Regeneration of a thiolated and antibody functionalized GaAs (001) surface using wet chemical processes
Bibliographic record
Abstract
Wet chemical processes were investigated to remove alkanethiol self-assembled monolayers (SAMs) and regenerate GaAs (001) samples studied in the context of the development of reusable devices for biosensing applications. The authors focused on 16-mercaptohexadecanoic acid (MHDA) SAMs that are commonly used to produce an interface between antibodies or others proteins and metallic or semiconductor substrates. As determined by Fourier transform infrared absorption spectroscopy, among the investigated solutions of HCl, H2O2, and NH4OH, the highest efficiency in removing alkanethiol SAM from GaAs was shown by NH4OH:H2O2 (3:1 volume ratio) diluted in H2O. The authors observed that this result was related to chemical etching of GaAs that even in a weak solution of NH4OH:H2O2:H2O (3:1:100) proceeded at a rate of 130 nm/min. The surface revealed by a 2-min etching under these conditions allowed depositing successfully a new MHDA SAM with comparable quality and density to the initial coating. This work provides an important view on the perspective of the development of a family of cost-effective GaAs-based biosensors designed for repetitive detection of a variety of biomolecules immobilized with dedicated antibody architectures.
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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) | 0.000 | 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 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".