Shallow V-Shape Nanostructured Pit Arrays in Germanium Using Aqua Regia Electroless Chemical Etching
Bibliographic record
Abstract
Due to its high refractive index, reflectance is often a problem when using Germanium for optoelectronic devices integration. In this work, we propose an effective and low-cost nano-texturing method for considerably reducing the reflectance of bulk Germanium. To do so, uniform V-shape pit arrays are produced by wet electroless chemical etching in a 3:1 volume ratio of highly-concentrated hydrochloridric and nitric acids or so-called aqua regia bath using immersion times ranging from 5 to 60 min. The resulting pit morphology, the crystalline structure of the surface and the changes in surface chemistry after nano-patterning are all investigated. Finally, broadband near-infrared reflectance measurements confirm a significant reduction using this simple wet etching protocol, while maintaining a crystalline, dioxide-free, and hydrogen-passivated surface. It is important to mention that reflectance could be further reduced using deeper pits. However, most optoelectronic applications such as photodetectors and solar cells require relatively shallow patterning of the Germanium to allow formation of a pn-junction close to the surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".