Evidence map›Paper›PMID 39410278›Full record

ArticleMaterials (Basel, Switzerland)2024

Classification of Electronic Waste Components through X-ray and Neutron-Based Imaging Techniques.

Noémi Anna Buczkó, Mariann Papp, Boglárka Maróti, Zoltán Kis, László Szentmiklósi

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. 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. 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.

Noémi Anna BuczkóNuclear Analysis and Radiography Department, HUN-REN Centre for Energy Research, 1121 Budapest, Hungary.ORCID 0000-0002-2175-6400
Mariann PappNuclear Analysis and Radiography Department, HUN-REN Centre for Energy Research, 1121 Budapest, Hungary.
Boglárka MarótiNuclear Analysis and Radiography Department, HUN-REN Centre for Energy Research, 1121 Budapest, Hungary.ORCID 0000-0001-9598-2913
Zoltán KisNuclear Analysis and Radiography Department, HUN-REN Centre for Energy Research, 1121 Budapest, Hungary.ORCID 0000-0002-8365-8507
László SzentmiklósiNuclear Analysis and Radiography Department, HUN-REN Centre for Energy Research, 1121 Budapest, Hungary.ORCID 0000-0001-7747-8545

Funding

Hungarian Academy of Sciences Young Scientist Fellowship
6 · The paper itself

Abstract

In modern society, the amount of e-waste is growing year by year. Waste electronic items are complex, highly heterogeneous systems, containing organic material as well as several exotic, valuable, toxic, mostly metallic elements. In this study, the potential of X-ray and neutron radiography to reveal the inner structure of various complex e-waste was investigated. The images obtained using the two techniques were evaluated together to investigate the possibility of a more efficient segmentation of the individual components. The advantages and limitations of the two methods were identified for the studied waste types. X-ray radiography was found to be preferable for the identification of small metallic parts and for revealing the internal structure of e-waste with thick plastic coatings. Neutron radiography allowed for the identification of several components that did not provide sufficient contrast with X-ray imaging due to their similar X-ray attenuation compared to their surroundings. The combination of the two methods opens up new opportunities and could provide much more effective segmentation than either method alone.

Indexed as

image segmentationneutron imagingnon-destructive characterizationX-ray imaging

Identifiers

PMID39410278
PMCPMC11478296

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.