A quartz tuning fork (QTF) has been widely used as a force sensor of the frequency modulation atomic force microscope due to its ultrahigh stiffness, high quality factor and self-sensing nature. However, due to the bulky structure and exposed surface electrode arrangement, its application is limited, especially in liquid imaging of in situ biological samples, ionic liquids, electrochemical reaction, etc. Although the complication can be resolved by coating insulating materials on the QTF surface and then immersing the whole QTF into the liquid, it would result in a sharp drop of the quality factor, which will reduce the sensitivity of the QTF. To solve the problem, a novel method, called the balanced trolling quartz tuning fork (BT-QTF), is introduced here. In this method, two same probes are glued on both prongs of the QTF separately while only one probe immersed in the liquid. With the method, the hydrodynamic interaction can be reduced, thus the BT-QTF can retain a high quality factor and constant resonance frequency. The stable small vibration of the BT-QTF can be achieved in the liquid. Initially, a theoretical model is presented to analyze the sensing performance of the BT-QTF in the liquid. Then, the sensing performance analysis experiments of the BT-QTF have been performed. At last, the proposed method is applied to atomic force microscope imaging different samples in the liquid, which proves its feasibility.

A novel method based on Bayesian compressed sensing is proposed to remove impulse noise from atomic force microscopy (AFM) images. The image denoising problem is transformed into a compressed sensing imaging problem of the AFM. First, two different ways, including interval approach and self-comparison approach, are applied to identify the noisy pixels. An undersampled AFM image is generated by removing the noisy pixels from the image. Second, a series of measurement matrices, all of which are identity matrices with some rows removed, are constructed by recording the position of the noise-free pixels. Third, the Bayesian compressed sensing reconstruction algorithm is applied to recover the image. Different from traditional compressed sensing reconstruction methods in AFM, each row of the AFM image is reconstructed separately in the proposed method, which will not reduce the quality of the reconstructed image. The denoising experiments are conducted to demonstrate that the proposed method can remove the impulse noise from AFM images while preserving the details of the image. Compared with other methods, the proposed method is robust and its performance is not influenced by the noise density in a certain range.