Iron-based trinuclear metal-organic nanostructures on a surface with local charge accumulation
Bibliographic record
Abstract
Coordination chemistry relies on harnessing active metal sites within organic matrices. Polynuclear complexes-where organic ligands bind to several metal atoms-are relevant due to their electronic/magnetic properties and potential for functional reactivity pathways. However, their synthesis remains challenging; few geometries and configurations have been achieved. Here, we synthesise-via supramolecular chemistry on a noble metal surface-one-dimensional metal-organic nanostructures composed of terpyridine (tpy)-based molecules coordinated with well-defined polynuclear iron clusters. Combining low-temperature scanning probe microscopy and density functional theory, we demonstrate that the coordination motif consists of coplanar tpy's linked via a quasi-linear tri-iron node in a mixed (positive-)valence metal-metal bond configuration. This unusual linkage is stabilised by local accumulation of electrons between cations, ligand and surface. The latter, enabled by bottom-up on-surface synthesis, yields an electronic structure that hints at a chemically active polynuclear metal centre, paving the way for nanomaterials with novel catalytic/magnetic functionalities.
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".