Desorption electrospray ionization-mass spectrometry for the detection of analytes extracted by thin-film molecularly imprinted polymers
Bibliographic record
Abstract
Desorption electrospray ionization-mass spectrometry (DESI-MS) is a powerful technique for the analysis of solid and liquid surfaces that has found numerous applications in the few years since its invention. For the first time, it is applied to the detection of analytes extracted by molecularly imprinted polymers (MIPs) in a thin-film format. MIPs formed with 2,4-dichlorophenoxyacetic acid (2,4-D) as the template were used for the extraction of this analyte from aqueous solutions spiked at concentrations of 0.0050-2.0 mg L(-1) (approximately 2 x 10(-8) to approximately 1 x 10(-5) M). The response was linear up to 0.50 mg L(-1), and then levelled off due to saturation of the active sites of the MIP. In MS/MS mode, the signal at 0.0050 mg L(-1) was still an order of magnitude higher than the signal of a blank. The MIP DESI-MS approach was also used for the analysis of tap water and river water spiked with 2,4-D and four analogues, which indicated that these analogues were also extracted to various extents. For practical applications of the MIP, a detection technique is required that can distinguish between these structurally similar compounds, and DESI-MS fulfills this purpose.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".