Evidence map›Paper›PMID 37020627›Full record

ArticleGlobal challenges (Hoboken, NJ)2023

Bio-Graphene Sensors for Monitoring Moisture Levels in Wood and Ambient Environment.

Mohammad Yusuf Mulla, Patrik Isacsson, Illia Dobryden, Valerio Beni, Emma Östmark, Karl Håkansson, Jesper Edberg

Abstract read
In one paragraph

Article in Global challenges (Hoboken, NJ), 2023. 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

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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
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.

Mohammad Yusuf MullaPrinted-, Bio- and Organic Electronics RISE Research Institutes of Sweden Bredgatan 35 Norrköping SE-602 21 Sweden.
Patrik IsacssonDigital Cellulose Center Bredgatan 35 Norrköping SE-602 21 Sweden.
Illia DobrydenBioeconomy and Health RISE Research Institutes of Sweden Drottning Kristinas väg 61 Stockholm SE-114 28 Sweden.
Valerio BeniPrinted-, Bio- and Organic Electronics RISE Research Institutes of Sweden Bredgatan 35 Norrköping SE-602 21 Sweden.
Emma ÖstmarkDigital Cellulose Center Bredgatan 35 Norrköping SE-602 21 Sweden.
Karl HåkanssonDigital Cellulose Center Bredgatan 35 Norrköping SE-602 21 Sweden.
Jesper EdbergPrinted-, Bio- and Organic Electronics RISE Research Institutes of Sweden Bredgatan 35 Norrköping SE-602 21 Sweden.ORCID https://orcid.org/0000-0002-2904-7238

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wood is an inherently hygroscopic material which tends to absorb moisture from its surrounding. Moisture in wood is a determining factor for the quality of wood being employed in construction, since it causes weakening, deformation, rotting, and ultimately leading to failure of the structures resulting in costs to the economy, the environment, and to the safety of residents. Therefore, monitoring moisture in wood during the construction phase and after construction is vital for the future of smart and sustainable buildings. Employing bio-based materials for the construction of electronics is one way to mitigate the environmental impact of such electronics. Herein, a bio-graphene sensor for monitoring the moisture inside and around wooden surfaces is fabricated using laser-induced graphitization of a lignin-based ink precursor. The bio-graphene sensors are used to measure humidity in the range of 10% up to 90% at 25 °C. Using laser induced graphitization, conductor resistivity of 18.6 Ω sq

Indexed as

cellulosehumidity sensorslaser‐induced grapheneligninmoisture sensorswood

Identifiers

PMID37020627
PMCPMC10069311

What OpenQuestion holds

Textmetadata
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.