Local raster scanning for high-speed imaging of biopolymers in atomic force microscopy

Upconversion optogenetics-driven biohybrid sensor for infrared sensing and imaging

Living organisms are far superior to state-of-the-art devices in visual perception as they have evolved a wide number of capabilities that encompass our most advanced technologies. By leveraging the performance of living organisms and directly interfacing them with artificial components, it can use the intricacy and metabolic efficiency of biological visual sensing within artificial machines. Inspired by the molecular basis (transient receptor potential, TRP) for infrared detection of pit-bearing organisms, we propose a TRP-like biohybrid sensor by integrating upconversion nanoparticles (UCNP) and optogenetically engineered cells on a graphene transistor for infrared sensing and imaging. The UCNP converts infrared light irradiation into blue light, the blue light activates the cells expressed with channelrhodopsin-2 (ChR2) and induces transmembrane photocurrent, and the photocurrent is detected by a biocompatible graphene transistor. Stepwise and overall experimental results show that, upon infrared light irradiation, the UCNP can rapidly mediate cellular photocurrents, which further translates into the extra output current of the graphene transistor. More notably, the response speed of the biohybrid sensor is 1∼3 orders of magnitude faster than those of TRPs heterologously expressed in cell lines in the literature, which confirms the response time advantage of the combination of UCNP and ChR2 within the sensor in place of TRPs. The biohybrid sensor can successfully image infrared targets, proving the feasibility of developing bionic infrared sensing devices by biohybrid integration of nonliving nanomaterials and biological components. This work opens up an avenue for biohybrid sensors to develop the bionic infrared vision that promisingly reproduces the functional superiority of natural organisms. STATEMENT OF SIGNIFICANCE: Infrared sensing and imaging have a wide range of military and civilian applications. Organisms have evolved excellent infrared vision with the molecular basis, transient receptor potential (TRP), and the performance is superior to existing state-of-the-art infrared devices. Inspired by this, a TRP-like biohybrid sensor based on upconversion optogenetics and a 2D material-based device is developed for infrared sensing and imaging. The biohybrid sensor has a relatively fast response speed that is 1∼3 orders of magnitude faster than that of the heterologously expressed TRPs, which enables its capability of infrared imaging with a single pixel-based method. This work broadens the spectrum of biohybrid sensing based on engineered cells to infrared, advancing the process of reproducing the excellent infrared detection of organisms.

Spectroscopic imaging of surfaces-Sum frequency generation microscopy (SFGM) combined with compressive sensing (CS) technique

Surface chemistry is notoriously difficult to study, in part, due to the decreased number of molecules that contribute to the properties compared to the bulk phase but often has significant effects on the chemical activity of the material. This is especially true in topics such as corrosion, catalysis, wetting, and many others in nature and industry. Sum frequency generation (SFG) spectroscopy was developed for interface studies due to its high molecular selectivity and surface sensitivity, which is quite useful to study the effects of structural inhomogeneity in microscopy. Compressive sensing (CS) combined with SFG spectroscopy minimizes the imaging time while still producing quality images. Selected systems are presented here to demonstrate the capability of CS-SFG microscopy. CS-SFG microscopy successfully distinguished the static monolayer molecular mixtures, the orientations and adsorption of adsorbed molecules by the dip-coating technique, and the localized CO behaviors on polycrystalline Pt electrodes. Further discussion includes dynamic imaging as a future direction in CS-SFG microscopy. As materials and surfaces become more complex, imaging with chemical contrast becomes indispensable to understanding their performance and CS-SFG microscopy seems highly beneficial in this respect.

