ﻻ يوجد ملخص باللغة العربية
While Atomic Force Microscopy is mostly used to investigate surface properties, people have almost since its invention sought to apply its high resolution capability to image also structures buried within samples. One of the earliest techniques for this was based on using ultrasound excitations to visualize local differences in effective tip-sample stiffness caused by the presence of buried structures with different visco-elasticity from their surroundings. While the use of ultrasound has often triggered discussions on the contribution of diffraction or scattering of acoustic waves in visualizing buried structures, no conclusive papers on this topic have been published. Here we demonstrate and discuss how such acoustical effects can be unambiguously recognized and can be used with Atomic Force Microscopy to visualize deeply buried structures.
We use an atomic vapor cell as a frequency tunable microwave field detector operating at frequencies from GHz to tens of GHz. We detect microwave magnetic fields from 2.3 GHz to 26.4 GHz, and measure the amplitude of the sigma+ component of an 18 GHz
Ultrasound Atomic Force Microscopy (US-AFM) has been used for subsurface imaging of nanostructures. The contact stiffness variations have been suggested as the origin of the image contrast. Therefore, to analyze the image contrast, the local changes
High-frequency atomic force microscopy has enabled extraordinary new science through large bandwidth, high speed measurements of atomic and molecular structures. However, traditional optical detection schemes restrict the dimensions, and therefore th
Morphological features of small vessels provide invaluable information regarding underlying tissue, especially in cancerous tumors. This paper introduces methods for obtaining quantitative morphological features from microvasculature images obtained
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