Ultra-high aspect ratio functional nanoporous silicon via nucleated catalysts
Bibliographic record
Abstract
Nanoporous silicon (NPSi) has drawn recent interest because of its potential in a range of applications such as battery anodes, photocatalysis, thermoelectrics, and filtration membranes. However, the inexpensive and scalable manufacturing of high aspect ratio porous structures on the nanometer scale has been difficult due to the reliance of current methods on complex and expensive equipment used for techniques such as anodization or photolithography. Here, we report a method of producing NPSi with sub-10 nm pore sizes and aspect ratios as high as 400 : 1 by leveraging the nucleation of sputtered noble metals on the Si surface, followed by metal-assisted chemical etching (MACE). The technique is capable of producing NPSi in an intrinsically scalable manner. Samples are characterized with SEM and TEM, along with vertical and horizontal FIB cross-sectional milling to elucidate the porous structure at several μm of depth within the substrate. Following preparation of the NPSi, it is functionalized with Al 2 O 3 and TiO 2 via atomic layer deposition (ALD). TiO 2 -functionalized NPSi exhibits reflectivity of 6-8% for visible wavelengths, and 2-3% in the infrared-showing its promise as a robust and functional porous substrate. The developed approach of employing MACE with sputtered nucleated catalysts facilitates the scalable fabrication of functional ultra-high aspect-ratio nanopores in silicon.
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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) | 0.000 | 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) | 0.001 | 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".