Developing surface plasmon resonance imaging for discrete particle detection based on a silver layer coated with polyacrylic acid/iodine polyelectrolyte brushes
Bibliographic record
Abstract
The detection and analysis of low concentrations of chemical and biological particles presents an enduring challenge in scientific exploration. Among the various techniques employed for this purpose, surface plasmon resonance (SPR) stands as a powerful label-free method with a wide-range applications in advanced detection and sensing. Traditional SPR, while highly effective, encounters inherent limitations when it comes to scrutinizing individual particles. To overcome this limitation, wide-field surface plasmon resonance microscopy emerges as a promising approach, offering real-time detection capabilities for suspended particles in solution. In this study, an innovative wide-field surface plasmon resonance microscope is presented, strategically combining a silver layer coated with polyelectrolyte brushes—polyacrylic acid/iodine, to enhance the detection sensitivity and mitigate silver’s susceptibility to oxidation. It is demonstrated that coating the silver layer with polyacrylic acid/iodine enhances the sensitivity of discrete particle imaging with a high spatial resolution of the recorded image. Since wide-field surface plasmon resonance microscopy can detect discrete particles, a mathematical model is proposed to describe the SPR sensing mechanism based on discrete particles for precisely characterizing and interpreting the experimental observations. This work demonstrates a capability for comprehensive analysis of low concentrations of chemical and biological particles at the single particle level.
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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 | 0.001 |
| 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".