What is he best known for?
The fields of study he is best known for:
- Catalysis
- Organic chemistry
- Oxygen
His primary scientific interests are in Catalysis, Inorganic chemistry, Methanol, Nanotechnology and Nanoparticle.
The subject of his Catalysis research is within the realm of Organic chemistry.
His Inorganic chemistry study combines topics in areas such as Self-assembly, Iron oxide, Hydrocarbon and Methane.
Liangshu Zhong has included themes like Hydrotalcite, Dispersion and Hydrogen in his Methanol study.
Many of his research projects under Nanotechnology are closely connected to SPHERES with SPHERES, tying the diverse disciplines of science together.
His Selectivity research is multidisciplinary, incorporating elements of Heterogeneous catalysis and Bifunctional catalyst.
His most cited work include:
- Self‐Assembled 3D Flowerlike Iron Oxide Nanostructures and Their Application in Water Treatment (1333 citations)
- Tin-Nanoparticles Encapsulated in Elastic Hollow Carbon Spheres for High-Performance Anode Material in Lithium-Ion Batteries† (895 citations)
- Synthesis of Hierarchically Structured Metal Oxides and their Application in Heavy Metal Ion Removal (485 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Catalysis, Selectivity, Syngas, Inorganic chemistry and Methanol.
His study in Catalysis is interdisciplinary in nature, drawing from both Yield and Nanoparticle.
His Selectivity research integrates issues from Bifunctional, Product distribution, Methane, Combinatorial chemistry and Oxygenate.
His work carried out in the field of Syngas brings together such families of science as Cobalt, Spinel, Adsorption and Nanostructure.
Liangshu Zhong works mostly in the field of Inorganic chemistry, limiting it down to concerns involving Desorption and, occasionally, Chemisorption.
His Methanol research also works with subjects such as
- Hydrotalcite, which have a strong connection to Specific surface area,
- Photocatalysis which intersects with area such as Acetonitrile and Regioselectivity,
- Ethylene glycol, which have a strong connection to Formaldehyde and Nanotechnology.
He most often published in these fields:
- Catalysis (132.09%)
- Selectivity (79.10%)
- Syngas (62.69%)
What were the highlights of his more recent work (between 2019-2021)?
- Catalysis (132.09%)
- Selectivity (79.10%)
- Syngas (62.69%)
In recent papers he was focusing on the following fields of study:
Liangshu Zhong mostly deals with Catalysis, Selectivity, Syngas, Combinatorial chemistry and Fischer–Tropsch process.
His Catalysis research incorporates themes from Yield, Methanol and Adsorption.
His studies deal with areas such as Methane, Oxygenate and Calcination as well as Selectivity.
His Syngas research is multidisciplinary, incorporating perspectives in Oxide, Precipitation and Copper.
He interconnects Cobalt catalyst and Regioselectivity in the investigation of issues within Combinatorial chemistry.
His Fischer–Tropsch process study incorporates themes from Activation energy, Nanostructure, Cobalt, Spinel and Alkali metal.
Between 2019 and 2021, his most popular works were:
- Enhancing CO2 methanation over a metal foam structured catalyst by electric internal heating. (7 citations)
- Control of Co0/Co2C dual active sites for higher alcohols synthesis from syngas (6 citations)
- Control of Co0/Co2C dual active sites for higher alcohols synthesis from syngas (6 citations)
In his most recent research, the most cited papers focused on:
- Catalysis
- Organic chemistry
- Oxygen
Liangshu Zhong focuses on Catalysis, Selectivity, Syngas, Fischer–Tropsch process and Oxygenate.
Methanation is the focus of his Catalysis research.
His studies in Fischer–Tropsch process integrate themes in fields like Electronic effect, Alkali metal, Dissociation and Reaction conditions.
His Cobalt research includes elements of Activation energy and Nanostructure.
His Oxide research includes themes of Spinel, Precipitation and Calcination.
As part of his studies on Combinatorial chemistry, Liangshu Zhong frequently links adjacent subjects like High selectivity.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
