What is he best known for?
The fields of study he is best known for:
Xiaofeng Li spends much of his time researching Synthetic aperture radar, Remote sensing, Virology, Graphene and Composite material.
His Synthetic aperture radar study integrates concerns from other disciplines, such as Wind speed, Meteorology, Hydrology, Hydrology and Radar.
His study looks at the relationship between Remote sensing and fields such as Buoy, as well as how they intersect with chemical problems.
His Virology study combines topics from a wide range of disciplines, such as Genetics and Antibody.
His Graphene study incorporates themes from Dispersion, Oxide, Thermal conductivity and Epoxy.
His work deals with themes such as Surface modification and Anisotropy, which intersect with Composite material.
His most cited work include:
- Structure of M pro from SARS-CoV-2 and discovery of its inhibitors. (1031 citations)
- Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells. (518 citations)
- A single mutation in the prM protein of Zika virus contributes to fetal microcephaly (255 citations)
What are the main themes of his work throughout his whole career to date?
Xiaofeng Li mainly focuses on Remote sensing, Synthetic aperture radar, Virology, Meteorology and Wind speed.
His Remote sensing research incorporates elements of Inverse synthetic aperture radar, Sea surface temperature and Wind wave.
His work is dedicated to discovering how Synthetic aperture radar, Tropical cyclone are connected with Typhoon and other disciplines.
His research on Virology often connects related areas such as Antibody.
He does research in Meteorology, focusing on Atmospheric model specifically.
Xiaofeng Li is interested in Wind direction, which is a branch of Wind speed.
He most often published in these fields:
- Remote sensing (23.17%)
- Synthetic aperture radar (23.17%)
- Virology (14.07%)
What were the highlights of his more recent work (between 2018-2021)?
- Remote sensing (23.17%)
- Virology (14.07%)
- Composite material (6.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Remote sensing, Virology, Composite material, Artificial intelligence and Synthetic aperture radar.
In general Remote sensing study, his work on Remote sensing often relates to the realm of Mean squared error, thereby connecting several areas of interest.
His biological study spans a wide range of topics, including Antibody, Monoclonal antibody and Severe acute respiratory syndrome coronavirus 2.
His research on Composite material often connects related topics like Graphene.
His research investigates the connection with Synthetic aperture radar and areas like Pixel which intersect with concerns in Data set.
The various areas that he examines in his Wind speed study include Radar and Surface roughness.
Between 2018 and 2021, his most popular works were:
- Structure of M pro from SARS-CoV-2 and discovery of its inhibitors. (1031 citations)
- Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells. (518 citations)
- A Mouse Model of SARS-CoV-2 Infection and Pathogenesis. (206 citations)
In his most recent research, the most cited papers focused on:
The scientist’s investigation covers issues in Composite material, Virology, Climatology, Brittleness and Digital image correlation.
In his study, Stress is inextricably linked to Discrete element method, which falls within the broad field of Composite material.
His Virology research incorporates themes from Mutation, Antibody and BALB/c.
As part of the same scientific family, Xiaofeng Li usually focuses on Antibody, concentrating on Binding site and intersecting with Gene, Messenger RNA, Viral Vaccine and RNA.
His research in Climatology intersects with topics in Urbanization and Hourly rainfall, Precipitation.
The concepts of his Digital image correlation study are interwoven with issues in Geometry, Fracture mechanics and Split-Hopkinson pressure bar.
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.
