Functionalized Porous Silicon Surfaces as DESI-MS Substrates for Small Molecules Analysis
Bibliographic record
Abstract
In desorption electrospray ionization mass spectrometry (DESI-MS), the type of surface in addition to low gas and solvent flow rates help to avoid the "splashing of solvent" or "washing effect", by which samples are promptly removed from the surface by the spray. These effects operate on smooth surfaces and generally result in unstable signals as the spray moves over the spot. The aim of this work is to compare the performance of functionalized porous silicon surfaces (pSi) for small molecules analysis with regard to the stability of the signal and the limits of detection (LODs) observed in DESI-MS. The results showed that functional groups, like 1-decene and heptadecafluoro-1,1,2,2-tetrahydrodecyl trimethoxysilane, on pSi surface provides a good alternative for dried spot analysis by DESI-MS, improving stability of the signal and the LODs. This improvement is possible because the dual process containing the weak sample-surface interactions of the hydrophobic characteristic of the functional groups and increasing the surface area of interaction between the sample and the thin solvent film created by the DESI spray, resulting in more effective dissolution of the analyte in the spray solvent without fast removal of the sample.
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) | 0.000 | 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".