A highly selective colorimetric assay for the determination of creatinine in biological samples using gluconic acid capped silver nanoparticles after ionic liquid based dispersive liquid phase microextraction
Bibliographic record
Abstract
A dispersive liquid-phase microextraction method combined with UV–vis spectrophotometry was utilized to highly selective determination of creatinine in human serum and urine samples. To overcome the interferences in complex matrices, creatinine reacted with 1,4-naphthoquinone-2- potassium sulfonate reagent to produce a red coloured product that could be extracted into a small volume of 1-hexyl-3-methylimidazolium hexafluorophosphate ([HMIM]PF6) ionic liquid solvent. To increase the sensitivity of the assay, gluconic acid capped silver nanoparticles (Ag NPs) were used. On addition of Ag NPs to the red coloured extracted product, the solution turned to blue accompanied with a red shift in wavelength around 620 nm that could be detected by the naked eye. The effective variables on the determination of creatinine such as concentration of the reagent, amount of formic and hydrochloric acids, type and volume of the extractant, and concentration of Ag NPs were investigated. Under the optimal conditions, the calibration plot was bimodal with linear ranges from 0.1 to 1.5 µg mL−1 and 1.5 to 105 µg mL−1 creatinine with a limit of detection 0.1 µg mL−1. The relative standard deviation for five measurements at 35 µg mL−1 concentration level was 3.8%. The newly developed assay was used for the determination of creatinine in human serum and urine specimens with satisfactory results.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".