Folate functionalized silicon nanowires with highly enhanced adhesion to cancer cells
Bibliographic record
Abstract
Abstract In this study, we implemented a functionalization process on the surface of silicon nanowires (SiNWs) which were fabricated by chemical vapour deposition (CVD) method in a low‐pressure CVD (LPCVD) chamber. Surface functionalization was carried out in three stages: hydroxylation of the surface with piranha solution, silanization with a solution of 3‐Aminopropyl triethoxysilane (APTES) in n‐hexane, and finally functionalization with folic acid as a detection agent. Optical spectroscopy and electron microscopy techniques were used to experimentally characterize materials. Surface functionalization was evaluated using Attenuated Total Reflectance Fourier‐Transform Infrared spectroscopy (FTIR‐ATR), Field Emission Scanning Electron Miscoscopy (FESEM), and Transmission Electron Microscopy (TEM). Cell adhesion to functionalized and non‐functionalized silicon nanowires was compared; a cell assay analysis on lung metastatic cells was conducted. Lung cancer metastatic QUDB cells were seeded on a SiNWs surface in RPMI1640 complete medium. To evaluate the adhesion of cancer cells to these surfaces FESEM imaging was used. The number of cancer cells trapped on the folic acid functionalized SiNWs was found to be approximately 3 times higher than that of the non‐functionalized ones.
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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 | 0.000 | 0.000 |
| 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".