Highly sensitive ZnO/Ag/BaTiO<sub>3</sub>/MoS<sub>2</sub> hybrid structure-based surface plasmon biosensor for the detection of mycobacterium tuberculosis bacteria
Why this work is in the frame
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Bibliographic record
Abstract
This study presents a novel biosensor utilizing surface plasmon resonance (SPR) technology, comprising og zinc oxide (ZnO), silver (Ag), barium titanate (BaTiO 3 ), and molybdenum disulfide (MoS 2 ). The detection of mycobacterium tuberculosis bacteria was accomplished through the utilization of the hybrid structure. The transfer matrix method (TMM) and finite element method are employed to analyze the suggested surface plasmon resonance (SPR) structure. A comparative analysis has been conducted to evaluate the angular sensitivity between normal blood samples (NBS) and cells affected by tuberculosis (TB). The optimization of the performance of the surface plasmon resonance (SPR) structure involves adjusting the thickness of ZnO, Ag and BaTiO 3 layer. The accurate measurement of the full width at half maximum (FWHM), detection accuracy (DA), quality factor and figure of merits (FOM) has also been conducted. The optimal angular sensitivity has been determined to be 10 nm for ZnO, 40 nm for Ag, 1.5 nm for BaTiO 3 , and one layer of MoS 2 with a sensitivity of 525 deg./RIU. Additionally, this study compared the effects on sensitivity of two dimensional materials graphene, WS 2 and MoS 2 . In contrast to the currently available biosensor utilizing surface plasmon resonance (SPR), the suggested structure exhibits higher angular sensitivity. Due to its improved sensitivity, the biosensor under consideration exhibits potential for detecting a wide range of biological analytes and organic compounds.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it