Catalyst Self-Assembly for Scalable Patterning of Sub 10 nm Ultrahigh Aspect Ratio Nanopores in Silicon
Bibliographic record
Abstract
Nanoporous silicon (NPSi) has received significant attention for its potential to contribute to a large number of applications, but has not yet been extensively implemented because of the inability of current state-of-the-art nanofabrication techniques to achieve sufficiently small pore size, high aspect ratio, and process scalability. In this work we describe the fabrication of NPSi via a modified metal-assisted chemical etching (MACE) process in which silica-shell gold nanoparticle (SiO2-AuNP) monolayers self-assemble from solution onto a silicon substrate. Exposure to the MACE etchant solution results in the rapid consumption of the SiO2 spacer shell, leaving well-spaced arrays of bare AuNPs on the substrate surface. Particles then begin to catalyze the etching of nanopore arrays without interruption, resulting in the formation of highly anisotropic individual pores. The excellent directionality of pore formation is thought to be promoted by the homogeneous interparticle spacing of the gold core nanocatalysts, which allow for even hole injection and subsequent etching along preferred crystallographic orientations. Electron microscopy and image analysis confirm the ability of the developed technique to produce micrometer-scale arrays of sub 10 nm nanopores with narrow size distributions and aspect ratios of over 100:1. By introducing a scalable process for obtaining high aspect ratio pores in a novel size regime, this work opens the door to implementation of NPSi in numerous devices and applications.
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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".