Super-Resolution Imaging with Graphene.

Xiaoxiao Jiang, Lu Kong, Yu Ying, Qiongchan Gu, Jiangtao Lv, Zhigao Dai, Guangyuan Si

Abstract readReview

In one paragraph

Review in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Xiaoxiao JiangCollege of Information Science and Engineering, Northeastern University, Shenyang 110004, China.

Lu KongCollege of Information Science and Engineering, Northeastern University, Shenyang 110004, China.ORCID 0000-0001-9157-9465

Yu YingCollege of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China.

Qiongchan GuCollege of Information Science and Engineering, Northeastern University, Shenyang 110004, China.

Jiangtao LvCollege of Information Science and Engineering, Northeastern University, Shenyang 110004, China.

Zhigao DaiEngineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China.

Guangyuan SiMelbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, Clayton, VIC 3168, Australia.

Funding

Fundamental Research Funds for the Central Universities Key Scientific Research Guidance Project N2023005Hebei Province Science and Technology Plan Key Research Development Project Funds 18273902DNational Natural Science Foundation of China 11704263Natural Science Foundation of Hebei Province F2018501063

6 · The paper itself

Abstract

Super-resolution optical imaging is a consistent research hotspot for promoting studies in nanotechnology and biotechnology due to its capability of overcoming the diffraction limit, which is an intrinsic obstacle in pursuing higher resolution for conventional microscopy techniques. In the past few decades, a great number of techniques in this research domain have been theoretically proposed and experimentally demonstrated. Graphene, a special two-dimensional material, has become the most meritorious candidate and attracted incredible attention in high-resolution imaging domain due to its distinctive properties. In this article, the working principle of graphene-assisted imaging devices is summarized, and recent advances of super-resolution optical imaging based on graphene are reviewed for both near-field and far-field applications.

Indexed as

GraphiteOptical ImagingMicroscopyNanotechnologyGraphiteevanescent-field enhancementgraphene plasmonicssuper-resolution imaging

Identifiers

PMID34562897

DOI10.3390/bios11090307

PMCPMC8471375

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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.