Adaptive-scanning, near-minimum-deformation atomic force microscope imaging of soft sample in liquid: Live mammalian cell example

Non-contact non-resonant atomic force microscopy method for measurements of highly mobile molecules and nanoparticles

Atomic force microscopy (AFM) is nowadays indispensable versatile scanning probe method widely employed for fundamental and applied research in physics, chemistry, biology as well as industrial metrology. Conventional AFM systems can operate in various environments such as ultra-high vacuum, electrolyte solutions, or controlled gas atmosphere. Measurements in ambient air are prevalent due to their technical simplicity; however, there are drawbacks such as formation of water meniscus that greatly increases attractive interaction (adhesion) between the tip and the sample, reduced spatial resolution, and too strong interactions leading to tip and/or sample modifications. Here we show how the attractive forces in AFM under ambient conditions can be used with advantage to probe surface properties in a very sensitive way even on highly mobile molecules and nanoparticles. We introduce a stable non-contact non-resonant (NCNR) AFM method which enables to reliably perform measurements in the attractive force regime even in air by controlling the tip position in the intimate surface vicinity without touching it. We demonstrate proof-of-concept results on helicene-based macrocycles, DNA on mica, and nanodiamonds on SiO2. We compare the results with other conventional AFM regimes, showing NCNR advantages such as higher spatial resolution, reduced tip contamination, and negligible sample modification. We analyze principle physical and chemical mechanisms influencing the measurements, discuss issues of stability and various possible method implementations. We explain how the NCNR method can be applied in any AFM system by a mere software modification. The method thus opens a new research field for measurements of highly sensitive and mobile nanoscale objects under air and other environments.

Data-driven dynamics-based optimal filtering of acoustic noise at arbitrary location in atomic force microscope imaging

This paper presents a post-filtering approach to eliminate distortions in atomic force microscope (AFM) images caused by acoustic noise from an unknown location. AFM operations are sensitive to external disturbances including acoustic noise, as disturbances to the probe-sample interaction directly results in distortions in the sample images obtained. Although conventional passive noise cancellation has been employed, limitation exists and residual noise still persists. Advanced online control techniques face difficulty in capturing the complex noise dynamic and limited system bandwidth imposed by robustness requirement. In this work, we propose a dynamics-based optimal filtering technique to remove the acoustic-caused distortions in AFM images. A dictionary-approach is integrated with time-delay measurement to localize the noise source and estimate the corresponding acoustic dynamics. Then a noise-to-image coherence minimization approach is proposed to minimize the acoustic-caused image distortion via a gradient-based optimization to seek an optimal modulator to the acoustic dynamics. Finally, the filter is obtained as the finite-impulse response of the optimized acoustic dynamics. Experimental implementation is presented and discussed to illustrate the proposed technique.

An automated vertical drift correction algorithm for AFM images based on morphology prediction

The atomic force microscope (AFM) has become a powerful tool in many fields. However, environmental noise and other disturbances are very likely to cause the AFM probe to vibrate, which lead to vertical drift in AFM imaging and limit its further application. Therefore, to correct image distortion caused by vertical drift, a morphology prediction based image correction algorithm is proposed in this paper. Specifically, a Gaussian-Hann filter is first designed for distorted AFM images, based on which, an adaptive image binarization algorithm is developed to achieve accurate object detection and background extraction. Furthermore, an advanced morphology prediction algorithm, consisting of morphological approximation prediction and morphological detail prediction, is proposed to correct image distortion by using the extracted substrate of a sample image. Approximate morphology is generated by an improved weighted fusion autoregressive model, and morphological detail is obtained by energy analysis based on discrete wavelet transform. Experimental and application results are presented to illustrate that the proposed algorithm is able to effectively eliminate vertical drift of AFM images.

A high-speed atomic force microscopy with super resolution based on path planning scanning

An atomic force microscopy generally adopts a raster scanning method to obtain the image of the sample morphology. However, the raster method takes too much time on the base part without focusing enough on the object, thereby restricting the scanning speed of an AFM. To solve this problem, this paper proposes a novel path planning based scanning method to achieve high-speed scanning with super resolution for AFMs. Specifically speaking, a fast scanning process is first carried out to generate a low-resolution image with less time, then a convolutional neural network is designed to construct a super-resolution image based on the fast scanning image. Afterwards, an advanced detection algorithm is proposed to achieve the accurate object detection and localization. Furthermore, an improved ant colony optimization algorithm is proposed to realize the path planning for scanning the objects with high quality, whose imaging result is then matched with the previous super-resolution image to construct the entire sample image, thus achieving fast scanning with super resolution. Experimental and application results demonstrate the good performance of the proposed scanning method.

Active control of acoustics-caused nano-vibration in atomic force microscope imaging

In this paper, we propose a finite-impulse-response (FIR)-based feedforward control approach to mitigate the acoustic-caused probe vibration during atomic force microscope (AFM) imaging. Compensation for the acoustic-caused probe vibration is important, as environmental disturbances including acoustic noise induce nano-scale probe vibration, directly affecting the AFM performance in applications such as imaging, nanomechanical characterization, and nanomanipulation. Although conventional passive noise cancellation apparatus has been employed, limitation exists, and residual noise still persists. Thus, a FIR-based active feedforward control approach is developed, by exploring a data-driven approach to account for the vibrational dynamics of the probe caused by the environmental acoustic noise in the controller design. An experimental implementation in AFM imaging application is presented and discussed to illustrate the proposed technique. Experimental results show that the FIR-based feedforward control is promising to not only complement, but also alleviate the limitations of passive noise control in AFM operations.