Surface Science and DFT Data for Adsorbed Molecules and CO2 Reduction Catalysts

A collection of research data pairs experimental surface science techniques like scanning tunneling microscopy with computational density functional theory. The dataset, authored by Alnald Javier, focuses on elucidating the structure of molecules adsorbed on metal surfaces and studying CO2-reduction electrocatalysts. It includes references to studies on hydroquinone, benzene, sulfuric acid, and atomic hydrogen on Pd surfaces, as well as novel catalysts like Au-on-W and NiGa alloys.

Use Cases

• Validate computational models of molecular adsorption based on experimental STM and HREELS data.
• Study structure-composition-function relationships for surface-modified materials used in electrocatalysis.
• Investigate CO2-reduction mechanisms to improve product yield and selectivity for fuel production.
• Benchmark DFT calculations against experimental data for self-assembled monolayers on electrode surfaces.

Strengths

• Dataset is based on a complementary approach combining multiple established surface science techniques.
• Includes references to specific, published studies on molecules like hydroquinone and catalysts like NiGa alloy.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Compilation of research data from published studies employing complementary experimental and computational methods.