Molecularly Imprinted Membrane Electrospray Ionization for Direct Sample Analyses
Bibliographic record
Abstract
Typically dealing with practical samples with very complex matrices, ambient ionization mass spectrometry suffers from low detection sensitivity. In this study, molecular imprinting technology was explored and integrated with the membrane electrospray ionization (MESI) method for direct sample analyses. By enriching targeted analytes on molecularly imprinted membranes (MIMs), improvement (by 10- to 50-fold) in the limit of quantitation could be achieved, compared to conventional nanoelectrospray ionization methods or other ambient ionization methods. MIMs were prepared by cross-linking a synthesized molecularly imprinted polymer layer onto a polyvinylidene difluoride (PVDF) membrane. The characteristics of MIM in recognizing target analytes were investigated and verified. Experiments showed that MIM-ESI could provide satisfactory performances for direct quantification of targeted analytes in complex samples using mass spectroscopy (MS), and the quantitative performance of this methodology was validated. With the capability of target enrichment, the uses of MIM-ESI MS in different application fields were also demonstrated, including food safety, quantification of drug concentrations in blood, pesticide residues in soil, and antibiotic residues in milk.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".