Fluorescent Hydrogel‐Coated Paper/Textile as Flexible Chemosensor for Visual and Wearable Mercury(II) Detection
Bibliographic record
Abstract
Abstract In some industrial districts, abuse discharge of waste water has resulted in serious Hg2+ pollution in seafood, grain, and even drinking water. In order to protect people from mercury(II)‐polluted food and water, many solid‐state fluorescent Hg2+‐sensing materials are developed in terms of facile operation. However, one primary challenging issue is the restricted sensitivity caused by hindered slow diffusion of aqueous testing samples inside these conventional hydrophobic, dense, and rigid film materials. Herein, robust hydrophilic fluorescent hydrogel‐coated flexible paper/textile film chemosensors are reported. Their design relies on a specific chemical reaction between Hg2+ and the grafted thiourea moieties to induce remarkable “green‐to‐blue” emission color change. Thanks to their hierarchical porous structures fixed by interwoven paper/textile fibers, these flexible chemosensors allow fast capillary‐force‐driven mercury(II) diffusion into the hydrophilic hydrogel matrix, thus enabling visual detection of nearly nM‐level Hg2+. On this basis, robust fluorescent hydrogel‐coated wearable sensing gloves are fabricated for the first time, which significantly facilitate infield visual detection and effectively protect operators far from the toxic Hg2+‐polluted samples. These developed flexible wearable sensing systems might not only hold great potential applications in mercury(II) detection, but also inspire the development of next‐generation sensing apparatus for other food and environmental pollutants.
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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 | 0.000 | 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".