Evidence map›Paper›PMID 31285541›Full record

ArticleNature chemistry2019

Super-resolution imaging of non-fluorescent reactions via competition.

Xianwen Mao, Chunming Liu, Mahdi Hesari, Ningmu Zou, Peng Chen

Abstract read
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In one paragraph

Article in Nature chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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  12. Revealing operando surface defect-dependent electrocatalytic performance of Pt at the subparticle level.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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.

Xianwen MaoDepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA.
Chunming LiuDepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA.
Mahdi HesariDepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA.
Ningmu ZouDepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA.
Peng ChenDepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA. pc252@cornell.edu.ORCID http://orcid.org/0000-0001-8582-7661

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Super-resolved fluorescence microscopy techniques have enabled substantial advances in the chemical and biological sciences. However, they can only interrogate entities that fluoresce, and most chemical or biological processes do not involve fluorescent species. Here we report a competition-enabled imaging technique with super-resolution (COMPEITS) that enables quantitative super-resolution imaging of non-fluorescent processes. It is based on the incorporation of competition into a single-molecule fluorescence-detection scheme. We demonstrate COMPEITS by investigating a photoelectrocatalytic reaction; we map, with nanometre precision, a non-fluorescent surface reaction that is important for water decontamination on single photocatalyst particles. The subparticle-level quantitative information of reactant adsorption affinities unambiguously decouples size- and shape-scaling laws on specific particle facets and uncovers a surprising biphasic shape dependence, leading to catalyst design principles for optimal reactant adsorption efficacy. With its ability to provide spatially resolved information on the behaviours of unlabelled, non-fluorescent entities under operando conditions, COMPEITS could interrogate a variety of surface processes in fields ranging from heterogeneous catalysis and materials engineering to nanotechnology and energy sciences.

Indexed as

AdsorptionFluorescent DyesMicroscopy, FluorescenceNanotechnologyOptical ImagingParticle SizeSurface PropertiesFluorescent Dyes

Identifiers

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Registered trials

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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.