Fast Specimen Boundary Tracking and Local Imaging with Scanning Probe Microscopy

An efficient and adaptive boundary tracking method is developed to confine area of interest for high-efficiency local scanning. By using a boundary point determination criterion, the scanning tip is steered with a sinusoidal waveform while estimating azimuth angle and radius ratio of each boundary point to accurately track the boundary of targets. A local scan region and path are subsequently planned based on the prior knowledge of boundary tracking to reduce the scan time. Boundary tracking and local scanning methods have great potential not only for fast dimension measurement but also for sample surface topography and physical characterization, with only scanning region of interest. The performance of the proposed methods was verified by using the alternate current mode scanning ion-conductance microscopy, tapping, and PeakForce modulation atomic force microscopy. Experimental results of single/multitarget boundary tracking and local scanning of target structures with complex boundaries demonstrate the flexibility and validity of the proposed method.

Reconstruction of atomic force microscopy image using compressed sensing

Atomic Force Microscopy (AFM) is one of the most popular and advanced tools for ultra high-resolution imaging and nanomanipulation of nano-scale matter. But AFM imaging typically takes a long time. High-speed and high-precision AFM measurement has attracted wide attention in recent several years. In traditional AFM, simple reduction in the number of measurement points may lose essential sample topography information. To resolve such problems, an AFM image reconstruction method based on Compressed Sensing (CS) theory is applied to reduce image acquisition time without cutting down the image quality. The benefit of using CS approach in AFM is shortening the imaging time, minimizing the interaction with the sample, and finally avoiding sample damage in AFM. Three kinds of testing samples with high and low frequency components were examined by a scanning electron microscope (SEM) and by AFM. An orthogonal Matching Pursuit (OMP) algorithm is employed to reconstruct an AFM image with different sampling rates. Subsequently the reconstruction results of sample topography images are analyzed and evaluated. Using the CS approach in AFM can greatly improve the AFM imaging process. Experimental results show that the obtained reconstructed images have different resolution and quality, depending on the surface morphology of the sample and sampling rates.

A comparison of reconstruction methods for undersampled atomic force microscopy images

Non-raster scanning and undersampling of atomic force microscopy (AFM) images is a technique for improving imaging rate and reducing the amount of tip-sample interaction needed to produce an image. Generation of the final image can be done using a variety of image processing techniques based on interpolation or optimization. The choice of reconstruction method has a large impact on the quality of the recovered image and the proper choice depends on the sample under study. In this work we compare interpolation through the use of inpainting algorithms with reconstruction based on optimization through the use of the basis pursuit algorithm commonly used for signal recovery in compressive sensing. Using four different sampling patterns found in non-raster AFM, namely row subsampling, spiral scanning, Lissajous scanning, and random scanning, we subsample data from existing images and compare reconstruction performance against the original image. The results illustrate that inpainting generally produces superior results when the image contains primarily low frequency content while basis pursuit is better when the images have mixed, but sparse, frequency content. Using support vector machines, we then classify images based on their frequency content and sparsity and, from this classification, develop a fast decision strategy to select a reconstruction algorithm to be used on subsampled data. The performance of the classification and decision test are demonstrated on test AFM images.

Note: Fast imaging of DNA in atomic force microscopy enabled by a local raster scan algorithm

Approaches to high-speed atomic force microscopy typically involve some combination of novel mechanical design to increase the physical bandwidth and advanced controllers to take maximum advantage of the physical capabilities. For certain classes of samples, however, imaging time can be reduced on standard instruments by reducing the amount of measurement that is performed to image the sample. One such technique is the local raster scan algorithm, developed for imaging of string-like samples. Here we provide experimental results on the use of this technique to image DNA samples, demonstrating the efficacy of the scheme and illustrating the order-of-magnitude improvement in imaging time that it provides.

High speed atomic force microscopy enabled by a sample profile estimator

In this paper, an estimation scheme for imaging in Atomic Force Microscopy (AFM) is presented which yields imaging rates well beyond the bandwidth of the vertical positioner and allows for high-speed AFM on a typical commercial instrument. The estimator can be applied to existing instruments with little to no hardware modification other than that needed to sample the cantilever signal. Experiments on a calibration sample as well as lambda DNA are performed to illustrate the effectiveness of this method. These show a greater than an order-of-magnitude improvement in the imaging rate.