LI Shuzhou

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 1,794 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
em2.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 1.2 MB - MD5: 95ed69d696ce8934a2aa1fc47c02aae9
em2.gro Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 27.0 KB - MD5: 61c83b6c12a9c573a907b98bba3316b2
em2.log Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Plain Text - 2.8 MB - MD5: c0f21e91e88439fdf6e80b8453868559
em2.tpr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 168.7 KB - MD5: ffe53624072ecc494143c789b4d06574
em2.trr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 7.1 KB - MD5: 09cc6782523584857d3794e3219cee23
em_all.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 1.8 MB - MD5: 93e6297c2d3ccd12cb2b654f9275d3bc
em_all.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 1.3 MB - MD5: db1b125d06cbabbb483a61df3e18c202
em_all.gro Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 8.3 MB - MD5: f512d97d9cfe437ccd29ed78666cc168
em_all.gro Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 18.0 MB - MD5: 4d5dbed55f2be6e455d8b0f2b750b832

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

em2.edr

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 1.2 MB -

em2.gro

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 27.0 KB -

em2.log

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Plain Text - 2.8 MB -

em2.tpr

Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Unknown - 168.7 KB -