Nanotech 2022

JUNE 13-15, 2022
WASHINGTON, DC


Materials Characterization & Imaging

Nanoscale Materials Characterization

Symposium Co-Chairs

Greg HaugstadGreg Haugstad
Technical Staff Member & Director, Characterization Facility (CharFac), University of Minnesota

Dalia YablonDalia Yablon
Technical Program Chair
TechConnect World Innovation Conference

Alex NormanAlex Norman
Associate Director of Materials Science
Modern Meadow

Key Speakers

Igor SokolovMachine learning for recognition and classification of AFM images
Igor Sokolov
Professor, Tufts University

Jerry HunterEffect of user fees on core facility income and user behavior: It’s not what you think
Jerry Hunter
Director, Wisconsin Centers for Nanoscale Technology

Babak EslamiEnhancing or Diminishing Sensitivity in Multifrequency AFM
Babak Eslami
Assistant Professor, Widener University

Arzu ColakFriction Property of Ti3C2Tx MXene at the Nanoscale
Arzu Colak
Research Assistant Professor, Clarkson University

Bede PittengerCharacterizing ferroelectric properties via SS-PFM: separating the instrument from the sample
Bede Pittenger
Application Scientist, Bruker Nano

Jason KillgoreVoxel Scale Characterization of Photopolymer Additive Manufacturing
Jason Killgore
Project Leader, National Institute of Standards and Technology

Liang GongNanoscale Materials Analysis using AFM-IR
Liang Gong
Senior Research Scientist, 3M

Nan YaoImaging and analysis - an engine for the discovery and innovation
Nan Yao
Professor & Founding Director, Princeton University Imaging and Analysis Center

Jacob JonesThe North Carolina Research Triangle Nanotechnology Network (RTNN)
Jacob Jones
Professor, North Carolina State University

Doug YatesAdvanced in situ microscopy techniques: Overview, management and success stories
Doug Yates
Director, Nanoscale Characterization Facility, University of Pennsylvania

Christopher SimsComparison Analysis of 2D Nanomaterial Dispersions using Analytical Ultracentrifugation and Microscopy Methods
Christopher Sims
Research Chemist, National Institute of Standards and Technology

Jason KillgoreQuantitative Piezoresponse Force Microscopy for Every User
Jason Killgore
Project Leader, NIST

This year's symposium will focus on innovations in nanoscale materials characterization including new method development as well as industry-specific applications. Submissions are also invited for special sessions focused on machine learning for materials characterization and imaging (in conjunction with the AI TechConnect Conference); and on industrial applications of x-ray scattering methods.

 
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2022 Symposium Sessions

Monday June 13

10:30Advanced Characterization Opportunities at Academic Centers of Excellence
1:30Material Characterization & Imaging

Tuesday June 14

9:00Characterization with Scanning Probe Microscopy
1:30Machine Learning for Characterization

2022 Symposium Program

Monday June 13

10:30Advanced Characterization Opportunities at Academic Centers of ExcellenceChesapeake A
Session chair: Alex Norman, Modern Meadow, US
10:30Imaging and analysis - an engine for the discovery and innovation
N. Yao, Princeton University, US
10:55Effect of user fees on core facility income and user behavior: It’s not what you think
J. Hunter, University of Wisconsin-Madison, US
11:20The North Carolina Research Triangle Nanotechnology Network (RTNN)
J.L. Jones, North Carolina State University, US
11:55Advanced in situ microscopy techniques: Overview, management and success stories
D. Yates, Nanoscale Characterization Facility, University of Pennsylvania, US
1:30Material Characterization & ImagingChesapeake A
Session chair: Greg Haugstad, University of Minnesota, US
1:30Comparison Analysis of 2D Nanomaterial Dispersions using Analytical Ultracentrifugation and Microscopy Methods
C. Sims, National Institute of Standards and Technology, US
1:55Nanoplastic arrays – a new order of microspectroscopy standards
A. Madison, D. Westly, B. Ilic, C. Copeland, A. Pintar, C. Camp, J. Liddle, S. Stavis, National Institute of Standards and Technology, US
2:15Ceramic sintering and properties characterization based on solid mechanics
H. Camacho Montes, Y. Espinosa Almeyda, J.D. Gamboa Garay, L.E. Barraza de León, A. Vega Siverio, I.M. Espinoza Ochoa, J.A. Otero Hernández, R. Rodríguez Ramos, B.J. Mederos Madrazo, F.J. Sabina, R.K. Bordia, Universidad Autónoma de Ciudad Juárez, MX
2:35Rheo-Raman microscopy for crystallization and crosslinking
A. Kotula, National Institute of Standards and Technology, US
2:55Rapid multiphoton autofluorescence lifetime imaging of retinal tissue
Y-I Chen, T.D. Nguyen, Y-J Chang, S-C Liao, Y-A Kuo, S. Hong, G. Rylander III, S.R. Santacruz, T. Yeh, University of Texas at Austin, US

Tuesday June 14

9:00Characterization with Scanning Probe MicroscopyChesapeake A
Session chair: Greg Haugstad, University of Minnesota, US
9:00Quantitative Piezoresponse Force Microscopy for Every User
J. Killgore, National Institute of Standards and Technology, US
9:25Characterizing ferroelectric properties via SS-PFM: separating the instrument from the sample
B. Pittenger, Bruker Nano, US
9:50Nanoscale Materials Analysis using AFM-IR
L. Gong, 3M Company, US
11:15Friction Property of Ti3C2Tx MXene at the Nanoscale
A. Colak, Clarkson University, US
11:40Enhancing or Diminishing Sensitivity in Multifrequency AFM
B. Eslami, Widener, US
1:30Machine Learning for CharacterizationChesapeake A
Session chair: Alex Norman, Modern Meadow, US
1:30Machine Learning for Automated Experiments in Charged Particle Beam Tools
A. Belianinov, Sandia National Laboratories, US
1:55Detecting Malaria Parasites with Machine Learning
S. Jaeger, NLM/NIH, US
2:20Machine learning for recognition and classification of AFM images
I. Sokolov, Tufts University, US
2:45Automated Classification of Defected Images by Means of Machine Learning for Improved Analysis of Vehicle Undercarriages
W.V. Giegerich, A. Stone, L. Forte III, P.J. Schneider, K.W. Oh, University at Buffalo, US

Topics & Application Areas

  • User facilities for industrial participation
  • Advances in image analysis
  • Characterization of real-world systems
  • Machine learning for Characterization
  • Other
 
 
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