Sir William Henry Bragg Building, Leeds University, Leeds, UK
This one-day workshop offers hands-on beginner and expert-level training in both practical techniques and data analysis for AFM and SPM. It is available as an optional add-on for delegates attending the AFM & SPM 2026 meeting, with a small additional fee of £40 to cover catering and consumables.
This workshop is open to all attendees of the AFM & SPM 2026 meeting and will be available on a first-come, first-served basis as an optional add-on during ticket booking. Spaces are limited.
Please note this programme is provisional and may be subject to change.
All timings are BST.
09:00 - 09:30: Registration and Welcome
09:30 - 10:00: Plenary Lecture 1: AFM & SPM Techniques
10:00 - 10:30: Plenary Lecture 2: Fundamentals of AFM & SPM Data Analysis
10:30 - 11:00: Break including refreshments
11:00 - 12:30: Parallel Session 1
- Hands-on AFM/SPM: Practical instrument operation (full instrument list TBC)
- Hands-on Scanning Ion Conductance Microscopy: Setup and Imaging
- Hands-on High-Speed AFM: Real-time molecular dynamics
- Data Analysis I: Imaging pre-processing
- Data Analysis II: Handling AFM/SPM data in Python
- Materials AFM/SPM Lectures: Methods, theory, tips, and tricks
- Bio AFM Lectures: Methods, theory, tips, and tricks
12:30 - 13:00: Lunch and Networking
13:00 - 15:00: Parallel Session 2
- Hands-on AFM/SPM: Practical instrument operation (full instrument list TBC)
- Hands-on Scanning Ion Conductance Microscopy: Setup and Imaging
- Hands-on High-Speed AFM: Real-time molecular dynamics
- Data Analysis I: Imaging pre-processing
- Data Analysis II: Handling AFM/SPM data in Python
- Data Analysis III: Machine learning in SPM
15:00 - 15:30: Break including refreshments
15:30 - 17:00: Parallel Session 3
- Hands-on AFM/SPM: Practical instrument operation (full instrument list TBC)
- Hands-on Scanning Ion Conductance Microscopy: Setup and Imaging
- Hands-on High-Speed AFM: Real-time molecular dynamics
- Data Analysis I: Imaging pre-processing
- Data Analysis II: Handling AFM/SPM data in Python
- Data Analysis III: Machine learning in SPM
17:00: Closing Remarks
Please accept {{cookieConsents}} cookies to view this content