Large‐Scale Formation of Uniform Porous Ge Nanostructures with Tunable Physical Properties
Bibliographic record
Abstract
Abstract Porous germanium (PGe) nanostructures attract a lot of attention for various emerging applications due to their unique properties. Consequently, there is an increasing need for the development of low‐cost synthesis routes that are compatible with large‐scale production. Bipolar electrochemical etching (BEE) is a widely used solution for producing porous Ge layers. However, the lack of controllable production of large‐scale uniform PGe layers is the limiting factor for mainstream applications. Large‐scale homogeneous PGe layers formation is demonstrated by improving the BEE process. The PGe structures demonstrate excellent homogeneity in thickness and porosity, with a relative variation of below 2% across the 100 mm wafer. Furthermore, this process enables accurate tuning of the PGe's physical properties through variation of the etching parameters. PGe structures with porosity ranging from 40% to 80% and an adjustable thickness, while preserving low surface roughness are demonstrated, giving access to a large variety of PGe nanostructures for a wide range of applications. Ellipsometry and X‐ray reflectivity are employed to measure the porosity and thickness of PGe layers, providing fast and non‐destructive methods of characterization. These findings lay the groundwork for the large‐scale production of high‐quality PGe layers with on‐demand characteristics.
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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) | 0.000 | 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 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".