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The Cypher VRS1250 is twice as fast as the first-generation Cypher VRS, scanning at up to 1250 lines/second. This enables either higher temporal resolution with frame rates up to 45 frames/second or increased spatial resolution by collecting more image pixels at lower frame rates. Combined with ultra-stable imaging, an easy-to-use fully sealed sample cell with perfusion capability, and a full range of modes and accessories, the Cypher VRS1250 is ready to help you achieve your most ambitious research goals.
Designed for both ultra-high speed and ultra-high resolution
Stable imaging allows dynamic events to be captured without drift or parameter adjustments
Ease of use features make setup and imaging simple
Includes all the added flexibility and capabilities of the Cypher ES research AFM
Contact mode
DART PFM
Dual AC
Dual AC Resonance Tracking (DART)
Electrostatic Force Microscopy (EFM)
Force curves
Force Mapping Mode (force volume)
Force modulation
Frequency modulation
Kelvin Probe Force Microscopy (KPFM)
Lateral Force Mode (LFM)
Loss tangent imaging
Magnetic Force Microscopy (MFM)
Nanolithography
Nanomanipulation
Phase imaging
Piezoresponse Force Microscopy (PFM)
Switching spectroscopy PFM
Tapping mode (AC mode)
Tapping mode with digital Q control
Vector PFM
AM-FM Viscoelastic Mapping Mode
Contact Resonance Viscoelastic Mapping Mode
Conductive AFM (CAFM) with ORCA™ and Eclipse™ Mode
Current mapping with Fast Force Mapping
Electrochemical Strain Microscopy (ESM)
Fast Force Mapping Mode
High voltage PFM
Nanoscale Time Dependent Dielectric Breakdown (nanoTDDB)
Scanning Capacitance Microscopy (SCM)
Scanning Microwave Impedance Microscopy (sMIM)
Scanning Tunneling Microscopy (STM)
Kalinin, S. V., Zhang, S., Valleti, M., Pyles, H., Baker, D., De Yoreo, J. J., & Ziatdinov, M. (2021). Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations. ACS nano. https://doi.org/10.1021/acsnano.0c08914
Ziatdinov, M., Zhang, S., Dollar, O., Pfaendtner, J., Mundy, C. J., Li, X., ... & Kalinin, S. V. (2020). Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data. Nano Letters, 21(1), 158-165. https://dx.doi.org/10.1021/acs.nanolett.0c03447
Chen, J., Zhu, E., Liu, J., Zhang, S., Lin, Z., Duan, X., ... & De Yoreo, J. J. (2018). Building two-dimensional materials one row at a time: Avoiding the nucleation barrier. Science, 362(6419), 1135-1139. https://doi.org/10.1126/science.aau4146
Mao, X., Li, K., Liu, M., Wang, X., Zhao, T., An, B., ... & Wang, L. (2019). Directing curli polymerization with DNA origami nucleators. Nature communications, 10(1), 1-10. https://doi.org/10.1038/s41467-019-09369-6
Sigdel, K. P., Wilt, L. A., Marsh, B. P., Roberts, A. G., & King, G. M. (2018). The conformation and dynamics of P-glycoprotein in a lipid bilayer investigated by atomic force microscopy. Biochemical pharmacology, 156, 302-311. https://doi.org/10.1016/j.bcp.2018.08.017
Zhu, H., Wang, X., Cui, Y., Cai, J., Tian, F., Wang, J., & Qiu, H. (2019). Blooming of Block Copolymer Micelles into Complex Nanostructures on a Surface. Macromolecules, 52(9), 3479-3485. https://doi.org/10.1021/acs.macromol.9b00197