Match tier
Confirmed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-05
J
Julia K. Hoskins
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Graduate Research Assistant
4 h-index
7 pubs
35 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Julia K. Hoskins' research focuses on the development and application of advanced manufacturing techniques, particularly 3D printing, for specialized scientific tools. Her work includes the creation of customizable microsampling devices designed for neuroscience applications, with recent publications exploring probes for neuroscience and high-porosity membranes for microfluidics. Hoskins also investigates the integration of different 3D printing methods, such as two-photon polymerization and LCD printing, to produce microfluidic chips. Her research extends to machine learning and machine vision applications in femtosecond laser machining, and the co-tuning of ultrathin films and 3D textures for adhesion control. She has collaborated with researchers at the University of Arkansas at Fayetteville, including Patrick M. Pysz, Julie A. Stenken, and Min Zou, on multiple publications.
Metrics
- h-index: 4
- Publications: 7
- Citations: 35
Positions
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Graduate Research Assistant publications 2021–2026University of Arkansas at Fayetteville Mechanical Engineering ORCID
Selected Publications
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Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion (2026)
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Multiscale 2PP and LCD 3D Printing for High-Resolution Membrane-Integrated Microfluidic Chips (2025)
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3D Printing of High-Porosity Membranes with Submicron Pores for Microfluidics (2024)
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3D Printed Microsampling Probe for Neuroscience (2024)
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Exploring Machine Learning and Machine Vision in Femtosecond Laser Machining (2023)
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3D Printed Customizable Microsampling Devices for Neuroscience Applications (2023)
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Designing a Bioinspired Surface for Improved Wear Resistance and Friction Reduction (2021)
Collaboration Network
Top Collaborators
Min Zou
University of Arkansas at Fayetteville
6 shared publications
In database
- Designing a Bioinspired Surface for Improved Wear Resistance and Friction Reduction
- 3D Printed Customizable Microsampling Devices for Neuroscience Applications
- 3D Printing of High-Porosity Membranes with Submicron Pores for Microfluidics
- Exploring Machine Learning and Machine Vision in Femtosecond Laser Machining
- Multiscale 2PP and LCD 3D Printing for High-Resolution Membrane-Integrated Microfluidic Chips
Showing 5 of 6 shared publications
Patrick M. Pysz
University of Arkansas at Fayetteville
3 shared publications
In database
- 3D Printed Customizable Microsampling Devices for Neuroscience Applications
- Multiscale 2PP and LCD 3D Printing for High-Resolution Membrane-Integrated Microfluidic Chips
- 3D Printed Microsampling Probe for Neuroscience
Julie A. Stenken
University of Arkansas at Fayetteville
3 shared publications
In database
- 3D Printed Customizable Microsampling Devices for Neuroscience Applications
- Multiscale 2PP and LCD 3D Printing for High-Resolution Membrane-Integrated Microfluidic Chips
- 3D Printed Microsampling Probe for Neuroscience
Han Hu
University of Arkansas at Fayetteville
1 shared publication
In database
- Exploring Machine Learning and Machine Vision in Femtosecond Laser Machining
Nathaniel Harris
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
Shae Threlfall
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
Kang Lu
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
Charles W. Miller
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
Issair Rodriguez-Herrera
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
Senjuti Chanda
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
Xiangbo Meng
University of Arkansas at Fayetteville
1 shared publication
In database
- Co-tuning ultrathin ZnO films and programmable 3D textures to control lunar dust simulant adhesion
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