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

Adaptive under-sampling strategy for fast imaging in compressive sensing-based atomic force microscopy

Compressive sensing (CS) can reconstruct the rest information almost without distortion by advanced computational algorithm, which significantly simplifies the process of atomic force microscope (AFM) scanning with high imaging quality. In common CS-AFM, the partial measurements randomly come from the whole region to be measured, which easily leads to detail loss and poor image quality in regions of interest (ROIs). Consequently, important microscopic phenomena are missed probably. In this paper, we developed an adaptive under-sampling strategy for CS-AFM to optimize the process of sampling. Under a certain under-sampling ratio, the weight coefficient of ROIs and regions of base (ROBs) were set to control the distribution of under-sampling points and corresponding measurement matrix. A series of simulations were completed to demonstrate the relationship between the weight coefficient of ROIs and image quality. After that, we verified the effectiveness of the method on our homemade AFM. Through a lot of simulations and experiments, we demonstrated how the proposed method optimized the sampling process of CS-AFM, which speeded up the process of AFM imaging with high quality.

High-quality AFM image acquisition of living cells by modified residual encoder-decoder network

Atomic force microscope enables ultra-precision imaging of living cells. However, atomic force microscope imaging is a complex and time-consuming process. The obtained images of living cells usually have low resolution and are easily influenced by noise leading to unsatisfactory imaging quality, obstructing the research and analysis based on cell images. Herein, an adaptive attention image reconstruction network based on residual encoder-decoder was proposed, through the combination of deep learning technology and atomic force microscope imaging supporting high-quality cell image acquisition. Compared with other learning-based methods, the proposed network showed higher peak signal-to-noise ratio, higher structural similarity and better image reconstruction performances. In addition, the cell images reconstructed by each method were used for cell recognition, and the cell images reconstructed by the proposed network had the highest cell recognition rate. The proposed network has brought insights into the atomic force microscope-based imaging of living cells and cell image reconstruction, which is of great significance in biological and medical research.

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.

High-speed multiparametric imaging through off-resonance tapping AFM with active probe

Off-resonance tapping (ORT) mode of atomic force microscopy (AFM), based on force-distance curve, is widely concerned due to its advantages of weak tip-sample interaction and concurrent quantitative property mapping. However, the ORT-AFM still has the disadvantage of slow scan speed caused by low modulation frequency. In this paper, we overcome this disadvantage by introducing active probe method. With active probe, the cantilever was directly actuated with the induced strain after applying voltage in the piezoceramic film. In this way, the modulation frequency could be increased to more than an order of magnitude faster than that of traditional ORT, thus improving the scan rate. We demonstrated high-speed multiparametric imaging with the active probe method in ORT-AFM.

Accelerating AFM Characterization via Deep-Learning-Based Image Super-Resolution

Atomic force microscopy (AFM) is one of the most popular imaging and characterizing methods applicable to a wide range of nanoscale material systems. However, high-resolution imaging using AFM generally suffers from a low scanning yield due to its method of raster scanning. Here, a systematic method of data acquisition and preparation combined with a deep-learning-based image super-resolution, enabling rapid AFM characterization with accuracy, is proposed. Its application to measuring the geometrical and mechanical properties of structured DNA assemblies reveals that around a tenfold reduction in AFM imaging time can be achieved without significant loss of accuracy. Through a transfer learning strategy, it can be efficiently customized for a specific target sample on demand.

Reducing molecular simulation time for AFM images based on super-resolution methods

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.