What is he best known for?
The fields of study he is best known for:
- Organic chemistry
- Nanotechnology
- Enzyme
His primary areas of investigation include Nanotechnology, Nanoparticle, Self-assembly, Monolayer and Nanoscopic scale.
The study incorporates disciplines such as Reaction–diffusion system, Distributed computing, Molecular switch, Contact electrification and Electrostatics in addition to Nanotechnology.
His Nanoparticle research is multidisciplinary, incorporating perspectives in Photochemistry, Photochromism, Curvature and Catalysis.
His Self-assembly research includes elements of Crystallization, Metastability, Scanning electron microscope, Scale and Data science.
His Monolayer research is multidisciplinary, incorporating elements of Crystallography, Molecule, Surface plasmon resonance and Adsorption.
The various areas that Bartosz A. Grzybowski examines in his Nanoscopic scale study include Chromatography, Precipitation and Static electricity.
His most cited work include:
- Self-assembly at all scales. (5036 citations)
- Nanoscale Forces and Their Uses in Self‐Assembly (1019 citations)
- Electrostatic Self-Assembly of Binary Nanoparticle Crystals with a Diamond-Like Lattice (635 citations)
What are the main themes of his work throughout his whole career to date?
Bartosz A. Grzybowski mainly investigates Nanotechnology, Nanoparticle, Self-assembly, Chemical physics and Monolayer.
He interconnects Electrostatics, Reaction–diffusion system and Polymer in the investigation of issues within Nanotechnology.
Bartosz A. Grzybowski has researched Nanoparticle in several fields, including Counterion, Catalysis and Adsorption.
His Self-assembly study typically links adjacent topics like Crystallization.
He studies Monolayer, namely Self-assembled monolayer.
He most often published in these fields:
- Nanotechnology (43.24%)
- Nanoparticle (26.26%)
- Self-assembly (12.73%)
What were the highlights of his more recent work (between 2017-2021)?
- Nanoparticle (26.26%)
- Machine learning (4.77%)
- Artificial intelligence (4.77%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Nanoparticle, Machine learning, Artificial intelligence, Biochemical engineering and Nanotechnology.
His biological study spans a wide range of topics, including Chemical physics, Monolayer, Counterion and Catalysis.
His research investigates the connection between Monolayer and topics such as Rectifier that intersect with issues in Optoelectronics.
His Machine learning research integrates issues from Diels–Alder reaction and Steric effects.
His studies deal with areas such as Yield, Sequence and Organic molecules as well as Biochemical engineering.
Nanotechnology is closely attributed to Metal-organic framework in his research.
Between 2017 and 2021, his most popular works were:
- Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory (103 citations)
- Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory (103 citations)
- Systems of mechanized and reactive droplets powered by multi-responsive surfactants (65 citations)
In his most recent research, the most cited papers focused on:
- Organic chemistry
- Enzyme
- Thermodynamics
His primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Yield and Cost savings.
His Artificial intelligence research incorporates themes from Diels alder, Imperfect and Diastereomer.
His Machine learning study integrates concerns from other disciplines, such as Diels–Alder reaction and Steric effects.
Yield is often connected to Biochemical engineering in his work.
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.
