Design and development of a simple and highly sensitive <scp>anthocyanin‐based</scp> sensing device for colorimetric urea determination
Bibliographic record
Abstract
Abstract We introduce a rapid anthocyanin‐based paper sensor with very high sensitivity and optical visibility for colorimetric detection of urea. The working principle is based on a colour change from purple to blue upon sensor exposure to ammonia generated from urea hydrolysis in the presence of urease as a catalyst. To improve sensor sensitivity and optical visibility, anthocyanin storage, urease solvent, urea hydrolysis time, and temperature were investigated. The results indicated that the anthocyanin extracted from red cabbage and stored in dark and low‐temperature conditions, urease extracted into water55 + glycerol45 (Aq55 + G45), and urea hydrolysis time of 40 min and temperature of 45°C offer the best detection condition. The fabricated sensor showed exceptional sensitivity of 0.018 pixel/mg urea‐N/L with a very low limit of detection (2.01 mg urea‐N/L) and a limit of quantification (6.71 mg urea‐N/L). Moreover, the sensor reaction zone is optically visible for urea concentration as low as 5 mg urea‐N/L, making it a promising tool for urea screening in diverse applications. The unique analytical features and accuracy of the sensor compared to the spectrophotometry method also suggest that it can be used as a replacement for environmentally unfriendly spectrophotometry methods for on‐site urea determination.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".