A New Disposable Paper-Based Chemosensor for the Rapid and Direct Detection of Hexavalent Chromium in Aqueous Media
Bibliographic record
Abstract
Abstract Hexavalent chromium (Cr 6+ ) poses significant risks to both individuals and the environment. However, direct detection of Cr 6+ in fully aqueous media with high sensitivity and selectivity is extremely challenging. Herein, an isoindigo-derived chemosensor (II-MT) is developed and utilized as a single use paper-based sensor. This sensor allows for quick and simple screening of Cr 6+ in water through a significant color change that is visible to the naked eyes. Moreover, via a simple UV detection method, the new sensor enables an accurate trace-level detection of Cr 6+ at concentration as low as 0.1 µM. This detection capability is one order of magnitude lower than the recommended limit for drinking water (0.960 µM) set by World Health Organization (WHO). More importantly, the sensor delivers an average recovery of 104.5% and 98.25% for the detection of tap water samples. This convenient, yet accurate, quantification method for Cr 6+ in spiked tap water was compared to the certified inductive coupled plasma (ICP) method. The agreement between the measurements obtained by our sensor and the ICP method was 93.57%. Mechanistic studies using Fourier Transform Infrared Spectroscopy (FT-IR) indicate a nucleophilic attack to the carbonyl group of II-MT by HCrO 4 − , resulting in the dye color changing from brown to colorless. Overall, this novel chemosensor has a high potential for application as a selective, sensitive, and disposable paper sensor for direct and rapid screening of Cr 6+ in real world environments.
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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) | 0.001 | 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 | 0.001 |
| 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".