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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".