Evidence map›Paper›PMID 34599208›Full record

ArticleScientific reports2021

Fabrication and microfluidic analysis of graphene-based molecular communication receiver for Internet of Nano Things (IoNT).

Murat Kuscu, Hamideh Ramezani, Ergin Dinc, Shahab Akhavan, Ozgur B Akan

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In one paragraph

Article in Scientific reports, 2021. 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

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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. Review
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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.

Murat KuscuInternet of Everything (IoE) Group, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK. mk959@cantab.ac.uk.
Hamideh RamezaniInternet of Everything (IoE) Group, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.
Ergin DincInternet of Everything (IoE) Group, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.
Shahab AkhavanCambridge Graphene Centre (CGC), Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.
Ozgur B AkanInternet of Everything (IoE) Group, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bio-inspired molecular communications (MC), where molecules are used to transfer information, is the most promising technique to realise the Internet of Nano Things (IoNT), thanks to its inherent biocompatibility, energy-efficiency, and reliability in physiologically-relevant environments. Despite a substantial body of theoretical work concerning MC, the lack of practical micro/nanoscale MC devices and MC testbeds has led researchers to make overly simplifying assumptions about the implications of the channel conditions and the physical architectures of the practical transceivers in developing theoretical models and devising communication methods for MC. On the other hand, MC imposes unique challenges resulting from the highly complex, nonlinear, time-varying channel properties that cannot be always tackled by conventional information and communication tools and technologies (ICT). As a result, the reliability of the existing MC methods, which are mostly adopted from electromagnetic communications and not validated with practical testbeds, is highly questionable. As the first step to remove this discrepancy, in this study, we report on the fabrication of a nanoscale MC receiver based on graphene field-effect transistor biosensors. We perform its ICT characterisation in a custom-designed microfluidic MC system with the information encoded into the concentration of single-stranded DNA molecules. This experimental platform is the first practical implementation of a micro/nanoscale MC system with nanoscale MC receivers, and can serve as a testbed for developing realistic MC methods and IoNT applications.

Identifiers

PMID34599208
PMCPMC8486847

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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.