Preparation of nanowire specimens for laser-assisted atom probe tomography
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it