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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91 to 100 of 1,794 Results
pr.log
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Plain Text - 263.2 KB - MD5: 0d7d180912ae945f7e81f9e7980b8fdf
pr.mdp
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Unknown - 558 B - MD5: 09dcdf6409053dd7adb9b9f4bbca3fff
pr.mdp
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Unknown - 559 B - MD5: df9f5b0ab1d67dcd64c005d566e54820
pr_prev.cpt
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
application/mac-compactpro - 142.5 KB - MD5: 616fa5d99fedd5a30b0d5a5bb6d60dca
pr.tpr
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Unknown - 169.3 KB - MD5: e4911a5369019853c6df422825d79200
pr.tpr
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Unknown - 169.4 KB - MD5: 5630610fe353b2c5ab640be5d179a519
pr.xtc
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Unknown - 493.5 KB - MD5: 76f08113ff481e578f73e75dcfc27309
pr.xtc
Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning
Unknown - 479.6 KB - MD5: 0f21fb41ebcf8f5662750735247a1368
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
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