Selective porosification of n-InP(100) after focused ion beam implantation of Si
Bibliographic record
Abstract
Selective pore formation can be electrochemically initiated on n-type InP(100) on presensitized surfaces. To create this presensitization, defect patterns were written into the surface by focused ion beam (FIB) implantation of Si++. These implant sites represent initiation sites for pore growth or for selective material dissolution in the patterns, if anodic polarization is carried out positive to the pore formation potential (PFP) of the defective surface but cathodic to the PFP of the intact surface. A variety of internal and external factors were found to influence the selectivity of the process. Parameters that significantly affect the morphology are polarization voltage, implantation dosage, the time and the anion present in the electrolyte. In the present work it is shown, that pore formation in 1 M HF only occurs for a small potential range and a certain dosage range of the implanted Si++ ions. At other potentials or dosages, a homogenous dissolution of the implanted patterns takes place. Apart from direct selective porosification of InP surfaces, the process described can be used for a selective surface patterning.
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) | 0.001 | 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".