Chemical sample preparation of plant materials in tunnel-type microwave digestion systems for elemental analysis
Bibliographic record
Abstract
Analyzing plant materials is essential for environmental monitoring, analytical control of food products, and medicinal raw materials. A review of global practices has shown that there are still no standard proceedings for chemical sample preparation suitable for all plant types without restrictions on the range of elements determined. Creating a standardized scheme for plants is feasible, as the macro composition of any plants consists of at least 90% organic compounds (cellulose, protein, lipids, etc.), whose mineralization results in the formation of water and a gaseous phase. In this study, certified plant samples were mineralized in a tunnel-type microwave digestion system MultiVIEW (SPC SCIENCE, Canada) with variations in analytical sample sizes, composition and volume of reagents, options for adding the reaction mixture, and vessel heating modes for simultaneous determination of a wide range of elements using inductively coupled plasma atomic emission spectrometry. The completeness of dissolution (the degree of correspondence between found and certified contents) was used as one of the criteria for the optimality of sample preparation conditions. It was shown that with a three-stage heating regime of the vessels (heating rate at the first stage 2.76 оC/min) with a sample weight of 0.5 g and separate sequential addition of the reaction mixture (4 ml HNO3, 1.5 ml H2O2, 1 ml HCl, and 0.05 ml HF), it is possible to reliably determine typical plant contents of Si, Al, Mg, Ca, Fe, Na, K, Ba, Sr, Rb, P, B, Mn, Ti, Ni, V, Cu, Zn.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".