Evidence map›Paper›PMID 36643902›Full record

ArticleNano energy2023

COVID-19 clinical waste reuse: A triboelectric touch sensor for IoT-cloud supported smart hand sanitizer dispenser.

Sayyid Abdul Basith, Arunkumar Chandrasekhar

Open access · greenAbstract read
In one paragraph

Article in Nano energy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
7.7field-weighted citation impact, top 2% 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

6 citing papers in PubMed, 70 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Sayyid Abdul BasithNanosensors and Nanoenergy Lab, Sensor Systems Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Arunkumar ChandrasekharNanosensors and Nanoenergy Lab, Sensor Systems Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Vellore Institute of Technology University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Earth's plastic pollution has increased due to the COVID-19 pandemic, and the world is on the doorstep of an enormous waste pandemic. The extensive use of mandatory personal protectives like masks, gloves, and PPE kits and the lack of proper waste management systems lead to a rise in the plastic pollution content of the earth. Such disposable and non-biodegradable personal protectives are thrown out to the environment after use. These distributed wastes pollute land, soil, and water bodies and effects their ecosystems. This research work establishes the concept of a waste-to-energy conversion approach to reuse COVID-19 scraps for green and sustainable development. Three-layered surgical masks and nitrile gloves were reused in this work after sterilization for energy harvesting and sensing applications by fabricating a 3D-printed contact-separation-based triboelectric nanogenerator. A piece of three-layered mask and nitrile gloves were placed inside the 3D structure as the top negative and bottom positive triboelectric materials with copper and aluminum as corresponding electrodes (MG-CS TENG). It can convert external mechanical motions into electrical energy. The maximum voltage, current, and power density obtained from the device are 50.7 V, 4.8 µA, and 6.39 µW/cm

Indexed as

3D printingCOVID-19Internet of thingsNanogeneratorsNanosensorsWaste management

Identifiers

PMID36643902
PMCPMC9822840
OpenAlexW4313656015

What OpenQuestion holds

Textmetadata
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.