Ultrasensitive Speciation Analysis of Mercury in Rice by Headspace Solid Phase Microextraction Using Porous Carbons and Gas Chromatography-Dielectric Barrier Discharge Optical Emission Spectrometry
Bibliographic record
Abstract
Rice consumption is a primary pathway for human methylmercury (MeHg) exposure in inland mercury mining areas of Asia. In addition, the use of iodomethane, a common fumigant that significantly accelerates the methylation of mercury in soil under sunlight, could increase the MeHg exposure from rice. Conventional hyphenated techniques used for mercury speciation analysis are usually too costly for most developing countries. Consequently, there is an increased interest in the development of sensitive and inexpensive methods for the speciation of mercury in rice. In this work, gas chromatography (GC) coupled to dielectric barrier discharge optical emission spectrometry (DBD-OES) was developed for the speciation analysis of mercury in rice. Prior to GC-DBD-OES analysis, mercury species were derivatized to their volatile species with NaBPh4 and preconcentrated by headspace solid phase microextraction using porous carbons. Limits of detection of 0.5 μg kg(-1) (0.16 ng), 0.75 μg kg(-1) (0.24 ng), and 1.0 μg kg(-1) (0.34 ng) were obtained for Hg(2+), CH3Hg(+), and CH3CH2Hg(+), respectively, with relative standard deviations (RSDs) better than 5.2% and 6.8% for one fiber or fiber-to-fiber mode, respectively. Recoveries of 90-105% were obtained for the rice samples, demonstrating the applicability of the proposed technique. Owing to the small size, low power, and low gas consumption of DBD-OES as well as efficient extraction of mercury species by porous carbons headspace solid phase micro-extraction, the proposed technique provides several advantages including compactness, cost-effectiveness, and potential to couple with miniature GC to accomplish the field speciation of mercury in rice compared to conventional hyphenated techniques.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| 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".