Evidence map›Paper›PMID 26791784›Full record

ArticleScientific reports2016

Precisely Determining Ultralow level UO2(2+) in Natural Water with Plasmonic Nanowire Interstice Sensor.

Raekeun Gwak, Hongki Kim, Seung Min Yoo, Sang Yup Lee, Gyoung-Ja Lee, Min-Ku Lee, Chang-Kyu Rhee, Taejoon Kang, Bongsoo Kim

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

9 authors.

Raekeun GwakKAIST, Department of Chemistry, Daejeon 34141, Korea.
Hongki KimKAIST, Department of Chemistry, Daejeon 34141, Korea.
Seung Min YooKAIST, Department of Chemical and Biomolecular Engineering, Daejeon 34141, Korea.
Sang Yup LeeKAIST, Department of Chemical and Biomolecular Engineering, Daejeon 34141, Korea.
Gyoung-Ja LeeKAERI, Nuclear Materials Development Division, Daejeon 34057, Korea.
Min-Ku LeeKAERI, Nuclear Materials Development Division, Daejeon 34057, Korea.
Chang-Kyu RheeKAERI, Nuclear Materials Development Division, Daejeon 34057, Korea.
Taejoon KangKRIBB, BioNanotechnology Research Center and BioNano Health Guard Research Center, Daejeon 34141, Korea.
Bongsoo KimKAIST, Department of Chemistry, Daejeon 34141, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Uranium is an essential raw material in nuclear energy generation; however, its use raises concerns about the possibility of severe damage to human health and the natural environment. In this work, we report an ultrasensitive uranyl ion (UO2(2+)) detection method in natural water that uses a plasmonic nanowire interstice (PNI) sensor combined with a DNAzyme-cleaved reaction. UO2(2+) induces the cleavage of DNAzymes into enzyme strands and released strands, which include Raman-active molecules. A PNI sensor can capture the released strands, providing strong surface-enhanced Raman scattering signal. The combination of a PNI sensor and a DNAzyme-cleaved reaction significantly improves the UO2(2+) detection performance, resulting in a detection limit of 1 pM and high selectivity. More importantly, the PNI sensor operates perfectly, even in UO2(2+)-contaminated natural water samples. This suggests the potential usefulness of a PNI sensor in practical UO2(2+)-sensing applications. We anticipate that diverse toxic metal ions can be detected by applying various ion-specific DNA-based ligands to PNI sensors.

Indexed as

Biosensing TechniquesNanowiresCarbocyaninesDNA, CatalyticIonsSensitivity and SpecificityUraniumWaterCarbocyaninescyanine dye 5DNA, CatalyticIonsUraniumWater

Identifiers

PMID26791784
PMCPMC4726367

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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.