Real-Time Imaging of On-Surface Ullmann Polymerization Reveals an Inhibiting Effect of Adatoms
Bibliographic record
Abstract
Ullmann coupling is a widely used reaction for the on-surface growth of low-dimensional carbon nanomaterials. The irreversible nature of this reaction prevents the “self-healing” of defects, and a detailed knowledge of its mechanism is therefore essential to enable the growth of extended ordered structures. However, the dynamics of the Ullmann polymerization remain largely unexplored, as coupling events occur on a timescale faster than conventional scanning probe microscopy imaging frequencies. Here, we reveal the dynamics of these surface events using high-speed variable-temperature scanning tunneling microscopy (STM) (10 frames per second). Performing the measurements at the onset reaction temperatures provides an unprecedented description of the evolution of organometallic (OM) and covalent surface species during the Ullmann polymerization of para -dibromobenzene on Cu(110). Our results demonstrate the existence of an intermediate OM phase with Cu adatoms that inhibits the polymerization. These observations now complete the picture of the pathways of on-surface Ullmann polymerization, which includes the complex interplay of the phenylene moieties and metal atoms. Our work demonstrates the unique capability of high-speed STM to capture the dynamics of molecular self-assembly and coupling.
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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 | 0.001 |
| 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".