ArticleBiosensors2023
Engineering a SERS Sensing Nanoplatform with Self-Sterilization for Undifferentiated and Rapid Detection of Bacteria.
Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Advances in methods for rapid detection of Staphylococcus aureus and methicillin-resistant S. aureus.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2025Review
- Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The development of a convenient, sensitive, rapid and self-sterilizing biosensor for microbial detection is important for the prevention and control of foodborne diseases. Herein, we designed a surface-enhanced Raman scattering (SERS) sensing nanoplatform based on a capture-enrichment-enhancement strategy to detect bacteria. The gold-Azo@silver-cetyltrimethylammonium bromide (Au-Azo@Ag-CTAB) SERS nanotags were obtained by optimizing the synthesis process conditions. The results showed that the modification of CTAB enabled the nanotags to bind to different bacteria electrostatically. This SERS sensing nanoplatform was demonstrated to be fast (15 min), accurate and sensitive (limit of detection (LOD): 300 and 400 CFU/mL for
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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.