pH-Dependent Photocorrosion of GaAs/AlGaAs Quantum Well Microstructures
Bibliographic record
Abstract
Semiconductor microstructures comprising stacks of GaAs/AlGaAs layers have found attractive applications for photocorrosion-based detection of electrically charged molecules immobilized in the vicinity of their surfaces. We have investigated sensitivity of the photocorrosion of GaAs/AlGaAs microstructures with a stack of 30 GaAs quantum well (QW) layers to pH of aqueous solutions ranging between 2.2 and 11.2. The effect was studied by measuring QW emission for bare and (3-mercaptopropyl)-trimethoxysilane (MPTMS) coated microstructures. It has been determined that in highly both acidic (pH 2.2) and alkaline (pH 11.2) solutions the uncoated microstructures photocorrode at relatively high rates (∼0.83 nm/min), while in moderate pH (7–9) solutions the photocorrosion proceeds at rates reduced to ∼0.33 nm/min, suggesting that some oxides accumulate on the in situ revealed surfaces of GaAs and Al 0.35 Ga 0.65 As. The photocorrosion at a moderate-to-high pH 10.2 revealed the formation of a series of well-defined PL maxima each time the photocorrosion front passes from the GaAs to the Al 0.35 Ga 0.65 As surface. For MPTMS functionalized samples, a series of similar origin, well-defined PL maxima have been observed in a more alkaline solution characterized by pH of 11.2. A delayed position of the first PL maximum illustrates the passivation function of the MPTMS layer, although its depolymerization is observed in a highly alkaline environment.
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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 | 0.001 | 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 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".