Hydride Generation for Headspace Solid-Phase Extraction with CdTe Quantum Dots Immobilized on Paper for Sensitive Visual Detection of Selenium
Bibliographic record
Abstract
A low-cost, simple, and highly selective analytical method was developed for sensitive visual detection of selenium in human urine both outdoors and at home, by coupling hydride generation with headspace solid-phase extraction using quantum dots (QDs) immobilized on paper. The visible fluorescence from the CdTe QDs immobilized on paper was quenched by H2Se from hydride generation reaction and headspace solid-phase extraction. The potential mechanism was investigated by using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) as well as Density Functional Theory (DFT). Potential interferences from coexisting ions, particularly Ag(+), Cu(2+), and Zn(2+), were eliminated. The selectivity was significantly increased because the selenium hydride was effectively separated from sample matrices by hydride generation. Moreover, due to the high sampling efficiency of hydride generation and headspace solid phase extraction, the sensitivity and the limit of detection (LOD) were significantly improved compared to conventional methods. A LOD of 0.1 μg L(-1) and a relative standard deviation (RSD, n = 7) of 2.4% at a concentration of 20 μg L(-1) were obtained when using a commercial spectrofluorometer as the detector. Furthermore, a visual assay based on the proposed method was developed for the detection of Se, 5 μg L(-1) of selenium in urine can be discriminated from the blank solution with the naked eye. The proposed method was validated by analysis of certified reference materials and human urine samples with satisfactory results.
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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 | 0.001 | 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".