Strong Substrate–Adsorbate Interactions Direct the Impact of Fluorinated N-Heterocyclic Carbene Monolayers on Au Surface Properties
Bibliographic record
Abstract
Fluorinated self-assembled monolayers (SAMs) have been utilized in a variety of applications such as transistors and optoelectronic devices. However, in most SAMs the fluorinated groups could not be positioned in high proximity to the surface due to steric effects. This limitation hinders the direct analysis of the impact of the fluorination level on surface properties. Herein, fluorinated aromatic N-heterocyclic carbenes (NHCs), with 1-5 fluorine atoms, were self-assembled on a gold substrate. These NHCs enabled the positioning of fluorinated groups in high proximity to the metal surface to identify the influence of the fluorination level on surface properties. Experimental measurements and theoretical calculations identified that all fluorinated NHCs formed SAMs and adopted a flat-lying adsorption configuration while anchored to the metal surface via Au adatom. A higher fluorination level induced a stronger interaction of the fluorinated side groups with the Au surface. The stronger interaction and surface proximity of the fluorinated side groups deteriorated the overall binding energy of the NHC due to the less-optimized adsorption geometry of the carbene carbon. Ultraviolet photoelectron spectroscopy measurements revealed that fluorinated NHC monolayers lowered the surface work function by up to 1 eV and induced an increase of 15-20° in the water contact angle. The impact on surface properties did not vary according to the fluorination level of NHCs, and similar values were measured for NHC with 1-5 fluorine atoms. It is therefore identified that dominant adsorbate-substrate interactions between the fluorinated side groups and the Au surface quenched the distinct impact of the fluorination level on surface functionality.
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 | 0.000 |
| 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".