Surface plasmon resonance biosensor based on graphene layer for the detection of waterborne bacteria
Bibliographic record
Abstract
As a result of the risks that waterborne bacteria bring to the human body, identifying them in drinking water has become a global concern. In this article, a highly sensitive surface plasmon resonance (SPR) biosensor consisting of prism, Ag, graphene, affinity layer and sensing medium is proposed for rapid detection of the waterborne bacteria. Four SPR-based sensors are first studied with the structures prism/Ag/sensing medium, prism/Ag/affinity layer/sensing medium, prism/Ag/graphene/sensing medium, and prism/Ag/graphene/affinity layer/sensing medium. The latter structure is found to have the highest sensitivity so it is considered for further investigations. Four different commonly used prisms are then demonstrated which are N-FK51A, 2S2G, SF10 and BK7. The structure with the prism N-FK51A is found to correspond to the highest sensitivity so it is considered for further investigations. The structure parameters are then optimized. The proposed SPR sensor can achieve high sensitivity of about 221.63 °/RIU for Escherichia coli and 178.12 °/RIU for Vibrio cholera bacteria with an average value of 199.87 °/RIU. We believe that the proposed structure will open a new window in the field of microorganism detections.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".