ArticleMicrosystems & nanoengineering2025
pH-Inhibited Fenton etching of gold nanobipyramids: a multicolor approach for enhanced urea detection.
Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This study presents an innovative urea detection method utilizing pH-controlled Fenton etching of gold nanobipyramids (AuNBPs), offering a multicolor visual response. By leveraging the urease-catalyzed hydrolysis of urea, which releases ammonia and raises pH, the Fenton reaction is inhibited, reducing the etching of AuNBPs. This approach enables a highly sensitive and distinct multichromatic response across a wide range of urea concentrations, particularly at low target levels. The solution-based sensor achieved an exceptionally low detection limit of 0.098 μM, surpassing existing colorimetric urea biosensors. Furthermore, embedding the sensor in an agarose hydrogel matrix to create a solid-state format resulted in a detection limit of 0.2 μM. Real-world validation demonstrated high recovery rates in urine samples, further affirming the sensor's reliability. This multicolor biosensing platform offers a robust tool for point-of-care diagnostics, facilitating accurate and user-friendly urea detection.
Identifiers
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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.