What is he best known for?
The fields of study he is best known for:
- Composite material
- Polymer
- Organic chemistry
His scientific interests lie mostly in Self-healing hydrogels, Composite material, Polymer, Polymer chemistry and Polyelectrolyte.
His Self-healing hydrogels study incorporates themes from Nanotechnology, Toughness and Polyacrylamide.
His study looks at the relationship between Composite material and fields such as Bacterial cellulose, as well as how they intersect with chemical problems.
His Polymer research is multidisciplinary, incorporating elements of Elastomer, Fracture mechanics and Elastic modulus.
His Polymer chemistry research includes themes of Polymer gel, Biophysics, Thermal and Deformation.
Jian Ping Gong interconnects Interpenetrating polymer network, Chromatography and Chemical physics in the investigation of issues within Polyelectrolyte.
His most cited work include:
- Double‐Network Hydrogels with Extremely High Mechanical Strength (2198 citations)
- Why are double network hydrogels so tough (1034 citations)
- Physical hydrogels composed of polyampholytes demonstrate high toughness and viscoelasticity (891 citations)
What are the main themes of his work throughout his whole career to date?
Jian Ping Gong mostly deals with Self-healing hydrogels, Composite material, Polymer chemistry, Polymer and Polyelectrolyte.
His Self-healing hydrogels study combines topics in areas such as Nanotechnology, Swelling and Toughness.
His study in Fracture, Stress, Deformation, Brittleness and Ultimate tensile strength is carried out as part of his studies in Composite material.
His study looks at the intersection of Polymer chemistry and topics like Monomer with Cationic polymerization and Polymerization.
Many of his studies on Polymer apply to Chemical physics as well.
His studies in Polyelectrolyte integrate themes in fields like Ionic bonding, Ionic strength and Pulmonary surfactant.
He most often published in these fields:
- Self-healing hydrogels (48.53%)
- Composite material (34.77%)
- Polymer chemistry (24.75%)
What were the highlights of his more recent work (between 2015-2021)?
- Self-healing hydrogels (48.53%)
- Composite material (34.77%)
- Double network (12.97%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Self-healing hydrogels, Composite material, Double network, Chemical engineering and Polymer.
His Self-healing hydrogels research includes elements of Nanotechnology, Ionic bonding, Polyelectrolyte, Swelling and Anisotropy.
His research brings together the fields of Dissipation and Composite material.
His Double network research incorporates elements of Coating and Biomedical engineering.
Jian Ping Gong studied Chemical engineering and Brittleness that intersect with Modulus.
In his research on the topic of Polymer, Shearing and Polymer solution is strongly related with Rheology.
Between 2015 and 2021, his most popular works were:
- Tough Physical Double-Network Hydrogels Based on Amphiphilic Triblock Copolymers. (203 citations)
- Mechanoresponsive self-growing hydrogels inspired by muscle training (150 citations)
- Double-Network Hydrogels Strongly Bondable to Bones by Spontaneous Osteogenesis Penetration. (116 citations)
In his most recent research, the most cited papers focused on:
- Composite material
- Polymer
- Organic chemistry
His main research concerns Self-healing hydrogels, Composite material, Double network, Nanotechnology and Chemical engineering.
The Self-healing hydrogels study combines topics in areas such as Polymer, Ionic bonding, Toughness, Dissipation and Swelling.
Jian Ping Gong has researched Composite material in several fields, including Phase and Anisotropy.
His Double network research integrates issues from Wear resistance, Cartilage, Osseointegration, Coating and Biomedical engineering.
His biological study spans a wide range of topics, including Hydrogel membrane and Stamping.
He combines subjects such as Copolymer, Amphiphile, Lamellar structure and Brittleness with his study of Chemical engineering.
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.
