Preparation of nanowire specimens for laser-assisted atom probe tomography
Bibliographic record
Abstract
The availability of reliable and well-engineered commercial instruments and data analysis software has led to development in recent years of robust and ergonomic atom-probe tomographs. Indeed, atom-probe tomography (APT) is now being applied to a broader range of materials classes that involve highly important scientific and technological problems in materials science and engineering. Dual-beam focused-ion beam microscopy and its application to the fabrication of APT microtip specimens have dramatically improved the ability to probe a variety of systems. However, the sample preparation is still challenging especially for emerging nanomaterials such as epitaxial nanowires which typically grow vertically on a substrate through metal-catalyzed vapor phase epitaxy. The size, morphology, density, and sensitivity to radiation damage are the most influential parameters in the preparation of nanowire specimens for APT. In this paper, we describe a step-by-step process methodology to allow a precisely controlled, damage-free transfer of individual, short silicon nanowires onto atom probe microposts. Starting with a dense array of tiny nanowires and using focused ion beam, we employed a sequence of protective layers and markers to identify the nanowire to be transferred and probed while protecting it against Ga ions during lift-off processing and tip sharpening. Based on this approach, high-quality three-dimensional atom-by-atom maps of single aluminum-catalyzed silicon nanowires are obtained using a highly focused ultraviolet laser-assisted local electrode atom probe tomograph.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".