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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11 to 20 of 1,794 Results

em_all.log Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Plain Text - 3.9 MB - MD5: 673d6bcee4de49cb8e1d0fa1d0ca1b04
em_all.log Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Plain Text - 2.9 MB - MD5: 433ab193651806c373c0fe5d79fba1a9
em_all.tpr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 4.6 MB - MD5: a3d6b9a10ec6bf2017d6147ef4652317
em_all.tpr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 9.5 MB - MD5: b779d276df39ee104a30faf007fec23f
em_all.trr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 4.7 MB - MD5: 6178bc22fb48ee550ef0f6fbc96a6ba2
em_all.trr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 2.2 MB - MD5: f53adb4d5d801aa4f44c777ce9aa83e0
em.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 144.1 KB - MD5: 48db65ad609500499ac110827d62ffe9
em.edr Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 2.8 MB - MD5: 54ae85e561db40ef598e4fb4b1ba6ab7
em.gro Oct 31, 2023 - Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Unknown - 27.1 KB - MD5: 878c89e32beeead3cf613dc92e9c302f
em.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: 87afd359d2373782cb5bed0c3625dfce

em_all.log

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

Plain Text - 3.9 MB -

em_all.log

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

Plain Text - 2.9 MB -

em_all.tpr

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

Unknown - 4.6 MB -

em_all.tpr

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

Unknown - 9.5 MB -