Bibliographic record
Abstract
The sample preparation step in an analytical process typically consists of extraction of components of interest from a sample matrix. This procedure can vary in degree of selectivity, speed, and convenience depending on the approach and conditions used as well as on geometric configurations of the extraction phase and conditions. Optimization of this process aids enhancement in performance of the overall analysis. Proper design of the extraction devices and procedures facilitates rapid and convenient on-site implementation, coupling to separationquantification, and (or) automation. The key to rational choice, optimization, and design is an understanding of fundamental principles governing mass transfer of analytes in multiphase systems. There is a tendency to divide extraction techniques according to random criteria. In this article, common principles among different extraction techniques are emphasized and a unified approach based on convolution of mathematical functions describing individual steps is presented. This approach considers gas, solvent, liquid polymer, and solid surfaces as extraction phases and air, water, and solids as sample matrices. The parameters that affect the kinetics of extraction techniques are emphasized resulting in new calibration strategies and novel geometric designs.Key words: separations, extractions, microextractions, mass transfer, multiphase equilibria.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".