Pico-molar electrochemical detection of ciprofloxacin at composite electrodes
Bibliographic record
Abstract
and in tap water. The designed sensor was constructed using oxygen functionalized carbon nanotubes (OCNTs), a layer of conductive polymer (polydopamine; PDA), and electro-deposited silver nanoparticles (Ag-NPs) at a glassy carbon electrode. Double-pulse electro-deposition was employed, as it offers numerous advantages including quick analysis time (s, ms), high reproducibility, and does not require expensive complexing agents, even at room temperature. The fabricated chemical sensor was fully optimized and characterized using physiochemical and electrochemical techniques, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and voltammetry. Electrochemical measurements demonstrate high applicability of the chemical sensor towards the detection of Cip in the pico-molar concentration range. Detection and quantification levels were determined and the stability of the sensor was assessed. The obtained results demonstrate the potential of the OCNTs-PDA-Ag sensor to detect Cip in environmental samples.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".