Biofunctionalization of Si(111)7×7 by “Renewable” <scp>l</scp>-Cysteine Transitional Layer
Bibliographic record
Abstract
Surface functionalization of an inorganic surface with bio-organic molecules is often aimed at creating a "permanent" bio-organic surface with receptor functional groups. We show here that L-cysteine can be used to transform a highly reactive Si(111)7×7 surface to not just a permanent bio-organic surface but also a semipermanent (or renewable) and a temporary bio-organic surfaces by manipulating the exposure. In the early growth stage, the strong bonding between the first cysteine adlayer and the Si substrate through Si-N or Si-S linkages in unidentate or bidentate arrangement provides permanent biofunctionalization by this interfacial layer. This interfacial layer can be used to build a transitional layer (second adlayer) mediated by interlayer vertical hydrogen bonding between an amino group and a carboxylic acid group. Further exposure of cysteine eventually leads to a zwitterionic multilayer film involving electrostatic interactions between cation (-NH3(+)) and anion moieties (-COO(-)). The interlayer hydrogen bonding therefore provides temporary trapping of bio-organic molecules as the second transitional layer that is stable up to 175 °C. This transitional layer can be easily removed by annealing above this temperature and then regenerated with the same molecular layer or a different one by "renewing" the interlayer hydrogen bonds. We also illustrate coverage-dependent adsorption structures of cysteine, from bidentate to unidentate attachments and to self-assembled multimers, involving formation of intralayer horizontal N···H-O hydrogen bonds, by combining our X-ray photoemission data with the local density-of-state images obtained by scanning tunnelling microscopy.
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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".