Salt Segregation and Sample Cleanup on Perfluoro-Coated Nanostructured Surfaces for Laser Desorption Ionization Mass Spectrometry of Biofluid Samples
Bibliographic record
Abstract
We present a surface assisted laser desorption ionization (SALDI) technique, coupled with fluorocarbon coating, to achieve selective segregation of ionic and/or hydrophilic analytes from background biofluid electrolytes for quantiatve mass spectrometric analysis. By controlling the contact angle of (1H,1H,2H,2H-perfluorooctyl) trichlorosilane or (1H,1H,2H,2H-perfluorooctyl) dimethylchlorosilane to a specific range (105-120°), background electrolytes can be made to segregate from hydrophilic analytes during a drying step on the surface of a highly nanoporous thin film. Nanoporous silicon films were prepared using glancing angle deposition (GLAD) thin film technology, then coated with fluorcarbon. This desalting method directly separates highly polar, ionic metabolites, such as amino acids, from salty biofluids such as aritificial cerebrospinal fluid (aCSF) and serum. Derivatization, extraction and rinsing steps are not required to separate the analytes from the bioelectrolytes. With on-chip desalting, the limit of quantitation for histidine spiked in aCSF is ∼1 μM, and calibration curves with internal standards can achieve a precision of 1-9% within a 1 to 50 μM range. Five highly polar organic acids in serum were successfully quantified, and the SALDI-MS results obtained on the desalted serum sample spots show both good reproducibility and compare well to results from NMR and liquid chromatography-mass spectrometry. Putative identification of a total of 32 metabolites was accomplished in blood using time-of-flight MS with perfluoro coated Si-GLAD SALDI, by comparison to tabulated data.
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 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".