A Very Simple Method for Detection of Bisphenol A in Environmental Water by Heme Signal Amplification
Bibliographic record
Abstract
The detection of bisphenol A (BPA) was carried out using several simplified electrochemical sensors in which electroactive materials Heme, dopamine (DA), β -cyclodextrins ( β -CD) or Fc-ECG were deposited on screen-printed electrodes (SPEs) through one-step. The preparation of the developed sensors were verified by SEM, CV and DPV. Results shown that Heme/SPE sensor demonstrated notable electro-catalytic capabilities and a greater current response than other sensors for BPA detection and optimal operating conditions were determined. The current response vs concentration of BPA showed a linear relationship in the range of 0.05–0.50 μ mol·l −1 , the concentration limit of detection (S/N = 3) as 5.0 nmol·l −1 and 0.0013 μ A· μ M −1 ·cm −2 as the sensitivity of detection. There were no interferences arising from common elements that relate to BPA. Heme/SPE was applied for BPA detection in bottle drinking water, tap water and lake water; findings showed a total R.S.D. of less than 3.1% and average recovery from 95.0 to 104.0%. These results showed that the actual application of BPA detection was rapid and precise when placed in environmental water and even useful for safety verification.
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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 | 0.001 | 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 | 0.001 |
| 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".