Super-assembled sandwich-like Au@MSN@Ag nanomatrices for high-throughput and efficient detection of small biomolecules
Bibliographic record
Abstract
Small biomolecules (m/z < 500) are the material basis of organisms and participate in life activities, but their comprehensive and accurate detection in complex samples remains a challenge. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a powerful detection tool for molecular analysis with high throughput. The development of a new matrix is essential to improve the efficiency of the MALDI-MS for molecular compound detection. In this work, the sandwich-like gold nanoparticles@mesoporous silica nanocomposite@silver nanoparticles (Au@MSN@Ag) nanospheres were prepared by layer-by-layer super-assembly strategy, and can be used as a novel matrix for the quantitative detection and enrichment of small biomolecules by LDI-MS. The sandwich-like nanospheres form a unique plasma resonant cavity that effectively absorbs the laser energy, while the homogeneous mesoporous structure of MSN can lock the analyte, which is essential for efficient LDI of small molecules. Compared to traditional matrices, Au@MSN@Ag shows the advantages of low background, wide application range, high sensitivity, super high salt and protein tolerance, and good stability. For example, the detection limit of glucose was as low as 5 fmol, and showed a good linear relationship in the range of 1–750 µg/mL. Au@MSN@Ag assisted LDI-MS allows the enrichment and detection of small molecules in traditional Chinese medicine (TCM) without derivatization and purification, classification of herbs using the accurate quantitative results oligosaccharides, and identification of gelatin by amino acid content. This research could help in designing more efficient nanostructure matrices and further explored the application of LDI-MS.
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 | 0.001 | 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) | 0.000 | 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".