Colorimetric Detection of Uranyl Using a Litmus Test
Bibliographic record
Abstract
Ingestion of water containing toxic contaminants above levels deemed safe for human consumption can occur unknowingly since numerous common contaminants in drinking water are colorless and odorless. Uranyl is particularly problematic as it has been found at dangerous levels in sources of drinking water. Detection of this heavy metal-ion species in drinking water currently requires sending a sample to a laboratory where trained personnel use equipment to perform the analysis and turn-around times can be long. A pH-responsive colorimetric biosensor was developed to enable detection of uranyl in water which coupled the uranyl-specific 39E DNAzyme as a recognition element, and an enzyme capable of producing a pH change as the reporter element. The rapid colorimetric assay presented herein can detect uranyl in lake and well water at concentrations relevant for environmental monitoring, as demonstrated by the detection of uranyl at levels below the limits set for drinking water by major regulatory agencies including the World Health Organization (30 μg/L). This simple and inexpensive DNAzyme-based assay enabled equipment-free visual detection of 15 μg/L uranyl, using both solution-based and paper-based pH-dependent visualization strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".