What is he best known for?
The fields of study he is best known for:
- Oxygen
- Enzyme
- Electrical engineering
Gerald Urban mainly investigates Nanotechnology, Biosensor, Microfluidics, Chromatography and Glucose oxidase.
His Nanotechnology research includes themes of Membrane, Protein expression, Biomedical engineering and Reference electrode.
His studies deal with areas such as Amperometry, Enzyme electrode, Microelectrode, Immobilized enzyme and Microsystem as well as Biosensor.
Gerald Urban combines subjects such as Control theory, 3D cell culture, Resist, Chip and Multiplexing with his study of Microfluidics.
His Chromatography research includes elements of Thin film, Buffer and Analytical chemistry.
As part of the same scientific family, Gerald Urban usually focuses on Analytical chemistry, concentrating on Lactic acid and intersecting with Microdialysis.
His most cited work include:
- Thin-film microbiosensors for glucose-lactate monitoring (266 citations)
- Miniaturized thin-film biosensors using covalently immobilized glucose oxidase☆ (220 citations)
- Determination of quantum confinement in CdSe nanocrystals by cyclic voltammetry (215 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Nanotechnology, Biosensor, Analytical chemistry, Optoelectronics and Microfluidics.
Gerald Urban usually deals with Nanotechnology and limits it to topics linked to Electrochemistry and Inorganic chemistry.
He has included themes like Immobilized enzyme, Chromatography, Membrane and Biomedical engineering in his Biosensor study.
His work in Chromatography addresses subjects such as Lysis, which are connected to disciplines such as Nucleic acid.
His research integrates issues of Thin film, Thermal and Chemical engineering in his study of Analytical chemistry.
Microfluidics is frequently linked to Chip in his study.
He most often published in these fields:
- Nanotechnology (29.01%)
- Biosensor (18.63%)
- Analytical chemistry (16.98%)
What were the highlights of his more recent work (between 2017-2021)?
- Biosensor (18.63%)
- Nanotechnology (29.01%)
- Nucleic acid (3.30%)
In recent papers he was focusing on the following fields of study:
Biosensor, Nanotechnology, Nucleic acid, Chemical engineering and Biomedical engineering are his primary areas of study.
His research in Biosensor intersects with topics in Electrochemical biosensor and CRISPR.
His biological study focuses on Microfluidics.
His study in Nucleic acid is interdisciplinary in nature, drawing from both Chromatography and Lab-on-a-chip.
The study incorporates disciplines such as Microreactor, Platinum, Carbon and Electrochemistry, Electrochemical cell in addition to Chemical engineering.
The concepts of his Biomedical engineering study are interwoven with issues in Confocal microscopy, Optical coherence tomography and Photodynamic therapy.
Between 2017 and 2021, his most popular works were:
- Disposable Sensors in Diagnostics, Food, and Environmental Monitoring. (131 citations)
- Microsensor systems for cell metabolism - from 2D culture to organ-on-chip. (67 citations)
- CRISPR/Cas13a‐Powered Electrochemical Microfluidic Biosensor for Nucleic Acid Amplification‐Free miRNA Diagnostics (56 citations)
In his most recent research, the most cited papers focused on:
- Enzyme
- Oxygen
- Electrical engineering
His primary areas of investigation include Nanotechnology, Chemical engineering, Biosensor, Electrochemistry and Nucleic acid.
His Nanotechnology study integrates concerns from other disciplines, such as Interdigitated electrode and 3D cell culture.
His Chemical engineering research is multidisciplinary, relying on both Inner sphere electron transfer, Platinum, Hydrogen peroxide, Cyclic voltammetry and Electrochemical cell.
Particularly relevant to Microfluidic biosensor is his body of work in Biosensor.
His work deals with themes such as Plasma-enhanced chemical vapor deposition, Adsorption, Catalysis, Electron transfer and Carbon, which intersect with Electrochemistry.
His Nucleic acid study incorporates themes from Transistor, Gene, Cas9 and Graphene.
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.
