Optimization of accelerated solvent extraction for the analysis of munitions residues in sediment samples
Bibliographic record
Abstract
Abstract Accelerated solvent extraction (ASE) has been compared with a Soxhlet extraction for extracting and estimating the concentration of hexahydro‐1,3,5‐trinitro‐s‐triazine (RDX) and octahydro‐1,3,5,7‐tetrazocine (HMX) in sediment samples. Extraction of RDX and HMX was studied in terms of process kinetics and recovery. A three factor, 16 run triplicate experimental design was used to generate data on each of two extraction solvents, acetonitrile and a mixture of acetone–methanol (1:1). Response surface methods were used to model the dependence of the concentration on the experimental conditions and to establish the optimum extraction conditions. The results were complex in that several interactions were found among analyte, method, and solvent. Acetonitrile was superior to acetone–methanol for RDX and HMX from the perspectives of kinetics and recovery, due in part to a much higher solubility. The accelerated solvent extraction generally recovered more than the Soxhlet. In terms of sample throughput, the accelerated solvent extraction offers advantages over the Soxhlet. A spike recovery study using fortified sediment yielded complete recoveries of HMX and RDX. © 2001 John Wiley & Sons, Inc. J Micro Sep 13: 54–61, 2001
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".