In-Tube Solid-Phase Microextraction Coupled to Capillary LC for Carbamate Analysis in Water Samples
Bibliographic record
Abstract
Recently, the on-line sample preparation technique, intube solid-phase microextraction (SPME), was successfully implemented with a Hewlett-Packard 1100 HPLC system for analysis of carbamates in water samples. This paper describes the coupling of in-tube SPME to capillary LC and explores its utility as a sample preparation method in that format, relative to conventional LC. The Hewlett-Packard HPLC system was upgraded to a capillary LC system using commercially available accessories from LC Packings. The combination of in-tube SPME with a capillary LC system was expected to build on the merits of both in-tube SPME and the capillary LC to generate a sensitive method with an easy, effective, and efficient sample preparation. Due to the relatively large effective injection volume of the in-tube SPME technique (30-45 microL), on-column focusing was employed in order to achieve good chromatographic efficiency. Excellent sensitivity was achieved with very good method precision. For all carbamates studied, the RSD of retention time was between 0.5 and 0.8% under 4 microL/min microgradient conditions. The RSD of peak area counts was between 1.5 and 4.6%. The detection limits for all carbamates studied were less than 0.3 microg/L and, for carbaryl, just 0.02 microg/L (20 ppt). Compared with the conventional in-tube SPME/LC method, the LODs were lowered for carbaryl, propham, methiocarb, promecarb, chlorpropham, and barban, by factors of 24, 45, 42, 81, 62, and 56, respectively. The optimized method was successfully applied to the analysis of carbamates in surface water samples.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".