Evidence map›Paper›PMID 34576990›Full record

ArticleMolecules (Basel, Switzerland)2021

Detection of Heavy Metals in Water Using Graphene Oxide Quantum Dots: An Experimental and Theoretical Study.

Lorenzo Gontrani, Olivia Pulci, Marilena Carbone, Roberto Pizzoferrato, Paolo Prosposito

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2021. 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. Review
  2. Article
  3. Article
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  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

5 authors.

Lorenzo GontraniDipartimento di Chimica, Università di Roma "La Sapienza", P. le A. Moro 5, 00185 Roma, Italy.ORCID 0000-0001-8212-7029
Olivia PulciDipartimento di Fisica, Università di Roma "Tor Vergata", Via Della Ricerca Scientifica, 00133 Roma, Italy.ORCID 0000-0002-9725-487X
Marilena CarboneDipartimento di Scienze e Tecnologie Chimiche, Università di Roma "Tor Vergata", Via Della Ricerca Scientifica, 00133 Roma, Italy.ORCID 0000-0002-4224-1393
Roberto PizzoferratoDipartimento di Ingegneria Industriale, Università di Roma "Tor Vergata", Via del Politecnico 1, 00133 Roma, Italy.ORCID 0000-0002-6319-2519
Paolo ProspositoDipartimento di Ingegneria Industriale, Università di Roma "Tor Vergata", Via del Politecnico 1, 00133 Roma, Italy.ORCID 0000-0001-9183-0162

Funding

European Commission DiSeTCom (GA 823728)Regione Lazio 85-2017-15125Università degli Studi della Tuscia DIBAF-Department of University of Tuscia, Project "Landscape 4.0 - food, 318 wellbeing and environment
6 · The paper itself

Abstract

In this work, we investigate by ab initio calculations and optical experiments the sensitivity of graphene quantum dots in their use as devices to measure the presence, and concentration, of heavy metals in water. We demonstrate that the quenching or enhancement in the optical response (absorption, emission) depends on the metallic ion considered. In particular, two cases of opposite behaviour are considered in detail: Cd2+, where we observe an increase in the emission optical response for increasing concentration, and Pb2+ whose emission spectra, vice versa, are quenched along the concentration rise. The experimental trends reported comply nicely with the different hydration patterns suggested by the models that are also capable of reproducing the minor quenching/enhancing effects observed in other ions. We envisage that quantum dots of graphene may be routinely used as cheap detectors to measure the degree of poisoning ions in water.

Indexed as

absorptionconcentrationenhancementfluorescencegraphene oxideheavy metalsoptical responsequantum dotsquenchingTD-DFT

Identifiers

PMID34576990
PMCPMC8467530

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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.