Galvanic Displacement Reaction Enabled Specific and Sensitive Detection of Bacteria with a Digital Photocorrosion GaAs/AlGaAs Biosensor
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The conjugation of ionic gold with bacterial antibodies makes it possible to induce a specific interaction between targeted bacteria and the surface of a GaAs/AlGaAs biochip. The process of immobilization is based on a galvanic displacement reaction (GDR) involving electron transfer between GaAs and Au 3+ ions that leads to the formation of a Au–Ga alloy anchoring bacteria to the biochip surface. The GDR-based immobilization of Escherichia coli on biochips comprising a stack of GaAs/AlGaAs nanolayers ( d GaAs = 12 nm, d AlGaAs = 10 nm) was confirmed by X-ray photoelectron spectroscopy and atomic force microscopy-based infrared experiments. We report the successful application of this approach for highly sensitive detection of E. coli with a digital photocorrosion (DIP) biosensor. The photoluminescence (PL) monitored DIP of GaAs/AlGaAs nanolayers results in the formation of a PL intensity maximum whose temporal appearance depends on the electric charge transfer between bacteria and the biochip. The formation of a robust bacteria–biochip interface achieved with the GDR process allowed us to observe the role of bacteria on the temporal position of a PL intensity maximum related to the etching of two pairs of GaAs/AlGaAs nanolayers extending up to 24 nm below the biochip surface. We demonstrate the attractive detection of E. coli at 250 CFU/mL, and we discuss the potential of this approach for designing a family of biosensors addressing the quasi-continuous monitoring of a water environment for the presence of pathogenic bacteria.
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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".