Photoluminescence quenching of colloidal silver nanoparticle on porous silicon
Bibliographic record
Abstract
Photoluminescent porous Si (pSi) is a potentially attractive material for biosensor devices. Its ease of fabrication, large active surface area and unique optical properties are just some important attributes. Among other transduction techniques, it is possible to monitor the onset of molecular binding events through the effective quenching of the bright pSi photoluminescence. Here we present the study of effective quenching through a colloidal Ag nanoparticle interaction with pSi. Placing the metallic nanoparticles in close proximity to the light emitting pSi can effectively sweep away the charge carriers from the semiconductor surface and result in a carrier depletion region near the Si-nanoparticle interface. By labeling the targeted bio-species with a silver nanoparticle, and the pSi surface with an appropriate receptor molecule ; in-situ PL monitoring can provide a real-time transduction scheme for the pSi- based biosensor devices.
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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) | 0.001 | 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 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".