What is he best known for?
The fields of study he is best known for:
- Optics
- Optical fiber
- Electrical engineering
His primary scientific interests are in Optical fiber, Optics, Fiber optic sensor, Refractive index and Optoelectronics.
His Optical fiber research incorporates themes from Layer by layer, Indium tin oxide and Optical power.
His work in Optics tackles topics such as Thin film which are related to areas like Polymer.
His work carried out in the field of Fiber optic sensor brings together such families of science as Coating, Nanotechnology, Core and Analytical chemistry.
His biological study spans a wide range of topics, including Waveguide, Resonance, Cladding and Multi-mode optical fiber.
His biological study focuses on Photonic-crystal fiber.
His most cited work include:
- Optimization of sensitivity in Long Period Fiber Gratings with overlay deposition. (271 citations)
- Optical fiber humidity sensor based on a tapered fiber coated with agarose gel (216 citations)
- Optical fiber humidity sensor using a nano Fabry–Perot cavity formed by the ionic self-assembly method (174 citations)
What are the main themes of his work throughout his whole career to date?
Ignacio R. Matias mainly focuses on Optical fiber, Optics, Optoelectronics, Fiber optic sensor and Refractive index.
His study explores the link between Optical fiber and topics such as Coating that cross with problems in Polymer and Chemical engineering.
His work deals with themes such as Indium tin oxide and Single-mode optical fiber, which intersect with Optoelectronics.
Ignacio R. Matias interconnects Self-assembly, Optical power and Analytical chemistry in the investigation of issues within Fiber optic sensor.
His work focuses on many connections between Refractive index and other disciplines, such as Surface plasmon resonance, that overlap with his field of interest in Surface plasmon.
His Cladding research includes elements of Coupled mode theory and Step-index profile.
He most often published in these fields:
- Optical fiber (63.80%)
- Optics (42.03%)
- Optoelectronics (41.01%)
What were the highlights of his more recent work (between 2016-2021)?
- Optical fiber (63.80%)
- Optoelectronics (41.01%)
- Refractive index (28.10%)
In recent papers he was focusing on the following fields of study:
His main research concerns Optical fiber, Optoelectronics, Refractive index, Fiber optic sensor and Optics.
His study in the field of Fiber Bragg grating is also linked to topics like Humidity.
His Optoelectronics research integrates issues from Thin film, Multi-mode optical fiber and Single-mode optical fiber.
His research in Refractive index intersects with topics in Attenuation, Spectrometer and Full width at half maximum.
His research on Fiber optic sensor often connects related topics like Guided-mode resonance.
His research investigates the connection between Optics and topics such as Resonance that intersect with problems in Lossy mode resonance.
Between 2016 and 2021, his most popular works were:
- Recent Developments in Fiber Optics Humidity Sensors. (87 citations)
- Optical sensors based on lossy-mode resonances (75 citations)
- Femtomolar Detection by Nanocoated Fiber Label-Free Biosensors. (71 citations)
In his most recent research, the most cited papers focused on:
- Optics
- Electrical engineering
- Organic chemistry
His scientific interests lie mostly in Optical fiber, Refractive index, Optoelectronics, Fiber optic sensor and Biosensor.
Ignacio R. Matias combines subjects such as Nanotechnology and Indium tin oxide with his study of Optical fiber.
Optics covers Ignacio R. Matias research in Refractive index.
His work investigates the relationship between Optics and topics such as Resonance that intersect with problems in Surface plasmon resonance, Voltage, Waveguide and Chemical species.
His study in Optoelectronics is interdisciplinary in nature, drawing from both Thin film and Interferometry.
The concepts of his Fiber optic sensor study are interwoven with issues in Tin dioxide and Multi-mode optical fiber.
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.
