Enhancing the Sensitivity of Surface Plasmon Resonance Measurements Utilizing Polymer Film/Au Assemblies
Bibliographic record
Abstract
Surface plasmon resonance (SPR) is used to infer information about a sample that is in contact with an Au-coated glass slide coupled to the SPR prism. Shifts in the angle of the “SPR minimum reflection” can be related to changes in the refractive index (and/or thickness) of the sample that is in contact with the Au film, which can then be used to determine the concentration of an analyte in that sample. Here, we show that by depositing a layer of poly( N -isopropylacrylamide- co -acrylic acid) [p(NIPAm- co -AAc)] microgel on the SPR’s Au film, with a subsequent layer of Au deposited on top of the microgels, the sensitivity of SPR to changes in solution properties can be enhanced. We investigated the sensitivity of the SPR to changes in the temperature of water in contact with the SPR’s Au film as a function of the microgel immobilization density and the thickness of the Au layer deposited on the microgel layer. The data revealed that the SPR’s Au film densely coated with microgels, with 5 nm of Au deposited, exhibited the maximal enhancement. The plasmon coupling effect between the additional Au film on the microgels and the SPR’s Au film was further confirmed by 3D finite difference time domain simulations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".