Evidence map›Paper›PMID 40268286›Full record

ArticleACS applied materials & interfaces2025

Metal Oxide-Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms.

Alishba T John, Jing Qian, Qi Wang, Fabian S Garay-Rairan, Y M Nuwan D Y Bandara, Artem Lensky, Krishnan Murugappan, Hanna Suominen, Antonio Tricoli

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS applied materials & interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Alishba T JohnNanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.
Jing QianSchool of Computing, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT 2601, Australia.
Qi WangNanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia.
Fabian S Garay-RairanNanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Camperdown, NSW 2006, Australia.ORCID 0000-0001-8863-5673
Y M Nuwan D Y BandaraNanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.
Artem LenskySchool of Engineering and Technology, The University of New South Wales, Canberra, ACT 2612, Australia.
Krishnan MurugappanNanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.ORCID 0000-0002-6845-4653
Hanna SuominenSchool of Computing, College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT 2601, Australia.
Antonio TricoliNanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia.ORCID 0000-0003-4964-2111

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing demand for gas molecule detection emphasizes the need for portable sensor devices possessing selectivity, a low limit of detection (LOD), and a large dynamic range. Despite substantial progress in developing nanostructured sensor materials with heightened sensitivity, achieving sufficient selectivity remains a challenge. Here, we introduce a strategy to enhance the performance of chemiresistive gas sensors by combining an advanced sensor design with machine learning (ML). Our sensor architecture consists of a tungsten oxide (WO

Indexed as

chemiresistivecomputer-assisted workflowsconcentration predictionevaluation studiesgas discriminationmachine learningnanoparticle networksZIF-8

Identifiers

PMID40268286
PMCPMC12067377

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.