Appointment: Associate Professor, School of Material Science & Engineering

Assistant Chair, Graduate Studies and Lifelong Learning

Research topics:

• developing high sensitive substrates for surface-enhanced Raman scattering and fluorescence

• optical properties of metal-semiconductor nanostructures

• assembly of metallic nanostructures

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1 to 10 of 19 Results
Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Oct 31, 2023 - Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Chen Xi, 2023, "Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning", https://doi.org/10.21979/N9/QR3JP5, DR-NTU (Data), V1
Data generated during project: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning(Nanyang Technological University)
Oct 31, 2023
Replication Data for: Bridging the gap between electrical double-layer and faraday intercalation with nanoconfinement
Replication Data for: Bridging the gap between electrical double-layer and faraday intercalation with nanoconfinement
Oct 30, 2023 - Jiaqi's utilizing nanoconfined capacitive oligomerization dataverse
Wei, Jiaqi, 2023, "Replication Data for: Bridging the gap between electrical double-layer and faraday intercalation with nanoconfinement", https://doi.org/10.21979/N9/L4DZ77, DR-NTU (Data), V1, UNF:6:RlyAZPvOH4uUOHotfDcckw== [fileUNF]
Raw data for: bridging the gap between electrical double-layer and faraday intercalation with nanoconfinement
Replication Data for: Quantifying ion solvation n structure evolution in carbon micropores
Oct 30, 2023 - Jiaqi's utilizing nanoconfined capacitive oligomerization dataverse
Wei, Jiaqi, 2023, "Replication Data for: Quantifying ion solvation n structure evolution in carbon micropores", https://doi.org/10.21979/N9/SBPTFT, DR-NTU (Data), V1
Raw data for: quantifying ion solvation n structure evolution in carbon micropores
Replication Data for: Unravelling nanoconfined electrochemical ion adsorption and intercalation using in situ XAS n XRD-PD data
Oct 30, 2023 - Jiaqi's utilizing nanoconfined capacitive oligomerization dataverse
Wei, Jiaqi, 2023, "Replication Data for: Unravelling nanoconfined electrochemical ion adsorption and intercalation using in situ XAS n XRD-PD data", https://doi.org/10.21979/N9/1JBXD0, DR-NTU (Data), V1
Raw data for: unravelling nanoconfined electrochemical ion adsorption and intercalation using in situ XAS n XRD-PD
Replication Data for: Unravelling nanoconfined electrochemical ion adsorption and intercalation using in situ XAS n XRD-XAS data
Oct 30, 2023 - Jiaqi's utilizing nanoconfined capacitive oligomerization dataverse
Wei, Jiaqi, 2023, "Replication Data for: Unravelling nanoconfined electrochemical ion adsorption and intercalation using in situ XAS n XRD-XAS data", https://doi.org/10.21979/N9/VX1BN3, DR-NTU (Data), V1
Raw data for: unravelling nanoconfined electrochemical ion adsorption and intercalation using in situ XAS n XRD-XAS
Jiaqi's utilizing nanoconfined capacitive oligomerization dataverse(Nanyang Technological University)
Oct 30, 2023
Related data for: SG Academies South-East Asia Fellowship (SASEAF) Programme
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
SG Academies South-East Asia Fellowship (SASEAF) Programme(Nanyang Technological University)
Oct 12, 2023
Related Data for: Catalyst design principle in low temperature methane activation and conversion through machine learning
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
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