Synergistic Electrochemical Amplification of Ferrocene Carboxylic Acid Nanoflowers and Cu Nanoparticles for Folic Acid Sensing
Bibliographic record
Abstract
Folic acid (FA) plays an indispensable role in human body and sometimes needs to be taken as a drug supplement, especially for pregnant women. Herein, an electrochemical FA sensor was constructed by electrodepositing Cu and ferrocene carboxylic acid (Fc(COOH)) on a glassy carbon electrode (GCE), indicating low cost, simple preparation and short time consumption. Furthermore, the field emission scanning electron microscopy illustrates that Fc(COOH) completely covering Cu nanoparticles (CuNPs) grew to be tufts of loose and porous nanoflowers in situ, which produces a large active surface area to adsorb FA. Results verify that two conjected materials exhibited a good synergistic amplification effect on FA signal. Ultimately, a great linear relationship of FA was established between 100.0 ∼ 1000.0 μ M under optimized conditions by differential pulse voltammetry (DPV). The limit of detection was 33.3 μ M, and the sensitivity was 0.10149 μ A· μ M −1 ·cm −2 . The sensor Fc(COOH)/CuNPs/GCE showed satisfactory selectivity and stability and could be used for FA detection in FA tablets samples with an average recovery of 91.43 ∼ 100.68%, and a relative standard deviation less than 3.17%. The consistency and validity were affirmed by comparisons with an ultra-visible spectrophotometer.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".