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Register a new userBrownian motion in a non-homogeneous force field and photonic force microscope
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The Photonic Force Microscope (PFM) is an opto-mechanical technique based on an optical trap that can be assumed to probe forces in microscopic systems. This technique has been used to measure forces in the range of pico- and femto-Newton, assessing the mechanical properties of biomolecules as well as of other microscopic systems. For a correct use of the PFM, the force field to measure has to be invariable (homogeneous) on the scale of the Brownian motion of the trapped probe. This condition implicates that the force field must be conservative, excluding the possibility of a rotational component. However, there are cases where these assumptions are not fulfilled Here, we show how to improve the PFM technique in order to be able to deal with these cases. We introduce the theory of this enhanced PFM and we propose a concrete analysis workflow to reconstruct the force field from the experimental time-series of the probe position. Furthermore, we experimentally verify some particularly important cases, namely the case of a conservative or rotational force-field.
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Magnetic force microscopy (MFM) measurements generally provide phase images which represent the signature of domain structures on the surface of nanomaterials. To quantitatively determine magnetic stray fields based on an MFM image requires calibrated properties of the magnetic tip. In this work, an approach is presented for calibrating a magnetic tip using a Co/Pt multilayered film as a reference sample which shows stable well-known magnetic properties and well-defined perpendicular band domains. The approach is based on a regularized deconvolution process in Fourier domain with a Wiener filter and the L-curve method for determining a suitable regularization parameter to get a physically reasonable result. The calibrated tip is applied for a traceable quantitative determination of the stray fields of a test sample which has a patial frequency spectrum covered by that of the reference sample. According to the Guide to the expression of uncertainty in measurement, uncertainties of the processing algorithm are estimated considering the fact that the regularization influences significantly the quantitative analysis. We discuss relevant uncertainty components and their propagations between real domain and Fourier domain for both, the tip calibration procedure and the stray field calculation, and propose an uncertainty evaluation procedure for quantitative magnetic force microscopy.
This paper develops a resolution enhancement method for post-processing the images from Atomic Force Microscopy (AFM). This method is based on deep learning neural networks in the AFM topography measurements. In this study, a very deep convolution neural network is developed to derive the high-resolution topography image from the low-resolution topography image. The AFM measured images from various materials are tested in this study. The derived high-resolution AFM images are comparable with the experimental measured high-resolution images measured at the same locations. The results suggest that this method can be developed as a general post-processing method for AFM image analysis.
This report describes a cantilever controller for magnetic resonance force microscopy (MRFM) based on a field programmable gate array (FPGA), along with the hardware and software used to integrate the controller into an experiment. The controller is assembled from a low-cost commercially available software defined radio (SDR) device and libraries of open-source software. The controller includes a digital filter comprising two cascaded second-order sections (biquads), which together can implement transfer functions for optimal cantilever controllers. An appendix in this report shows how to calculate filter coefficients for an optimal controller from measured cantilever characteristics. The controller also includes an input multiplexer and adder used in calibration protocols. Filter coefficients and multiplexer settings can be set and adjusted by control software while an experiment is running. The input is sampled at 64 MHz; the sampling frequency in the filters can be divided down under software control to achieve a good match with filter characterisics. Data reported here were sampled at 500 kHz, chosen for acoustic cantilevers with resonant frequencies near 8 kHz. Inputs are digitized with 12 bits resolution, outputs with 14 bits. The experiment software is organized as a client and server to make it easy to adapt the controller to different experiments. The server encapusulates the details of controller hardware organization, connection technology, filter architecture, and number representation. The same server could be used in any experiment, while a different client encodes the particulars of each experiment.
A theory of additive Markov chains with long-range memory, proposed earlier in Phys. Rev. E 68, 06117 (2003), is developed and used to describe statistical properties of long-range correlated systems. The convenient characteristics of such systems, a memory function, and its relation to the correlation properties of the systems are examined. Various methods for finding the memory function via the correlation function are proposed. The inverse problem (calculation of the correlation function by means of the prescribed memory function) is also solved. This is demonstrated for the analytically solvable model of the system with a step-wise memory function.
In this activity, students will make a working model of an atomic force microscope (AFM). A permanent magnet attached to a compact disc (CD) strip acts as the sensor. The sensor is attached to a base made from Legos. Laser light is reflected from the CD sensor and onto a sheet of photosensitive paper. An array of permanent magnets attached to cardboard acts as the atoms on a surface. When the sensor is brought near this atomic surface the magnets will deflect the sensor, which in turn deflects the reflected laser. This deflection is recorded on the photosensitive paper, which students can take home with them.
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