Evidence map›Paper›PMID 35957095›Full record

ReviewNanomaterials (Basel, Switzerland)2022

Natural and Engineered Nanomaterials for the Identification of Heavy Metal Ions-A Review.

Joseph Merillyn Vonnie, Bong Jing Ting, Kobun Rovina, Nasir Md Nur' Aqilah, Koh Wee Yin, Nurul Huda

Abstract readReview
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2022. 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
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
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

6 authors.

Joseph Merillyn VonnieFaculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia.ORCID 0000-0002-7351-3507
Bong Jing TingFaculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia.
Kobun RovinaFaculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia.ORCID 0000-0002-4985-9145
Nasir Md Nur' AqilahFaculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia.
Koh Wee YinFaculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia.ORCID 0000-0002-2734-5178
Nurul HudaFaculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia.ORCID 0000-0001-9867-6401

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, there has been much interest in developing advanced and innovative approaches for sensing applications in various fields, including agriculture and environmental remediation. The development of novel sensors for detecting heavy metals using nanomaterials has emerged as a rapidly developing research area due to its high availability and sustainability. This review emphasized the naturally derived and engineered nanomaterials that have the potential to be applied as sensing reagents to interact with metal ions or as reducing and stabilizing agents to synthesize metallic nanoparticles for the detection of heavy metal ions. This review also focused on the recent advancement of nanotechnology-based detection methods using naturally derived and engineered materials, with a summary of their sensitivity and selectivity towards heavy metals. This review paper covers the pros and cons of sensing applications with recent research published from 2015 to 2022.

Indexed as

environmental-friendlygreen synthesisheavy metal ionsnanotechnologysensorsustainable

Identifiers

PMID35957095
PMCPMC9370674

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.