Low-Temperature and Atmospheric Pressure Sample Digestion Using Dielectric Barrier Discharge
Bibliographic record
Abstract
A new sample digestion method using a double layer and coaxial dielectric barrier discharge (DBD) digestion reactor was developed for the sensitive determination of trace elements in rice samples. All the operation parameters of the DBD microplasma and other digestion conditions were carefully optimized. Three DBD-digestion modes were investigated for real matrix samples, including H 2 O-DBD-digestion, H 2 O 2 -DBD-digestion, and HNO 3 -DBD-digestion systems. Among the three modes, the H 2 O-DBD-digestion system provides a suitable digestion of sample without any additional chemicals, achieving environmental friendly sample treatment and eliminating the potential interferences. Under the optimized conditions, limits of detection for Mg, Mn, Zn, Cd, Cr, Co, and As were in the range of 0.01–0.35 ng g –1 by inductively coupled plasma mass spectrometry (ICPMS). The accuracy of the proposed method was checked by analysis of a certified reference material (GBW10043) and spiked samples with satisfactory results (83–113% recoveries).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".