Evidence map›Paper›PMID 36763639›Full record

ArticlePloS one2023

Towards a bionic IoT: Environmental monitoring using smartphone interrogated plant sensors.

Yunlong Guo, John Canning, Zenon Chaczko

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Yunlong GuoSchool of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW, Australia.
John CanningLaseire Pty Ltd, Sydney, NSW, Australia.ORCID 0000-0001-8281-7783
Zenon ChaczkoDept. of Computer Science, Faculty of Design, SWPS University of Social Sciences & Humanities, Warsaw, Poland.ORCID 0000-0002-2816-7510

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The utilisation of plants directly as quantifiable natural sensors is proposed. A case study measuring surface wettability of Aucuba japonica, or Japanese Laurel, plants using a novel smartphone field interrogator is demonstrated. This plant has been naturalised globally from Asia. Top-down contact angle measurements map wettability on-site and characterise a range of properties impacting plant health, such as aging, solar and UV exposure, and pollution. Leaves at an early age or in the shadow of trees are found to be hydrophobic with contact angle θ ~ 99°, while more mature leaves under sunlight are hydrophilic with θ ~ 79°. Direct UVA irradiation at λ = 365 nm is shown to accelerate aging, changing contact angle of one leaf from slightly hydrophobic at θ ~ 91° to be hydrophilic with θ ~ 87° after 30 min. Leaves growing beside a road with heavy traffic are observed to be substantially hydrophilic, as low as θ ~ 47°, arising from increased wettability with particulate accumulation on the leaf surface. Away from the road, the contact angle increases as high as θ ~ 96°. The results demonstrate that contact angle measurements using a portable diagnostic IoT edge device can be taken into the field for environmental detection, pollution assessment and more. Using an Internet connected smartphone combined with a plant sensor allows multiple measurements at multiple locations together in real-time, potentially enabling tracking of parameter change anywhere where plants are present or introduced. This hybrid integration of widely distributed living organic systems with the Internet marks the beginning of a new bionic Internet-of-things (b-IoT).

Indexed as

BionicsSmartphoneEnvironmental MonitoringPlantsWettability

Identifiers

PMID36763639
PMCPMC9917261

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.