Optimization of a localized surface plasmon resonance biosensor for heat shock protein 70
Bibliographic record
Abstract
Localized surface plasmon resonance, a property characteristic of metal nanoparticles, is a promising technique for the development of low cost, rapid, and portable biosensors for a variety of medical diagnostic applications. In order to meet the demanding detection limits required for many such applications, performance improvements are required. Designing nanoparticle structures to maximize refractive index sensitivity and optimize the electromagnetic field decay length is one approach to achieving better performance. However, experimentally finding the optimal nanoparticle structure, as has been done in the past, is time consuming and costly, and needs to be done for each biomolecule of interest. Instead, simulations can be used to find the optimal nanoparticle design prior to fabrication. In this paper, we present a numerical modeling technique that allows the design of optimal nanoparticles for LSPR biosensors, and report on the effect of the size and shape of gold nanoparticles on the sensitivity and decay length. The results are used to determine the optimal nanoparticle geometry for an LSPR immunosensor for heat shock protein 70, an important protein with applications in medical and wildlife diagnostics. Our simulations show an improvement of 373% in sensor response when using the optimal configuration, showcasing the significant advantages of proper nanoparticle design.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".