What is he best known for?
The fields of study he is best known for:
- Semiconductor
- Organic chemistry
- Oxygen
Xiangfeng Duan focuses on Nanotechnology, Optoelectronics, Nanowire, Graphene and Semiconductor.
His Nanotechnology research incorporates elements of Transistor, Band gap and Electronics.
His research in Optoelectronics focuses on subjects like Field-effect transistor, which are connected to Orders of magnitude and Gallium nitride.
The study incorporates disciplines such as Dielectrophoresis, Yield, Nanoscopic scale and Silicon in addition to Nanowire.
His studies deal with areas such as Supercapacitor, Oxygen evolution and Porosity as well as Graphene.
His Semiconductor research is multidisciplinary, incorporating perspectives in Photocatalysis, Charge separation, Photodiode, van der Waals force and Heterojunction.
His most cited work include:
- Indium phosphide nanowires as building blocks for nanoscale electronic and optoelectronic devices (2946 citations)
- Single-nanowire electrically driven lasers (2075 citations)
- Directed Assembly of One-Dimensional Nanostructures into Functional Networks (1971 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Nanotechnology, Optoelectronics, Graphene, Nanowire and Semiconductor.
His Nanotechnology study incorporates themes from Chemical engineering, Catalysis and Electronics.
His work deals with themes such as Field-effect transistor and Transistor, which intersect with Optoelectronics.
His studies in Transistor integrate themes in fields like Electronic circuit and Electron mobility.
His research integrates issues of Monolayer, Oxide and Band gap in his study of Graphene.
The Nanowire study combines topics in areas such as Platinum, Substrate and Silicon.
He most often published in these fields:
- Nanotechnology (46.94%)
- Optoelectronics (34.72%)
- Graphene (29.26%)
What were the highlights of his more recent work (between 2018-2021)?
- Optoelectronics (34.72%)
- Monolayer (8.95%)
- Nanotechnology (46.94%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Optoelectronics, Monolayer, Nanotechnology, Graphene and Catalysis.
Xiangfeng Duan has included themes like Transistor and Voltage in his Optoelectronics study.
Xiangfeng Duan combines subjects such as Semiconductor and Density of states with his study of Transistor.
In general Nanotechnology study, his work on Nanoscopic scale often relates to the realm of Energy transformation, thereby connecting several areas of interest.
His Graphene research includes themes of Silicon nitride, Self-assembled monolayer and Carbon nanotube.
His Catalysis research includes elements of Electrocatalyst, Nanowire, Nanoparticle, Chemical engineering and Atom.
Between 2018 and 2021, his most popular works were:
- Van der Waals integration before and beyond two-dimensional materials. (264 citations)
- Hierarchical 3D electrodes for electrochemical energy storage (202 citations)
- Nanoscale Structure Design for High‐Performance Pt‐Based ORR Catalysts (163 citations)
In his most recent research, the most cited papers focused on:
- Semiconductor
- Organic chemistry
- Oxygen
His main research concerns Catalysis, Nanotechnology, van der Waals force, Chemical engineering and Optoelectronics.
Xiangfeng Duan interconnects Nanoparticle, Nanowire and Oxygen reduction reaction, Electrochemistry in the investigation of issues within Catalysis.
His work on Nanostructure as part of general Nanotechnology study is frequently linked to Energy transformation, bridging the gap between disciplines.
The various areas that Xiangfeng Duan examines in his van der Waals force study include Field-effect transistor, Transistor, Thin film, Monolayer and Heterojunction.
His study in the fields of Semiconductor and Sensitivity under the domain of Optoelectronics overlaps with other disciplines such as Current density.
His Electrocatalyst research integrates issues from Transmission electron microscopy, Platinum and Graphene.
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.
