What is he best known for?
The fields of study he is best known for:
- Oxygen
- Organic chemistry
- Catalysis
Supercapacitor, Specific surface area, Electrolyte, Graphene and Inorganic chemistry are his primary areas of study.
His Supercapacitor study is focused on Capacitance in general.
His research integrates issues of Carbon, Electrochemistry and Inert gas in his study of Capacitance.
His Specific surface area study combines topics from a wide range of disciplines, such as Crystallinity and Redox.
His study on Electrolyte is mostly dedicated to connecting different topics, such as Nanotechnology.
The concepts of his Graphene study are interwoven with issues in Photocatalysis, Catalysis, Photodegradation, Oxide and Composite number.
His most cited work include:
- Activated porous carbon prepared from paulownia flower for high performance supercapacitor electrodes (160 citations)
- A green and scalable route to yield porous carbon sheets from biomass for supercapacitors with high capacity (149 citations)
- Graphene–CdS composite, synthesis and enhanced photocatalytic activity (148 citations)
What are the main themes of his work throughout his whole career to date?
Fang Xu mainly focuses on Nanotechnology, Graphene, Supercapacitor, Electrolyte and Energy conversion efficiency.
His study looks at the relationship between Nanotechnology and topics such as Photocurrent, which overlap with Surface plasmon resonance and Analytical chemistry.
His Graphene research integrates issues from Composite number, Oxide and Detection limit.
His Supercapacitor research incorporates themes from Carbon, Substrate and Specific surface area.
His Electrolyte study combines topics in areas such as Alkali metal and Catalysis.
The study incorporates disciplines such as Photocatalysis, Photodegradation and Nanorod in addition to Nanoparticle.
He most often published in these fields:
- Nanotechnology (31.25%)
- Graphene (30.00%)
- Supercapacitor (28.75%)
What were the highlights of his more recent work (between 2018-2021)?
- Supercapacitor (28.75%)
- Catalysis (20.00%)
- Graphene (30.00%)
In recent papers he was focusing on the following fields of study:
His main research concerns Supercapacitor, Catalysis, Graphene, Electrolyte and Electrochemistry.
Fang Xu combines subjects such as Ion, Nanowire and Substrate with his study of Supercapacitor.
His Graphene study frequently links to other fields, such as Oxide.
His Electrolyte research is multidisciplinary, incorporating perspectives in Capacitance and Alkali metal.
The Capacitance study combines topics in areas such as Specific surface area and Boron.
His study focuses on the intersection of Electrochemistry and fields such as Carbon with connections in the field of Faraday efficiency and Mesoporous material.
Between 2018 and 2021, his most popular works were:
- The synthesis of Bi2S3/2D-Bi2WO6 composite materials with enhanced photocatalytic activities (39 citations)
- Ultrasonic-assisted synthesis of two dimensional BiOCl/MoS2 with tunable band gap and fast charge separation for enhanced photocatalytic performance under visible light. (37 citations)
- Boron and nitrogen Co-doped holey graphene aerogels with rich B–N motifs for flexible supercapacitors (24 citations)
In his most recent research, the most cited papers focused on:
- Oxygen
- Organic chemistry
- Redox
Fang Xu mostly deals with Photocatalysis, Electrolyte, Catalysis, Electrochemistry and Rhodamine B.
His Electrolyte research is multidisciplinary, relying on both Boron, Capacitance, Supercapacitor, Specific surface area and Graphene.
His Catalysis research incorporates elements of Photocurrent, Molecule, Shell and Gaseous diffusion.
His work carried out in the field of Electrochemistry brings together such families of science as Porosity, Carbon, Selectivity and Electron transfer.
His Rhodamine B study incorporates themes from Ion exchange, Photodegradation, Visible spectrum, Composite number and Band gap.
He has included themes like Optoelectronics, Composite material, Oxygen and Nanosheet in his Photodegradation study.
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.
