What is he best known for?
The fields of study he is best known for:
- Gene
- Quantum mechanics
- Internal medicine
His primary scientific interests are in Catalysis, Nanotechnology, Inorganic chemistry, Optics and Nanoparticle.
His Catalysis study incorporates themes from Electrocatalyst and Chemical engineering.
His Nanotechnology research focuses on Nanostructure in particular.
Peng Zhang has researched Inorganic chemistry in several fields, including Selectivity, Electrochemistry and Lithium.
His Optics study is mostly concerned with Light beam, Diffraction, Airy beam, Refractive index and Beam.
His Nanoparticle research is multidisciplinary, incorporating perspectives in Absorption and Nanomaterials.
His most cited work include:
- Photochemical route for synthesizing atomically dispersed palladium catalysts (742 citations)
- Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19. (580 citations)
- Enhancing multiphoton upconversion through energy clustering at sublattice level (362 citations)
What are the main themes of his work throughout his whole career to date?
Peng Zhang spends much of his time researching Optics, Artificial intelligence, Catalysis, Chemical engineering and Internal medicine.
His work on Optics deals in particular with Light beam, Diffraction, Refractive index and Beam.
Peng Zhang interconnects Machine learning, Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
His research on Catalysis frequently links to adjacent areas such as Inorganic chemistry.
Particularly relevant to Nanoparticle is his body of work in Chemical engineering.
Many of his studies on Internal medicine involve topics that are commonly interrelated, such as Oncology.
He most often published in these fields:
- Optics (8.49%)
- Artificial intelligence (8.17%)
- Catalysis (6.65%)
What were the highlights of his more recent work (between 2019-2021)?
- Catalysis (6.65%)
- Internal medicine (5.13%)
- Chemical engineering (6.08%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Catalysis, Internal medicine, Chemical engineering, Cancer research and Artificial intelligence.
His research in Catalysis intersects with topics in Inorganic chemistry and Nanoparticle.
Internal medicine is closely attributed to Oncology in his work.
His work on Chemical engineering is being expanded to include thematically relevant topics such as Adsorption.
His Cancer research research includes elements of Cancer cell, Adenocarcinoma and Immunotherapy.
His work deals with themes such as Machine learning, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
Between 2019 and 2021, his most popular works were:
- Association of Inpatient Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Mortality Among Patients With Hypertension Hospitalized With COVID-19. (580 citations)
- Clinical Data on Hospital Environmental Hygiene Monitoring and Medical Staff Protection during the Coronavirus Disease 2019 Outbreak (39 citations)
- O-coordinated W-Mo dual-atom catalyst for pH-universal electrocatalytic hydrogen evolution. (31 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Quantum mechanics
- Internal medicine
His scientific interests lie mostly in Catalysis, Chemical engineering, Nanotechnology, Cancer research and Lung cancer.
His Catalysis research incorporates elements of Inorganic chemistry, Nanoparticle, Atom and Alloy.
His work carried out in the field of Chemical engineering brings together such families of science as Carbon and Adsorption.
His Nanotechnology study frequently links to other fields, such as Metal-organic framework.
His Cancer research research is multidisciplinary, incorporating elements of Immune checkpoint, Cell growth, Immune system and In vivo.
His studies in Lung cancer integrate themes in fields like Cancer, Lung and Cohort.
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.
