Sorption of Very Hydrophobic Organic Compounds onto Poly(dimethylsiloxane) and Dissolved Humic Organic Matter. 1. Adsorption or Partitioning of VHOC on PDMS-Coated Solid-Phase Microextraction FibersA Never-Ending Story?
Bibliographic record
Abstract
Solid-phase microextraction (SPME) using nonpolar fiber coatings is a very useful method for determining concentrations (more precisely, activities) of environmentally relevant very hydrophobic organic compounds (VHOC: alkanes, PCBs, and PAHs). The issue of adsorption (surface effect) versus absorption (partitioning) is of huge importance for the application of SPME to determine VHOC in environmental samples. Competition effects, which are associated with adsorption processes, would result in concentration-dependent and mixture-dependent responses. The confusion in the literature about the processes responsible for analyte extraction by the poly(dimethylsiloxane) (PDMS) fiber coatings turned out to be mainly attributed to experimental errors when applying conventional static SPME approaches. Determining fiber coating distribution coefficients ( K f ) using dynamic systems is more accurate in comparison with static systems because analyte losses in the system (due to the fiber uptake, sorption on the walls, etc.) can be compensated for, thus ensuring constant concentration of the dissolved analyte(s) during the experiment. Fiber distribution coefficients of VHOC on PDMS coatings are strongly correlated with the analyte hydrophobicity, expressed by the octanol−water partitioning coefficient ( K ow ). This indicates that partitioning between the sample and the coating is the prevailing process. Therefore, equilibrium SPME extractions in multicomponent systems allow the determi nation of concentrations of any of the VHOC, provided that the extraction is carried out in a depletion-free system and that appropriate partition coefficients of the analytes, which can be estimated on the basis of their K ow data, are available.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".