ArticleHeliyon2024
Dual-core silver-coated plasmonic sensor modeling with machine learning.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Simultaneous dual-analyte detection biosensor through a stacking-based ensemble machine learning approach: design and optimization.Biomedical optics express · 2026Article
- Article
- Design optimization of high-sensitivity PCF-SPR biosensor using machine learning and explainable AI.PloS one · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plasmonic sensors utilizing surface plasmon resonance (SPR) have emerged as powerful tools for sensitive and label-free detection across a wide range of applications. This study introduces a new dual-core silver-coated plasmonic sensor designed to significantly enhance sensitivity and resolution, making it particularly effective for precise analyte detection in complex environments. A key innovation of this sensor lies in its dual-core architecture, which achieves the highest wavelength sensitivity reported at 30,000 nm/RIU and resolution of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.