Hyphenation of capillary high‐performance liquid chromatography with matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry for nano‐scale screening of single‐bead combinatorial libraries
Bibliographic record
Abstract
This paper focuses on the technical aspects of chemical screening from 384-well plate nano-scale single-bead combinatorial libraries. The analytical technique utilized is a combination of capillary liquid chromatography with ultraviolet detection and matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. The HPLC/MALDI-MS hyphenation is achieved by means of a micro-fraction collector with a peak detection system that automatically collects the peaks onto the MALDI targets for subsequent characterization. Several experimental parameters such as type of 384-well plate, well-plate sealing foils, and a column-switching procedure were investigated using a small test library of nine components. Additionally, the influence of different MALDI matrices, different MALDI targets and sample-spotting techniques on the MALDI detection sensitivity as well as the ruggedness and sample throughput capacity of this technique were studied. Optimum results for the analytes investigated were obtained with 2,5-dihydroxybenzoic acid using on-line mixing of HPLC effluent and matrix solution. To demonstrate the potential of this capillary HPLC/MALDI-TOFMS method, its application to several single-bead libraries was investigated. The instrumental method allowed for the rapid identification and purity assessment of combinatorial libraries with detection limits down to the higher femtomole level using both UV detection and MALDI mass spectrometry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".