guest ::
| Home > Workflow collections > Publication Charges > Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells > print |
| Home > Workflow collections > Publication Charges > Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells > print |
| 001 | 905288 | ||
| 005 | 20240712113145.0 | ||
| 024 | 7 | _ | |a 10.1039/D1NR06435E |2 doi |
| 024 | 7 | _ | |a 2040-3364 |2 ISSN |
| 024 | 7 | _ | |a 2040-3372 |2 ISSN |
| 024 | 7 | _ | |a 2128/30300 |2 Handle |
| 024 | 7 | _ | |a altmetric:117526502 |2 altmetric |
| 024 | 7 | _ | |a pmid:34846412 |2 pmid |
| 024 | 7 | _ | |a WOS:000723812900001 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-00559 |
| 082 | _ | _ | |a 600 |
| 100 | 1 | _ | |a Colliard-Granero, André |0 P:(DE-Juel1)188204 |b 0 |
| 245 | _ | _ | |a Deep learning for the automation of particle analysis in catalyst layers for polymer electrolyte fuel cells |
| 260 | _ | _ | |a Cambridge |c 2022 |b RSC Publ. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1676461577_10240 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often ad hoc, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. A dataset of 40 high-resolution TEM images at different magnification levels, from 10 to 100 nm scales, was annotated manually. This dataset was used to train the U-Net model, with the StarDist formulation for the loss function, for the nanoparticle segmentation task. StarDist reached a precision of 86%, recall of 85%, and an F1-score of 85% by training on datasets as small as thirty images. The segmentation maps outperform models reported in the literature for a similar problem, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost. |
| 536 | _ | _ | |a 1231 - Electrochemistry for Hydrogen (POF4-123) |0 G:(DE-HGF)POF4-1231 |c POF4-123 |f POF IV |x 0 |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Batool, Mariah |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Jankovic, Jasna |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Jitsev, Jenia |0 P:(DE-Juel1)158080 |b 3 |u fzj |
| 700 | 1 | _ | |a Eikerling, Michael H. |0 P:(DE-Juel1)178034 |b 4 |
| 700 | 1 | _ | |a Malek, Kourosh |0 P:(DE-Juel1)181057 |b 5 |e Corresponding author |
| 700 | 1 | _ | |a Eslamibidgoli, Mohammad Javad |0 P:(DE-Juel1)181059 |b 6 |e Corresponding author |
| 773 | _ | _ | |a 10.1039/D1NR06435E |g Vol. 14, no. 1, p. 10 - 18 |0 PERI:(DE-600)2515664-0 |n 1 |p 10 - 18 |t Nanoscale |v 14 |y 2022 |x 2040-3364 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/905288/files/Invoice_INV_017301.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/905288/files/Nanoscale_2021.pdf |y Published on 2021-11-24. Available in OpenAccess from 2022-11-24. |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/905288/files/d1nr06435e.pdf |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:905288 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)188204 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)158080 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)178034 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)181057 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)181059 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Materialien und Technologien für die Energiewende (MTET) |1 G:(DE-HGF)POF4-120 |0 G:(DE-HGF)POF4-123 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Chemische Energieträger |9 G:(DE-HGF)POF4-1231 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 1 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a Embargoed OpenAccess |0 StatID:(DE-HGF)0530 |2 StatID |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-01-27 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-01-27 |
| 915 | _ | _ | |a National-Konsortium |0 StatID:(DE-HGF)0430 |2 StatID |d 2022-11-12 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2022-11-12 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2022-11-12 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2022-11-12 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2022-11-12 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b NANOSCALE : 2021 |d 2022-11-12 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2022-11-12 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b NANOSCALE : 2021 |d 2022-11-12 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IEK-13-20190226 |k IEK-13 |l IEK-13 |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 1 |
| 980 | 1 | _ | |a APC |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IEK-13-20190226 |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a APC |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IET-3-20190226 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|