An ultrasensitive electrochemical sensor for the detection of acetaminophen<i>via</i>a three-dimensional hierarchical nanoporous gold wire electrode
Bibliographic record
Abstract
Acetaminophen is one of the most commonly used non-steroidal anti-inflammatory drugs worldwide. However, due to the increasing popularity of this drug, overdosing and the contamination of ambient waterways have emerged as major issues. Here, we report on a reliable, ultrasensitive, and easy-to-use sensor for the electrochemical detection of acetaminophen. This sensor employs a gold wire electrode with a unique three-dimensional hierachical nanoporous structure, fabricated using a dissolution, disproportion and deposition procedure. In consideration of optimal sensitivity and reproducibility, the most suitable nanoporous gold electrode was employed for the detection of acetamiophen among a set of nanoporous electrodes made under different reaction times. It was found that the pore size, film thickness, and electrochemically active surface area (ECSA) played major roles in the fouling resistance of the developed sensor. The ECSA of the selected sensor was increased by 15.8 times after the post-treatment. The 3D nanoporous electrode demonstrated excellent performance for the detection of acetaminophen with a low detection limit of 3.37 nM, and a strong anti-interference capability. The developed nanoporous Au electrode proved effective for the detection of acetaminophen in real sheep serum, which confirmed its promising application for medical diagnostics and pollutant surveilliance in source waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".