Selective Hybridization of a Terpyridine-Based Molecule with a Noble Metal
Bibliographic record
Abstract
The electronic properties of metal–molecule interfaces can in principle be controlled by molecular design and self-assembly, yielding great potential for future nano- and optoelectronic technologies. However, the coupling between molecular orbitals and the electronic states of the surface can significantly influence molecular states. In particular, molecules designed to create metal–organic self-assembled networks have functional groups that by necessity are designed to interact strongly with metals. Here, we investigate the adsorption interactions of a terpyridine (tpy)-based molecule on a noble metal, Ag(111), by low-temperature scanning tunneling microscopy (STM) and spectroscopy (STS) together with density functional theory (DFT) calculations. By comparing the local density of states (DOS) information gained from STS for the molecule on the bare Ag(111) surface with that of the molecule decoupled from the underlying metal by a NaCl bilayer, we find that tpy-localized orbitals hybridize strongly with the metal substrate. Meanwhile, those related to the phenyl rings that link the two terminal tpy groups are less influenced by the interaction with the surface. The selective hybridization of the tpy groups provides an example of strong, orbital-specific electronic coupling between a functional group and a noble-metal surface, which may alter the intended balance of interactions and resulting electronic behavior of the molecule–metal interface.
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) | 0.002 | 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".