Electrochemical Impedance Spectroscopy of Zinc Oxide Nanoparticles After Deposition on Screen Printed Electrode
Bibliographic record
Abstract
A small aliquot (10–14 μL) of ZnO nanoparticles dispersed in deionized water was deposited by evaporation to produce a dry residue on the working area of a screen-printed electrode. An electrochemical test solution containing K 3 Fe(CN) 6 and KCl was added to the electrode surface for analysis by electrochemical impendence spectroscopy (EIS). Using this deposition analysis technique, a new relationship between the charge transfer resistance ( R ct ) and the amount of ZnO nanoparticles has been explored. Based on the trend of increasing R ct value with an increase of ZnO nanoparticles, a quantitative analysis method can be established to determine the mass of nanoparticles (0.01–1.00 μg) deposited from an unknown dispersion. To study the matrix effect, addition of Nafion solution to the aqueous dispersion resulted in a change of the linear range to 0.3–0.5 μg nanoparticles. Addition of methanol (10% by volume) to the aqueous dispersion changes the analysis range to 0.2–0.6 μg nanoparticles, while additional methanol (50% by volume) changes the analysis range to 0.06–1.00 μg nanoparticles. The analytical sensitivity, as indicated by the slope of each standard calibration curve, ranked as: aqueous dispersion > Nafion/aqueous dispersion > 10% methanol/aqueous dispersion > 50% methanol/aqueous dispersion. Altogether these results verify that deionized water is the best dispersion medium for EIS analysis of ZnO nanoparticles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".