Design and Analysis of PtSe<sub>2</sub> and Blue Phosphorus/MoS<sub>2</sub> Heterostructure-Based SPR Biosensor
Bibliographic record
Abstract
Biosensors using surface plasmon resonance (SPR) have emerged as effective tools for accurate and instantaneous sensing applications. Nevertheless, optimizing sensor configurations continues to be of utmost importance to attain dependable sensing capabilities. This work presents an intriguing design structure that integrates conventional SPR technology with PtSe 2 and blue phosphorus–MoS 2 heterostructure, resulting in an improved and adaptable sensing technology. The finite-difference time-domain (FDTD) method was used to model and design the prospective sensor. The proposed design has notable attributes, such as an extraordinarily narrow full width at half-maximum (FWHM) of 8.23°, small detection accuracy of 0.1215, an extraordinary sensitivity of 240.54°/RIU, and an outstanding quality factor of 29.23 RIU –1 . Significantly, this sensor has a sensitivity that is 3.2 times higher than that of traditional gold-based SPR sensors. In addition, the sensor provides a broad observable range of refractive indices, ranging from 1.33 to 1.36. The broad spectrum of this detection system allows for the identification of diverse ambient chemicals, such as alcohol, ethanol, and water, as well as biomolecules, including urine, glucose, and DNA hybridization. These results represent a significant stride forward for SPR biosensors, allowing for the creation of high-performance sensing systems with a wide range of potential uses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".