Atomic Force Microscopy for Molecular Structure Elucidation
Bibliographic record
Abstract
Using scanning probe microscopy techniques, at low temperatures and in ultrahigh vacuum, individual molecules adsorbed on surfaces can be probed with ultrahigh resolution to determine their structure and details of their conformation, configuration, charge states, aromaticity, and the contributions of resonance structures. Functionalizing the tip of an atomic force microscope with a CO molecule enabled atomic-resolution imaging of single molecules, and measurement of their adsorption geometry and bond-order relations. In addition, by using scanning tunneling microscopy and Kelvin probe force microscopy, the density of the molecular frontier orbitals and the electric charge distribution within molecules can be mapped. Combining these techniques yields a high-resolution tool for the identification and characterization of individual molecules. The single-molecule sensitivity and the possibility of atom manipulation to induce chemical reactions with the tip of the microscope open up unique applications in chemistry, and differentiate scanning probe microscopy from conventional methods for molecular structure elucidation. Besides being an aid for challenging cases in natural product identification, atomic force microscopy has been shown to be a powerful tool for the investigation of on-surface reactions and the characterization of radicals and molecular mixtures. Herein we review the progress that high-resolution scanning probe microscopy with functionalized tips has made for molecular structure identification and characterization, and discuss the challenges it will face in the years to come.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".