Influence of the Particle Shape and Density of Self-Assembled Gold Nanoparticle Sensors on LSPR and SERS
Bibliographic record
Abstract
A low-cost methodology to prepare highly sensitive sensors is reported with different nanoparticles for LSPR and SERS measurements. The LSPR and SERS sensor was self-assembled as an array of polystyrene-capped gold nanoparticles (Au NPs), produced by drop-coating a Au NP suspension on a glass surface followed by plasma etching of the polystyrene. The procedure was applied to different sizes and shapes of Au NPs, primarily 60 nm gold nanospheres and 80 nm gold nanoraspberries, created with precise control on the packing density of the array. The LSPR sensitivity and figure of merit (FOM) for the arrays of gold nanoraspberries were found to be almost 2-fold better than for the nanospheres and had a shorter penetration depth, a consequence of their small-sized surface asperities. The average measured SERS enhancement factor (EF) for the raspberries was also higher than for spheres and control over the EF was achieved using different packing densities which allowed control of the interparticle coupling enhancing the SERS response. The localized EF (hot spot) in dense arrays of nanoraspberries was measured to be in the order of a typical EF required for single molecule SERS, as demonstrated from the general agreement of experimental measurements with simulations performed using Comsol Multiphysics. This simple LSPR and SERS sensor could potentially serve as an effective platform for the identification and quantification of various analytes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".