Evidence map›Paper›PMID 37931219›Full record

ArticleACS nano2023

Ultraviolet Resonant Nanogap Antennas with Rhodium Nanocube Dimers for Enhancing Protein Intrinsic Autofluorescence.

Prithu Roy, Siyuan Zhu, Jean-Benoît Claude, Jie Liu, Jérôme Wenger

Abstract read
In one paragraph

Article in ACS nano, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Prithu RoyAix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, AMUTech, 13013 Marseille, France.ORCID 0000-0003-4063-9434
Siyuan ZhuDepartment of Chemistry, Duke University, Durham, North Carolina 27708, United States.
Jean-Benoît ClaudeAix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, AMUTech, 13013 Marseille, France.
Jie LiuDepartment of Chemistry, Duke University, Durham, North Carolina 27708, United States.ORCID 0000-0003-0451-6111
Jérôme WengerAix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, AMUTech, 13013 Marseille, France.ORCID 0000-0002-2145-5341

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plasmonic optical nanoantennas offer compelling solutions for enhancing light-matter interactions at the nanoscale. However, until now, their focus has been mainly limited to the visible and near-infrared regions, overlooking the immense potential of the ultraviolet (UV) range, where molecules exhibit their strongest absorption. Here, we present the realization of UV resonant nanogap antennas constructed from paired rhodium nanocubes. Rhodium emerges as a robust alternative to aluminum, offering enhanced stability in wet environments and ensuring reliable performance in the UV range. Our results showcase the nanoantenna's ability to enhance the UV autofluorescence of label-free streptavidin and hemoglobin proteins. We achieve significant enhancements of the autofluorescence brightness per protein by up to 120-fold and reach zeptoliter detection volumes, enabling UV autofluorescence correlation spectroscopy (UV-FCS) at high concentrations of several tens of micromolar. We investigate the modulation of fluorescence photokinetic rates and report excellent agreement between the experimental results and numerical simulations. This work expands the applicability of plasmonic nanoantennas to the deep UV range, unlocking the investigation of label-free proteins at physiological concentrations.

Indexed as

RhodiumPolymersProteinsSpectrometry, FluorescencePolymersProteinsRhodiumnanophotonicsoptical antennasplasmonicssingle-molecule fluorescencetryptophan autofluorescenceultraviolet UV

Identifiers

PMID37931219
PMCPMC10690780

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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.