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3,071 to 3,080 of 8,021 Results
Oct 12, 2023 - SG Academies South-East Asia Fellowship (SASEAF) Programme
Gagus, 2023, "Related data for: SG Academies South-East Asia Fellowship (SASEAF) Programme", https://doi.org/10.21979/N9/CVT2WB, DR-NTU (Data), V1
Data for Singapore academies south-east Asia fellowship programme
ZIP Archive - 258.4 MB - MD5: b49f62106046b1ffde21dcc85c02d202
Oct 12, 2023LI Shuzhou
Oct 11, 2023 - ACS Applied Energy Materials
Ahmed, Mahmoud; Nguyen, Thi Hiep; Zhang, Mengyuan; Halevi, Oded; Abdi, Fatwa F.; Magdassi, Shlomo; Wong, Lydia Helena, 2023, "Replication Data for: Enhancing Photoelectrochemical Performance of the Printed Nanoporous FeVO4 Photoanode by Dual-Layer CoOx–CoPi Catalysts", https://doi.org/10.21979/N9/AJJSHR, DR-NTU (Data), V1
Raw data for the article titled "Enhancing Photoelectrochemical Performance of the Printed Nanoporous FeVO4 Photoanode by Dual-Layer CoOx–CoPi Catalysts"
application/vnd.ms-powerpoint.presentation.macroEnabled.12 - 3.5 MB - MD5: 842b8f6442437e96670788fdee894871
Raw data for article titled: Photoelectrochemical Performance of the Printed Nanoporous FeVO4 Photoanode by Dual-Layer CoOx−CoPi Catalysts.pptm
ACS Applied Energy Materials(Nanyang Technological University)
Oct 11, 2023Mahmoud Ahmed
The dataset contains the raw data for the paper published in ACS Applied Energy Materials titled as: Enhancing Photoelectrochemical Performance of the Printed Nanoporous FeVO4 Photoanode by Dual-Layer CoOx–CoPi Catalysts (21 Jul 2023)
Oct 10, 2023 - Project: Catalyst design principle in low temperature methane activation and conversion through machine learning
Liu, Jiyuan, 2023, "Related Data for: Catalyst design principle in low temperature methane activation and conversion through machine learning", https://doi.org/10.21979/N9/2MGYW2, DR-NTU (Data), V1
data for Catalyst design principle in low temperature methane activation and conversion through machine learning
Oct 10, 2023LI Shuzhou
data for project "Catalyst design principle in low temperature methane activation and conversion through machine learning"
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