Evidence map›Paper›PMID 37561216›Full record

ArticleJournal of molecular modeling2023

Möbius carbon nanobelts interacting with heavy metal nanoclusters.

C Aguiar, N Dattani, I Camps

Abstract read
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Article in Journal of molecular modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

C AguiarLaboratório de Modelagem Computacional -LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas - UNIFAL-MG, Alfenas, Minas Gerais, Brazil.
N DattaniHPQC College, Waterloo, Canada. nike@hpqc.org.
I CampsLaboratório de Modelagem Computacional -LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas - UNIFAL-MG, Alfenas, Minas Gerais, Brazil. icamps@unifal-mg.edu.br.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

contextThe interaction between carbon nanostructures and heavy metal clusters is of great interest due to their potential applications as sensors and filters to remove the former from environment. In this work, we investigated the interaction between two types of carbon nanobelts (Möbius-type nanobelt and simple nanobelt) and nickel, cadmium, and lead nanoclusters. Our aim was to determine how both systems interact which would shed light on the potential applications of the carbon nanostructures as pollutant removal and detecting devices.

methodsTo investigate the interaction between carbon nanostructures and heavy metal nanoclusters, we utilized the semiempirical tight binding framework provided by xTB software with the GFN2-xTB Hamiltonian. We performed calculations to determine the best interaction site, lowest energy geometries, complexes stability (using molecular dynamics at 298K), binding energy, and electronic properties. We also carried out a topological study to investigate the nature and intensity of the bonds formed between the metal nanoclusters and the nanobelts. Our results demonstrate that heavy metal nanoclusters have a favorable binding affinity towards both nanobelts, with the Möbius-type nanobelt having a stronger interaction. Additionally, our calculations reveal that the nickel nanocluster has the lowest binding energy, displaying the greatest charge transfer with the nanobelts, which was nearly twice that of the cadmium and lead nanoclusters. Our combined results lead to the conclusion that the nickel nanoclusters are chemisorbed, whereas cadmium and lead nanoclusters are physisorbed in both nanobelts. These findings have significant implications for the development of sensor and filtering devices based on carbon and heavy metal nanoclusters.

Indexed as

CadmiumCarbon nanobeltHeavy metalsLeadMöbius beltNickel

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Registered trials

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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.