What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Optics
- Composite material
Yong Liu spends much of his time researching Metallurgy, Composite material, Shape-memory alloy, Nickel titanium and Martensite.
His research is interdisciplinary, bridging the disciplines of Deformation and Composite material.
Yong Liu has included themes like Annealing and Condensed matter physics in his Shape-memory alloy study.
His biological study spans a wide range of topics, including Titanium alloy, Differential scanning calorimetry, Structural engineering, Deformation and Damping capacity.
His Martensite study combines topics in areas such as Crystal twinning, Crystallite and Austenite.
His Diffusionless transformation research is multidisciplinary, relying on both Phase, Temperature cycling and Thermodynamics.
His most cited work include:
- Core-shell palladium nanoparticle@metal-organic frameworks as multifunctional catalysts for cascade reactions. (440 citations)
- Core–Shell Noble‐Metal@Metal‐Organic‐Framework Nanoparticles with Highly Selective Sensing Property (412 citations)
- 3D printing of smart materials: A review on recent progresses in 4D printing (365 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Optics, Composite material, Optoelectronics, Shape-memory alloy and Metallurgy.
His work in Fiber laser, Wavelength, Mode-locking, Optical fiber and Dispersion-shifted fiber is related to Optics.
His work deals with themes such as Thin film and Annealing, which intersect with Composite material.
His Shape-memory alloy research incorporates themes from Martensite, Pseudoelasticity, Diffusionless transformation and Stress.
His studies deal with areas such as Austenite and Thermodynamics as well as Martensite.
Alloy and Microstructure are subfields of Metallurgy in which his conducts study.
He most often published in these fields:
- Optics (17.33%)
- Composite material (15.96%)
- Optoelectronics (14.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Optics (17.33%)
- Optoelectronics (14.08%)
- Artificial intelligence (5.01%)
In recent papers he was focusing on the following fields of study:
Yong Liu mainly investigates Optics, Optoelectronics, Artificial intelligence, Fiber laser and Composite material.
His Modulation research extends to Optics, which is thematically connected.
Yong Liu combines subjects such as Bandwidth and Microwave with his study of Optoelectronics.
His Artificial intelligence research incorporates elements of Machine learning, Computer vision and Pattern recognition.
His study in Diffusionless transformation, Shape-memory alloy, Ultimate tensile strength, Deformation and Composite number is done as part of Composite material.
His biological study focuses on Nickel titanium.
Between 2018 and 2021, his most popular works were:
- AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (141 citations)
- Barcoded point-of-care bioassays (43 citations)
- Barcoded point-of-care bioassays (43 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Optics
- Artificial intelligence
His primary scientific interests are in Optics, Fiber laser, Composite material, Optoelectronics and Artificial intelligence.
His research investigates the connection between Fiber laser and topics such as Pulse-width modulation that intersect with problems in Dysprosium and Nanoparticle.
Ultimate tensile strength, Composite number, Alloy, Compressive strength and Tensile testing are the subjects of his Composite material studies.
His research in Ultimate tensile strength intersects with topics in Flexural strength, Nickel titanium, Shape-memory alloy, Titanium alloy and Diffusionless transformation.
His Shape-memory alloy study combines topics from a wide range of disciplines, such as Transformation, Microstructure and Pseudoelasticity.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision.
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