Consequence of Galvanic Displacement Reaction on Digital Photocorrosion of GaAs/Al<sub>0.35</sub>Ga<sub>0.65</sub>As Nanoheterostructures
Bibliographic record
Abstract
GaAs/AlGaAs semiconductor nanoheterostructures have found attractive application in the field of biosensing based on the effect of digital photocorrosion (DIP). The sensitivity of semiconductor–nanoheterostructure-based biosensors depends on the precision of controlling the process of DIP, which is highly sensitive to the surface presence of electrically charged biomolecules. To explore further this process, we have investigated the role of a galvanic displacement (GD) reaction on DIP of GaAs/Al0.35Ga0.65As (001) nanoheterostructures. Deposition of ionic gold on the GaAs surface induces spontaneous electron transfer between the semiconductor and ionic gold, which affects the photocorrosion of GaAs/AlGaAs layers observed simultaneously with the photoluminescence effect. The immediate consequence of the electron transfer from GaAs toward Au3+ is a significantly increased rate of DIP. At the same time, the formation of neutralized gold nanoparticles and Au–Ga alloy takes place on the surface of photocorroding nanoheterostructures. In the presence of 2.8% solution of ammonia and 0.1 mM gold chloride, a significantly reduced rate of deposition of gold nanoparticles is observed. This allows achieving layer-by-layer removal of the investigated material, which is sensitive to perturbations induced by surface immobilized electrically charged molecules. We have elaborated various factors stimulating photocorrosion of the GaAs/Al0.35Ga0.65As nanoheterostructures and we demonstrate the biosensing potential of an innovative GD-based DIP sensor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".