Surface depletion and electrical transport model of AlInP-passivated GaAs nanowires
Bibliographic record
Abstract
Fabrication, current–voltage characterization and analytical modeling of an AlInP-passivated GaAs nanowire (NW) ensemble device are presented. During fabrication, sonication was used as a novel and crucial step to ensure effective contacting of the NWs. Current–voltage characteristics of the passivated NW devices were fitted using an analytical surface depletion and transport model which improves upon established models by implementing a non-uniform density of GaAs surface states and including a NW diameter distribution. Scanning electron microscopy, capacitance–voltage characterization and secondary ion mass spectrometry were used to fix key parameters in the model. A 55% decrease in surface state density was achieved upon passivation, corresponding to an impressive four order of magnitude increase in the effective carrier concentration of the NWs. Moreover, the thickest NWs in the ensemble were found to dictate the device characteristics, which is a behavior that should be common to all ensemble NW devices with a distribution in radius. As final confirmation of effective passivation, time-resolved photoluminescence measurements showed a 25× improvement in carrier lifetime upon passivation. The fabrication and passivation methods can be easily implemented into future optoelectronic applications.
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 | 0.001 | 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".