ReviewRSC advances2023
Surface modified nanoparticles and their applications for enantioselective detection, analysis, and separation of various chiral compounds.
Review in RSC advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of efficient enantioselective detection, analysis, and separation relies significantly on molecular interaction. In the scale of molecular interaction, nanomaterials have a significant influence on the performance of enantioselective recognitions. The use of nanomaterials for enantioselective recognition involved synthesizing new materials and immobilization techniques to produce various surface-modified nanoparticles that are either encapsulated or attached to surfaces, as well as layers and coatings. The combination of surface-modified nanomaterials and chiral selectors can improve enantioselective recognition. This review aims to offer engagement insights into the production and application of surface-modified nanomaterials to achieve sensitive and selective detection, better chiral analysis, and separation of numerous chiral compounds.
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.