A surface plasmon resonance biosensor for bacteria and virus detection: A Comsol Multiphysics simulation
Bibliographic record
Abstract
This study provides a comprehensive simulation-based investigation into the design and performance optimization of a surface plasmon resonance (SPR) biosensor. The main goal of this study is to improve sensitivity and accuracy by combining optical and colorimetric biosensing techniques. The biosensor is studied, examined, and simulated using Comsol Multiphysics. Sensing medium, black phosphorus, tungsten diselenide (WSe2), gold (Au), magnetite (Fe3O4), and N-BK7 glass as prism are the layers that make up the structure of the proposed sensor. The study evaluates various parameters such as electric potential distribution, surface temperatures, conductive heat flux, eigenfrequency, electric field norm, and temperature gradients. The use of WSe2 aims for a higher sensitivity for detecting biomolecules. This paper proves the effect of using Fe3O4 and WSe2 among the six layers of the sensor in increasing the selectivity and sensitivity of the SPR biosensor. The findings reveal intricate interactions between the biosensor layers, which influence its thermal and electromagnetic behavior. The findings of this study contribute to the advancement of SPR biosensor technology, which has the potential for a variety of applications in the biomedical field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".