Application of Electrochemical Immuno-sensor Based on Ketamine Hydrochloride for the Detection of Sports Illicit Drugs
Bibliographic record
Abstract
With the development of modern technology, the developed electrochemical immuno-sensor becomes a new kind of micro measurement technology. The electrochemical immuno-sensor based on ketamine hydrochloride was prepared in this study. Sufficient anti-bodies are combined with the electrode through adsorption of 3-mercaptopropionic acid and gold electrode. The results showed that the electrode had a specific response to antigens. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and atomic force microscope (AFM) were used for the electrode characterization. Results indicated that, the prepared electrode had good stability and re-producibility. The prepared electrochemical immuno-sensor based on ketamine hydrochloride in this study was applied to the detection of morphine content in sports illicit drugs, and we found that, such kind of sensor was appropriate for detection of illicit drugs. Moreover, it had high sensitivity and was hopeful to be made into a miniature instrument. Thus, the electrochemical immuno-sensor based on ketamine hydrochloride plays an important role in the detection of illicit drugs on the competition site of sports events.
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) | 0.001 | 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".