, the oxidation current density peak of HQ was linear over the 0.1-100 μM concentration range, with a detection limit as low as 0.12 μM (signal-to-noise of 3). The relative standard deviation of the anodic peak current intensity was 1.37% for eight different electrodes. After one week of storage in a 0.1 M Tris-HCl buffer solution at 4 °C, the anodic peak current intensity was 93.4% of the initial current intensity. In addition, this sensor showed no interference activity, while the addition of 30 μM CC, RS, or 1 mM of different inorganic ions does not have a significant impact on the test results, enabling HQ quantification in actual water samples.
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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".