Microarray-Based Nanoextraction Combined with Ambient Ionization Mass Spectrometry for Analysis of Drugs of Abuse in Wastewater
Bibliographic record
Abstract
There is a significant need to simplify the analytical workflow for complex sample analysis. In particular, those innovations that are high-throughput, miniaturized, reliable, and more-resource conscientious are especially important. Given these considerations, a nanoextraction microarray composed of sessile microdroplets is herein proposed; this nanoextraction is achieved using a long-chain organic acid, namely, myristic acid, as the extractant. Different drugs of abuse (i.e., cocaine, fentanyl, methamphetamine, and oxycodone) in wastewater were used as the analyte:sample model. Under the optimized conditions (i.e., pH = 10, 23 nL of myristic acid as the extractant, and 20 min as the extraction time), the method was applied to a complex matrix (i.e., wastewater) as a proof-of-principle, giving limits of detection and preconcentration factors in the ranges of 0.194-0.871 μg/L and 2.1-9.9, respectively. The chemically green(er) organic acid extractant, high preconcentration factors, and throughput are advantages of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".