Effect of Gradual Heating of Gold Nanoparticle Multilayers on Polymer Substrates on the Characteristics of their LSPR Bands
Bibliographic record
Abstract
Polymer materials have become increasingly useful for photonic sensing applications.Their consistency and optical properties make them highly desirable for sensing possibilities.Bio-molecules, such as proteins, can be attached to a polymer substrate using a thin layer of gold nanoparticle.Gold nanoparticles (GNP) hold unique localized plasmon resonance (LSPR) properties, which are affected by their structure, shape, distribution, and their degree of penetration into the surrounding medium.As well, the dielectric properties of the medium influence LSPR responses.These characteristics, in turn, depend on the thermal history of the sample, that is, the extent and duration of heating of the polymer-GNP systems.In this work, we examine the specific effects of gradually increasing temperature through incremental heating on the LSPR response of GNP on various polymers, including cyclic olefin copolymer (COC), poly (methyl methacrylate) (PMMA), poly (dimethyl siloxane) (PDMS), SU-8, polycarbonate (PC) and polystyrene (PS).GNP on polymers manifested a shift in the LSPR peak response from 7 nm to 34 nm on average, and an important change in absorbance as well.These results will help enhance the sensing performance of microphotonic sensors by improving the shape of the LSPR band and selecting the ideal polymer material for specific applications.The information will also benefit other applications, including cell culture and biomolecular separation in a microfluidic environment.Furthermore, the findings herein may be employed towards developing a unidirectional temperature sensor.
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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".