Stemless InSb nanowire networks and nanoflakes grown on InP
Bibliographic record
Abstract
Abstract Among the experimental realization of fault-tolerant topological circuits are interconnecting nanowires with minimal disorder. Out-of-plane indium antimonide (InSb) nanowire networks formed by merging are potential candidates. Yet, their growth requires a foreign material stem usually made of InP–InAs. This stem imposes limitations, which include restricting the size of the nanowire network, inducing disorder through grain boundaries and impurity incorporation. Here, we omit the stem allowing for the growth of stemless InSb nanowire networks on an InP substrate. To enable the growth without the stem, we show that a preconditioning step using arsine (AsH3) is required before InSb growth. High-yield of stemless nanowire growth is achieved by patterning the substrate with a selective-area mask with nanohole cavities, containing restricted gold droplets from which nanowires originate. Interestingly, these nanowires are bent, posing challenges for the synthesis of interconnecting nanowire networks due to merging failure. We attribute this bending to the non-homogeneous incorporation of arsenic impurities in the InSb nanowires and the interposed lattice-mismatch. By tuning the growth parameters, we can mitigate the bending, yielding large and single crystalline InSb nanowire networks and nanoflakes. The improved size and crystal quality of these nanostructures broaden the potential of this technique for fabricating advanced quantum devices.
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".