Taking a Little off the Top: Nanorod Array Morphology and Growth Studied by Focused Ion Beam Tomography
Bibliographic record
Abstract
The high surface area, large aspect ratio, and porous nature of nanorod arrays make them excellent foundation materials for many devices. Of the many synthesis techniques for forming nanorods, glancing angle deposition (GLAD) offers one of the more straightforward and flexible methods for ensuring control of alignment, porosity, and architecture of the nanorods. Here we demonstrate the first use of a dual-beam (focused ion beam (FIB) combined with scanning electron microscopy (SEM)) instrument to section and image the internal morphology of a nanorod array fabricated using the GLAD technique. We have used the FIB-SEM to reconstruct the 3D composition of TiO(2) nanorods, allowing us to visualize for the first time the core structures of many potential devices. We have also been able to probe the relationship between critical parameters such as diameter (w(act)), internanorod spacing (ν(act)), center-to-center spacing (c(act)), and nanorod population density (d(act)) and the depth of the nanocolumn (t) for a single homogeneous structure. A continuous data set was obtained from a single 5-μm-thick GLAD film, avoiding the artifacts arising from the analysis of the top surfaces of multiple samples of varying thicknesses. An analysis of the acquired sectioned data has allowed us to determine that the critical nanocolumn parameters follow a power-law scaling trend with w(act) = 9.4t(0.35) nm, ν(act) = 15.2t(0.25) nm, c(act) = 24.8t(0.31) nm, and d(act) = 3402t(-0.65) columns μm(-2). Using the FIB/SEM images acquired for the TiO(2) nanorods, we have also investigated the evolution of individual nanocolumns and have observed that bifurcation and branching play a significant role in the extinction or survival of these nanorods. These findings will allow for the optimization of nanorod properties for device applications. Also, the FIB sectioning and reconstruction process developed here will permit for the investigation of nanorod arrays formed from a range of synthesis techniques and materials.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".