Evidence map›Paper›PMID 38305121›Full record

ReviewACS sensors2024

Colloidal Quantum Dots for Explosive Detection: Trends and Perspectives.

Andrea De Iacovo, Federica Mitri, Serena De Santis, Carlo Giansante, Lorenzo Colace

Abstract readReview
In one paragraph

Review in ACS sensors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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.

Andrea De IacovoDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, Rome I-00146, Italy.ORCID 0000-0001-5006-5505
Federica MitriDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, Rome I-00146, Italy.
Serena De SantisDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, Rome I-00146, Italy.
Carlo GiansanteConsiglio Nazionale delle Ricerche, Istituto di Nanotecnologia CNR-NANOTEC, Via Monteroni, Lecce I-73100, Italy.ORCID 0000-0003-4558-5367
Lorenzo ColaceDepartment of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via Vito Volterra 62, Rome I-00146, Italy.ORCID 0000-0002-7111-3905

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensitive, accurate, and reliable detection of explosives has become one of the major needs for international security and environmental protection. Colloidal quantum dots, because of their unique chemical, optical, and electrical properties, as well as easy synthesis route and functionalization, have demonstrated high potential to meet the requirements for the development of suitable sensors, boosting the research in the field of explosive detection. Here, we critically review the most relevant research works, highlighting three different mechanisms for explosive detection based on colloidal quantum dots, namely photoluminescence, electrochemical, and chemoresistive sensing. We provide a comprehensive overview and an extensive discussion and comparison in terms of the most relevant sensor parameters. We highlight advantages, limitations, and challenges of quantum dot-based explosive sensors and outline future research directions for the advancement of knowledge in this surging research field.

Indexed as

Explosive AgentsQuantum DotsExplosive AgentsChemoresistive SensorsColloidal Quantum DotsElectrochemical SensorsExplosive DetectionLuminescent SensorsNanomaterialsNanotechnologiesNitroaromatic Compounds

Identifiers

PMID38305121
PMCPMC11425854

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.