Limits of elemental contrast by low energy electron point source holography
Bibliographic record
Abstract
Motivated by the need for less destructive imaging of nanostructures, we pursue point-source in-line holography (also known as point projection microscopy, or PPM) with very low energy electrons (∼100 eV). This technique exploits the recent creation of ultrasharp and robust nanotips, which can field emit electrons from a single atom at their apex, thus creating a path to an extremely coherent source of electrons for holography. Our method has the potential to achieve atom resolved images of nanostructures including biological molecules. We demonstrate a further advantage of PPM emerging from the fact that the very low energy electrons employed experience a large elastic scattering cross section relative to many-kV electrons. Moreover, the variation of scattering factors as a function of atom type allows for enhanced elemental contrast. The idea that one can obtain chemical information about a sample whereas imaging it by PPM is worth pursuing in more detail, and here we undertake simulations of this scheme to assess its feasibility to first order. Model results for small molecules and adatoms on graphene substrates, where very small damage is expected, indicate that a phase contrast is obtainable between elements with significantly different Z-numbers. For example, for typical setup parameters, atoms such as C and P are discernible, whereas C and N are not.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".