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Record W4393394060 · doi:10.1134/s1061934824030134

Chemical Sample Preparation of Plant Materials in Tunnel-Type Microwave Decomposition Systems for Elemental Analysis

2024· article· en· W4393394060 on OpenAlexaboutno aff
Е. В. Шабанова, А. А. Зак, И. Е. Васильева

Bibliographic record

VenueJournal of Analytical Chemistry · 2024
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsnot available
Fundersnot available
KeywordsDecompositionMicrowaveElemental analysisSample (material)Sample preparationMaterials scienceType (biology)Environmental scienceProcess engineeringChemistryComputer scienceEngineeringInorganic chemistryGeologyOrganic chemistryChromatographyTelecommunications

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.343
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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