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Here, we present a novel mechano-spectroscopic atomic force microscopy (AFM-MS) technique that overcomes the limitations of current spectroscopic methods by combining the high-resolution imaging capabilities of AFM with machine learning (ML) classification. AFM-MS employs AFM operating in sub-resonance tapping imaging mode, which enables the collection of multiple physical and mechanical property maps of a sample with sub-nanometer lateral resolution in a highly repeatable manner. By comparing these properties to a database of known materials, the technique identifies the location of constituent materials at each image pixel with the assistance of ML algorithms. We demonstrate AFM-MS on various material mixtures, achieving an unprecedented lateral spectroscopic resolution of 1.6 nm. This powerful approach opens new avenues for nanoscale material study, including the material identification and correlation of nanostructure with macroscopic material properties. The ability to map material composition with such high resolution will significantly advance the understanding and design of complex, nanostructured materials.
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Large-range high-speed dynamic-mode atomic force microscope imaging: adaptive tapping towards minimal force
Abstract This paper presents a software-hardware integrated approach to high-speed large-range dynamic mode imaging of atomic force microscope (AFM). High speed AFM imaging is needed to interrogate dynamic processes at nanoscale such as cellular interactions and polymer crystallization process. High-speed dynamic-modes such as tapping-mode AFM imaging is challenging as the probe tapping motion is sensitive to the highly nonlinear probe-sample interaction during the imaging process. The existing hardware-based approach via bandwidth enlargement, however, results in a substantially reduction of imaging area that can be covered. Contrarily, control (algorithm)-based approach, for example, the recently developed adaptive multiloop mode (AMLM) technique, has demonstrated its efficacy in increasing the tapping-mode imaging speed without loss of imaging size. Further improvement, however, has been limited by the hardware bandwidth and online signal processing speed and computation complexity.Thus, in this paper, the AMLM technique is further enhanced to optimize the probe tapping regulation and integrated with a field programmable gate array (FPGA) platform to further increase the imaging speed without loss of imaging quality and range. Experimental implementation of the proposed approach demonstrates that the high-quality imaging can be achieved at a high-speed scanning rate of 100 Hz and higher, and over a large imaging area of over 20 µm.
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- PAR ID:
- 10420373
- Date Published:
- Journal Name:
- Nanotechnology
- ISSN:
- 0957-4484
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1361-6528/acd700
