Characteristics of a novel on-line micro pressurized liquid extraction method
Bibliographic record
Abstract
A novel on-line micro pressurized liquid extraction (μPLE) method is introduced, which directly interfaces miniaturized solid sample preparation with HPLC for fast analysis. The technique employs rapid heating to remove analytes from 5–10 mg samples in typically 20–40 s using only about 300 μL of solvent. The resulting extract is then internally transferred to an HPLC injector for chromatographic analysis. Results show that good analyte recoveries can be achieved, similar to conventional PLE and off-line μPLE approaches, without manual sample handling. For example, 103% ± 3% (n = 4) of the acetylsalicylic acid present in pharmaceutical tablets was extracted into methanol after 20 s at 180 °C. Further, 105% ± 9% (n = 4) of the caffeine present in a green tea sample was extracted into methanol after 40 s at 275 °C. Typical time to analysis was about 95 s total for most samples, and solvents could also be easily alternated during trials to increase extract selectivity. The on-line μPLE system was applied to the extraction of model PAHs from a biochar matrix and was found to extract 97% ± 5% (n = 4) of anthracene present in the sample after a 30 s static and 60 s dynamic extraction at 220 °C. This yield is much better than results obtained by previous approaches and is attributed to the small size, high temperature, low thermal mass, and dynamic flow of the system. Findings indicate that the on-line μPLE system can greatly assist in such extractions and provide a useful method for rapidly preparing solid samples for analysis using little solvent.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".