Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOn modeling and measuring viscoelasticity with dynamic Atomic Force Microscopy
86
0
0.0
(
0
)
Added by
Per-Anders Thor\\'en
Publication date
2017
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The interaction between a rapidly oscillating atomic force microscope tip and a soft material surface is described using both elastic and viscous forces with a moving surface model. We derive the simplest form of this model, motivating it as a way to capture the impact dynamics of the tip and sample with an interaction consisting of two components: interfacial or surface force, and bulk or volumetric force. Analytic solutions to the piece-wise linear model identify characteristic time constants, providing a physical explanation of the hysteresis observed in the measured dynamic force quadrature curves. Numerical simulation is used to fit the model to experimental data and excellent agreement is found with a variety of different samples. The model parameters form a dimensionless impact-rheology factor, giving a quantitative physical number to characterize a viscoelastic surface that does not depend on the tip shape or cantilever frequency.
rate research
Read More
We report the development of a scanning force microscope based on an ultra-sensitive silicon nitride membrane transducer. Our development is made possible by inverting the standard microscope geometry - in our instrument, the substrate is vibrating and the scanning tip is at rest. We present first topography images of samples placed on the membrane surface. Our measurements demonstrate that the membrane retains an excellent force sensitivity when loaded with samples and in the presence of a scanning tip. We discuss the prospects and limitations of our instrument as a quantum-limited force sensor and imaging tool.
Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules, due to difficulties with interpretation of highly distorted AFM images originating from non-planar molecules. Here we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to apply high-resolution AFM to a large variety of systems for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.
While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic characterization at the same scale. In this work, we introduce Electrostatic Discovery Atomic Force Microscopy, a machine learning based method which provides immediate quantitative maps of the electrostatic potential directly from Atomic Force Microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach opens the door to reliable atomic scale electrostatic maps on any system with minimal computational overhead.
Graphene oxide can be used as a precursor to graphene but the quality of graphene flakes is highly heterogeneous. Scanning-Raman-Microscopy (SRM) is used to characterize films of graphene derived from flakes of graphene oxide with an almost intact carbon framework (ai-GO). The defect density of these flakes is visualized in detail by analyzing the intensity and full-width at half-maximum of the most pronounced Raman peaks. In addition, we superimpose the SRM results with AFM images and correlate the spectroscopic results with the morphology. Furthermore, we use SRM technique to display the amount of defects in a film of graphene. Thus, an area of 250 x 250 {my}m2 of graphene is probed with a step-size increment of 1 {mu}m. We are able to visualize the position of graphene flakes, edges and the substrate. Finally, we alter parameters of measurement to analyze the quality of graphene fast and reliable. The described method can be used to probe and visualize the quality of graphene films.
Magnetic resonance force microscopy (MRFM) is a scanning probe technique capable of detecting MRI signals from nanoscale sample volumes, providing a paradigm-changing potential for structural biology and medical research. Thus far, however, experiments have not reached suffcient spatial resolution for retrieving meaningful structural information from samples. In this work, we report MRFM imaging scans demonstrating a resolution of 0.9 nm and a localization precision of 0.6 nm in one dimension. Our progress is enabled by an improved spin excitation protocol furnishing us with sharp spatial control on the MRFM imaging slice, combined with overall advances in instrument stability. From a modeling of the slice function, we expect that our arrangement supports spatial resolutions down to 0.3 nm given suffcient signal-to-noise ratio. Our experiment demonstrates the feasibility of sub-nanometer MRI and realizes an important milestone towards the three-dimensional imaging of macromolecular structures.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
