Evidence map›Paper›PMID 35591238›Full record

ArticleSensors (Basel, Switzerland)2022

Impedance Characteristics of Monolayer and Bilayer Graphene Films with Biofilm Formation and Growth.

Ryoichi Nakagawa, Kai Saito, Hideyuki Kanematsu, Hidekazu Miura, Masatou Ishihara, Dana M Barry, Takeshi Kogo, Akiko Ogawa, Nobumitsu Hirai, Takeshi Hagio and 4 more

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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
0.2field-weighted citation impact, top 54% of its field
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, 4 citations in OpenAlex.

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

14 authors at 8 institutions in 3 countries.

Ryoichi NakagawaNational Institute of Technology (KOSEN), Suzuka College, Mie 510-0294, Japan.
Kai SaitoNational Institute of Technology (KOSEN), Suzuka College, Mie 510-0294, Japan.
Hideyuki KanematsuNational Institute of Technology (KOSEN), Suzuka College, Mie 510-0294, Japan.ORCID 0000-0002-9144-6539
Hidekazu MiuraFaculty of Medical Engineering, Suzuka University of Medical Science, Mie 510-0293, Japan.
Masatou IshiharaNanomaterials Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki 305-8565, Japan.ORCID 0000-0002-5016-3356
Dana M BarryDepartment of Electrical & Computer Engineering, Clarkson University, Potsdam, NY 13699, USA.ORCID 0000-0001-5662-7548
Takeshi KogoNational Institute of Technology (KOSEN), Suzuka College, Mie 510-0294, Japan.ORCID 0000-0002-8453-8830
Akiko OgawaNational Institute of Technology (KOSEN), Suzuka College, Mie 510-0294, Japan.ORCID 0000-0001-8684-288X
Nobumitsu HiraiNational Institute of Technology (KOSEN), Suzuka College, Mie 510-0294, Japan.ORCID 0000-0002-3400-7417
Takeshi HagioInstitutes of Innovation for Future Society, Nagoya University, Nagoya 464-8601, Japan.ORCID 0000-0002-7434-0150
Ryoichi IchinoInstitutes of Innovation for Future Society, Nagoya University, Nagoya 464-8601, Japan.
Masahito BanDepartment of Applied Chemistry, Nippon Institute of Technology, Saitama 345-8501, Japan.
Michiko YoshitakeNational Institute for Materials Science (NIMS), Tsukuba 305-0044, Japan.
Stefan ZimmermannInstitute of Electrical Engineering and Measurement Technology, Leibniz Universität Hannover, 30167 Hannover, Germany.ORCID 0000-0002-1725-6657
National Institute of Technology, Kagoshima College · JPNagoya University · JPLeibniz University Hannover · DENational Institute for Materials Science · JPNational Institute of Advanced Industrial Science and Technology · JPNippon Institute of Technology · JPSUNY Canton · USSuzuka University of Medical Science · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biofilms are the result of bacterial activity. When the number of bacteria (attached to materials' surfaces) reaches a certain threshold value, then the bacteria simultaneously excrete organic polymers (EPS: extracellular polymeric substances). These sticky polymers encase and protect the bacteria. They are called biofilms and contain about 80% water. Other components of biofilm include polymeric carbon compounds such as polysaccharides and bacteria. It is well-known that biofilms cause various medical and hygiene problems. Therefore, it is important to have a sensor that can detect biofilms to solve such problems. Graphene is a single-atom-thick sheet in which carbon atoms are connected in a hexagonal shape like a honeycomb. Carbon compounds generally bond easily to graphene. Therefore, it is highly possible that graphene could serve as a sensor to monitor biofilm formation and growth. In our previous study, monolayer graphene was prepared on a glass substrate by the chemical vapor deposition (CVD) method. Its biofilm forming ability was compared with that of graphite. As a result, the CVD graphene film had the higher sensitivity for biofilm formation. However, the monolayer graphene has a mechanical disadvantage when used as a biofilm sensor. Therefore, for this new research project, we prepared bilayer graphene with high mechanical strength by using the CVD process on copper substrates. For these specimens, we measured the capacitance component of the specimens' impedance. In addition, we have included a discussion about the possibility of applying them as future sensors for monitoring biofilm formation and growth.

Indexed as

Cardiovascular DiseasesGraphiteBacteriaBiofilmsCarbonElectric ImpedanceHumansPolymersCarbonGraphitePolymersbiofilmEPSimpedanceRaman spectroscopysensors

Identifiers

PMID35591238
PMCPMC9101136
OpenAlexW4229366468

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.