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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".