Evidence map›Paper›PMID 34361804›Full record

ReviewMolecules (Basel, Switzerland)2021

Infrared Polaritonic Biosensors Based on Two-Dimensional Materials.

Guangyu Du, Xiaozhi Bao, Shenghuang Lin, Huan Pang, Shivananju Bannur Nanjunda, Qiaoliang Bao

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 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

6 authors.

Guangyu DuSchool of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China.
Xiaozhi BaoJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Macau, China.
Shenghuang LinSongshan Lake Materials Laboratory, Dongguan 523808, China.ORCID 0000-0001-9552-4680
Huan PangSchool of Chemistry and Chemical Engineering, Yangzhou University, Yangzhou 225009, China.
Shivananju Bannur NanjundaDepartment of Electrical Engineering, Centre of Excellence in Biochemical Sensing and Imaging Technologies (Cen-Bio-SIM), Indian Institute of Technology Madras, Chennai 600036, India.
Qiaoliang BaoShenzhen Exciton Science and Technology Ltd., Shenzhen 518052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, polaritons in two-dimensional (2D) materials have gained intensive research interests and significant progress due to their extraordinary properties of light-confinement, tunable carrier concentrations by gating and low loss absorption that leads to long polariton lifetimes. With additional advantages of biocompatibility, label-free, chemical identification of biomolecules through their vibrational fingerprints, graphene and related 2D materials can be adapted as excellent platforms for future polaritonic biosensor applications. Extreme spatial light confinement in 2D materials based polaritons supports atto-molar concentration or single molecule detection. In this article, we will review the state-of-the-art infrared polaritonic-based biosensors. We first discuss the concept of polaritons, then the biosensing properties of polaritons on various 2D materials, then lastly the impending applications and future opportunities of infrared polaritonic biosensors for medical and healthcare applications.

Indexed as

Biosensing TechniquesBiocompatible MaterialsElectrochemical TechniquesGraphiteHumansNanostructuresBiocompatible MaterialsGraphitebiosensorsmedical and healthcare applicationsmid-infraredpolaritontwo-dimensional materials

Identifiers

PMID34361804
PMCPMC8347072

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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.