Gold-Poly(methyl methacrylate) Nanocomposite Films for Plasmonic Biosensing Applications
Bibliographic record
Abstract
Gold-poly(methyl methacrylate) nanocomposites are prepared by an in situ method, by irradiating spin-coated films containing the polymer and the gold precursor dissolved in acetone. The reduction of gold ions results in the formation of Au that nucleates and grows within the polymer film. It is shown that, depending on the energy source, gold nanoparticles with different shapes can be formed. Nanocomposites prepared through UV-, thermal-, and MW-irradiation, respectively, show a low sensitivity toward the environment. However, by annealing the samples at temperatures well above the glass transition temperature of the polymer, the response to dielectric environment appears to be enhanced significantly. The sensitivity of samples synthesized through the three different methods is found to be comparable, around 100 nm/RIU. The increased sensitivity of the annealed sample is accounted for by the increased mobility of both polymer chains and gold nanoparticles in the rubbery state of the material and the presence of the monomer. Gold nanoparticles “freed” from the strong interaction with the polymer are now able to feel the molecules from the surrounding environment. The results show that, by using adequate post-synthesis heat treatments, gold-polymer nanocomposites can be used as plasmonic sensing platforms.
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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 | 0.000 |
| 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".