Soft-landing electrospray ion beam deposition of sensitive oligoynes on surfaces in vacuum
Bibliographic record
Abstract
Advances in synthetic chemistry permit the synthesis of large, highly functional, organic molecules. Characterizing the complex structure of such molecules with highly resolving, vacuum-based methods like scanning probe microscopy requires their transfer into the gas phase and further onto an atomically clean surface in ultrahigh vacuum without causing additional contamination. Conventionally this is done via sublimation in vacuum. However, similar to biological molecules, large synthetic compounds can be non-volatile and decompose upon heating. Soft-landing ion beam deposition using soft ionization methods represents an alternative approach to vacuum deposition. Using different oligoyne derivatives of the form of R1(CC)nR2, here we demonstrate that even sensitive molecules can be handled by soft-landing electrospray ion beam deposition. We generate intact molecular ions as well as fragment ions with intact hexayne parts and deposit them on clean metal surfaces. Scanning tunneling microscopy shows that the reactive hexayne segments of the molecules of six conjugated triple bonds are intact. The molecules agglomerate into ribbon-like islands, whose internal structure can be steered by the choice of the substituents. Our results suggest the use of ion beam deposition to arrange reactive precursors for subsequent polymerization reactions.
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".