Detecting Cr<sup>6+</sup> at ≈100 pM Concentration with Fluorescence Enhancement Signatures in a Novel Eco‐Fluorophore: Matching WHO's 96 pM Recommended Standard for Drinking Water
Bibliographic record
Abstract
Abstract Hexavalent chromium (Cr6+) ions in drinking water pose a significant risk to human health, being a leading cause for neurological disorders, organ damage, and infertility. This study introduces an ultrasensitive method for detecting trace Cr6+ over a wide concentration range (≈ 100 pM – 100 µM) through fluorescence enhancement signatures via integration of both covalent and non‐covalent interaction strategies on carbon quantum dots (CQD). The covalent functionalization is achieved from dual‐functionalized CQD (CQD‐(NH2, COOH)) derived from coffee‐waste. Additionally, the covalent and non‐covalent approach integrates CQD‐(NH2, COOH) with graphitic carbon nitride (g‐C3N4) to form a 2D/2D heterostructure. The synergy between CQD‐(NH2, COOH) and g‐C3N4 introduces a mid‐gap band in their band structure, allowing multiple carrier excitation and recombination states, significantly enhancing the fluorescence quenching signal. This combination allows to achieve Cr6+ detection sensitivity down to ≈100 pM concentration—matching the World Health Organization's 96 pM permissible limit of total Cr in drinking water. Furthermore, a 70 pM detection limit is reported for Cr6+ in a mixture of twelve ions, including cations and anions, surpassing current state‐of‐the‐art detection limits. These results highlight the potential of dual covalent and non‐covalent modification strategy in nanomaterials to set new standards in ultrasensitive and wide‐range fluorescent sensing applications.
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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 | 0.000 |
| Open science | 0.000 | 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".