What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Operating system
- Electron
His main research concerns Nanotechnology, Graphene, Mathematical analysis, Boundary value problem and Chemical engineering.
Xiaoping Wang is involved in the study of Nanotechnology that focuses on Nanostructure in particular.
His Graphene research is multidisciplinary, incorporating perspectives in Nanoscopic scale, Oxide, Doping, Raman spectroscopy and Zigzag.
His research integrates issues of Projection method, Stability and Nonlinear system in his study of Mathematical analysis.
His Boundary value problem study also includes
- Dissipation, which have a strong connection to Slip,
- Mechanics which is related to area like Continuum, Molecular dynamics and Contact line.
His Chemical engineering study combines topics from a wide range of disciplines, such as Anode and Iridium.
His most cited work include:
- Highly efficient dye adsorption and removal: a functional hybrid of reduced graphene oxide–Fe3O4 nanoparticles as an easily regenerative adsorbent (286 citations)
- Molecular scale contact line hydrodynamics of immiscible flows. (268 citations)
- An amperometric glucose biosensor based on the immobilization of glucose oxidase on the ZnO nanotubes (266 citations)
What are the main themes of his work throughout his whole career to date?
Xiaoping Wang mostly deals with Nanotechnology, Condensed matter physics, Boundary value problem, Mathematical analysis and Optoelectronics.
His Nanotechnology study frequently draws parallels with other fields, such as Chemical engineering.
Xiaoping Wang studies Superconductivity, a branch of Condensed matter physics.
His work carried out in the field of Boundary value problem brings together such families of science as Slip, Mechanics, Boundary and Dissipation.
While the research belongs to areas of Mechanics, Xiaoping Wang spends his time largely on the problem of Classical mechanics, intersecting his research to questions surrounding Molecular dynamics.
His research is interdisciplinary, bridging the disciplines of Optics and Optoelectronics.
He most often published in these fields:
- Nanotechnology (12.03%)
- Condensed matter physics (11.60%)
- Boundary value problem (10.55%)
What were the highlights of his more recent work (between 2018-2021)?
- Memristor (7.81%)
- Topology (2.74%)
- Attractor (2.74%)
In recent papers he was focusing on the following fields of study:
Xiaoping Wang mainly focuses on Memristor, Topology, Attractor, Artificial intelligence and Artificial neural network.
His Memristor study results in a more complete grasp of Electronic engineering.
His Attractor study incorporates themes from Randomness and Chaotic, Lyapunov exponent, Nonlinear system.
His Nonlinear system research focuses on Lorenz system in particular.
His work on Artificial neural network is being expanded to include thematically relevant topics such as Algorithm.
His Process research incorporates themes from Signal and State.
Between 2018 and 2021, his most popular works were:
- Fabrication of Low-Cost and Highly Sensitive Graphene-Based Pressure Sensors by Direct Laser Scribing Polydimethylsiloxane. (25 citations)
- Novel circuit designs of memristor synapse and neuron (18 citations)
- An improved Elman neural network with piecewise weighted gradient for time series prediction (16 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Operating system
- Electron
His primary areas of investigation include Topology, Attractor, Memristor, Nonlinear system and Chaotic.
His research on Topology frequently connects to adjacent areas such as Initial value problem.
His research investigates the link between Attractor and topics such as Randomness that cross with problems in Multistability, Offset, Boosting, Computer hardware and Phase space.
His studies in Memristor integrate themes in fields like Artificial neural network, Artificial neuron and Electronic circuit.
His studies deal with areas such as Optimization problem, Electronic engineering, Image restoration, Network operations center and Amplifier as well as Artificial neural network.
In general Nonlinear system study, his work on Lyapunov exponent often relates to the realm of Constant, thereby connecting several areas of interest.
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