What is he best known for?
The fields of study Michal Kruk is best known for:
- Catalysis
- Adsorption
- Polymer
Michal Kruk links adjacent fields of study such as Characterization (materials science), Transmission electron microscopy, Nanoporous and Template in the subject of Nanotechnology.
Michal Kruk integrates many fields, such as Characterization (materials science) and X-ray crystallography, in his works.
He regularly ties together related areas like Optics in his Transmission electron microscopy studies.
Michal Kruk connects Optics with Diffraction in his study.
Michal Kruk undertakes multidisciplinary investigations into Diffraction and X-ray crystallography in his work.
Many of his studies on Template involve topics that are commonly interrelated, such as Nanotechnology.
In his papers, he integrates diverse fields, such as Porosity, Porous medium and Specific surface area.
He conducted interdisciplinary study in his works that combined Porous medium and Porosity.
Chemical engineering is often connected to Nanoporous in his work.
His most cited work include:
- Gas Adsorption Characterization of Ordered Organic−Inorganic Nanocomposite Materials (2881 citations)
- Synthesis of New, Nanoporous Carbon with Hexagonally Ordered Mesostructure (2234 citations)
- Ordered Mesoporous Carbons (1435 citations)
What are the main themes of his work throughout his whole career to date
Poloxamer and Ethylene oxide are the two main areas of interest in his Copolymer studies.
His study in Polymer extends to Poloxamer with its themes.
In his articles, Michal Kruk combines various disciplines, including Polymer and Polymer chemistry.
Michal Kruk undertakes multidisciplinary investigations into Polymer chemistry and Copolymer in his work.
Michal Kruk regularly ties together related areas like Pulmonary surfactant in his Chemical engineering studies.
His research on Organic chemistry often connects related topics like Molecular sieve.
He combines Catalysis and Specific surface area in his research.
His Mesoporous material study frequently links to related topics such as MCM-41.
His MCM-41 study frequently intersects with other fields, such as Mesoporous material.
Michal Kruk most often published in these fields:
- Chemical engineering (87.18%)
- Organic chemistry (83.76%)
- Catalysis (69.23%)
What were the highlights of his more recent work (between 2015-2019)?
- Artificial intelligence (50.00%)
- Chemical engineering (50.00%)
- Organic chemistry (50.00%)
In recent works Michal Kruk was focusing on the following fields of study:
Throughout his Support vector machine studies, Michal Kruk incorporates elements of other sciences such as Feature selection, Discriminative model, Linear discriminant analysis and Random forest.
Michal Kruk performs multidisciplinary studies into Feature selection and Support vector machine in his work.
His Artificial intelligence study frequently draws connections to other fields, such as Segmentation.
His study in Pulmonary surfactant extends to Chemical engineering with its themes.
His Pulmonary surfactant study frequently links to other fields, such as Chemical engineering.
Organic chemistry is closely attributed to Amine gas treating in his research.
Amine gas treating and Organic chemistry are commonly linked in his work.
Much of his study explores Composite material relationship to Matrix (chemical analysis).
He combines topics linked to Composite material with his work on Matrix (chemical analysis).
Between 2015 and 2019, his most popular works were:
- Synthesis of large-pore face-centered-cubic periodic mesoporous organosilicas with unsaturated bridging groups (26 citations)
- Novel methods of image description and ensemble of classifiers in application to mammogram analysis (23 citations)
- False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification (19 citations)
In his most recent research, the most cited works focused on:
- Small-angle X-ray scattering
- Crystallography
- Cancer
His study in Support vector machine intersects with areas of studies such as Linear discriminant analysis, Discriminative model, Random forest and Feature selection.
Michal Kruk conducts interdisciplinary study in the fields of Linear discriminant analysis and Support vector machine through his works.
The study of Artificial intelligence is intertwined with the study of False positive paradox in a number of ways.
His study connects Random subspace method and Classifier (UML).
As part of his studies on Random subspace method, he often connects relevant areas like Classifier (UML).
Pattern recognition (psychology) connects with themes related to Artificial intelligence in his study.
As part of his studies on Image (mathematics), Michal Kruk often connects relevant subjects like Computer vision.
His study connects Image (mathematics) and Computer vision.
He undertakes interdisciplinary study in the fields of Ensemble learning and Machine learning through his research.
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.
