Chemical Sample Preparation of Plant Materials in Tunnel-Type Microwave Decomposition Systems for Elemental Analysis
Bibliographic record
Abstract
Abstract Analysis of plant materials is necessary for environmental monitoring and analytical control of food and medicinal raw materials. A study of world experience has shown that there are still no unified schemes for chemical sample preparation that are simultaneously suitable for all types of plants without limiting the range of elements to be determined. The creation of a unified scheme for plants is possible, because the macrocomposition of any plant is represented by at least 90 wt % organic compounds (fiber, protein, lipids, etc.), the mineralization of which leads to the formation of water and a gas phase. In this work, certified plant samples are mineralized in a MultiVIEW tunnel-type microwave digestion system (SPC SCIENCE, Canada) with variations in analyzed portions, composition, and volume of reagents, variants for dosing the reaction mixture, and vessel heating modes for the simultaneous determination of a wide range of elements by inductively coupled plasma atomic emission spectrometry. Assessment of the completeness of dissolution (the degree of correspondence between the found and certified contents) is used as a criterion for the optimal conditions of sample preparation. It is shown that with a three-stage mode of heating vessels (heating rate at the first stage 2.76 K/min) at a sample of 0.5 g and the separate and sequential addition of the reaction mixture (HNO3 4, H2O2 1.5, HCl 1 and HF 0.05 mL), reliable determination of typical plant contents of Si, Al, Mg, Ca, Fe, Na, K, Ba, Sr, Rb, P, B, Mn, Ti, Ni, V, Cu, and Zn is possible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".