Photoluminescence model for a hybrid aptamer-GaAs optical biosensor
Bibliographic record
Abstract
The present work describes the development of a hybrid GaAs-aptamers biosensor for the label-free detection of adenosine 5′-triphosphate (ATP). The implemented sensing strategy relies on the sensitivity of the GaAs photoluminescence (PL) emission to the local environment at its surface. Specifically, GaAs substrates were chemically modified with thiol-derivatized oligonucleotide aptamers following conventional condensed-phase deposition techniques and exposed to the target ATP molecules. The resulting modification in the PL intensity is attributed to a specific biorecognition interaction between the aptamer receptors and the ATP target and, more importantly, the accompanying ligand-induced structural change in the aptamer conformation. Since the negatively charged aptamer probes are covalently anchored to the substrate surface, the sensing mechanism can be understood in terms of a change in the surface charge distribution and thereby, the width of the nonemissive GaAs surface depletion layer. Biosensors fabricated with aptamer probes of various lengths indicate a strand length-dependent nature of the luminescence response to the biorecognition events, with longer aptamers yielding a greater PL enhancement. Results provided by several control experiments demonstrate the sensitivity, specificity, and selectivity of the proposed biosensor in accurately identifying ATP. Modeling the performance data by means of Poisson–Boltzmann statistics in combination with the GaAs depletion layer model shows a good correlation between the structural conformation of the aptamers and the PL yield of the underlying substrate. Collectively, the results described within indicate the promise of the prospective luminescence-based GaAs-aptamer biosensor for use in real-time sensing assays requiring a straightforward and efficient means of label-free analytical detection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".