Breaching the 10 Second Barrier of Total Analysis Time for Complex Matrices via Automated Coated Blade Spray
Bibliographic record
Abstract
In the development of modern analytical workflows, parameters such as sample turnaround time, cost of analysis, and ease of use must be prioritized. Automation enables reductions in total analysis time, human intervention, and cost per sample. In this report, a suitable automated coated blade spray (CBS) workflow is proposed for the screening and quantitation of multiple substances (i.e., drugs of abuse and pesticides) in complex matrices. In an attempt to reduce the total sample analysis time, several parameters were investigated, including tandem mass spectrometry (MS) dwell time, CBS spray time, and extraction time. Solid-phase microextraction (SPME) method parameters are explored, such as reduction of extraction time for increased signal-to-noise. Model compounds with a moderately wide range of molecular weights (150-500 Da), polarities, and structural diversity were selected in order to monitor analytical figures of merit during method optimization. The resultant automated CBS method proved capable of analyzing the model compounds in human urine in under 10 s total analysis time with excellent accuracy (95-120%) and precision (RSD < 12%). As an application, an automated method for the screening and quantitation of more than 150 pesticides from apple juice was demonstrated on both triple quadrupole and orbitrap instruments in under 15 s total sample analysis time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.099 | 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 teacher head, 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".