Advanced Structural and Chemical Imaging最新文献

One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integrated into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.

Mathematics Subject Classification: (2010) 94A08 · 78A15

Electron tomographic reconstructions often contain artefacts from sources such as noise in the projections and a “missing wedge” of projection angles which can hamper quantitative analysis. We present a machine-learning approach using freely available software for analysing imperfect reconstructions to be used in place of the more traditional thresholding based on grey-level technique and show that a properly trained image classifier can achieve manual levels of accuracy even on heavily artefacted data, though if multiple reconstructions are being processed, a separate classifier will need to be trained on each reconstruction for maximum accuracy.

Many microscopic investigations of materials may benefit from the recording of multiple successive images. This can include techniques common to several types of microscopy such as frame averaging to improve signal-to-noise ratios (SNR) or time series to study dynamic processes or more specific applications. In the scanning transmission electron microscope, this might include focal series for optical sectioning or aberration measurement, beam damage studies or camera-length series to study the effects of strain; whilst in the scanning tunnelling microscope, this might include bias-voltage series to probe local electronic structure. Whatever the application, such investigations must begin with the careful alignment of these data stacks, an operation that is not always trivial. In addition, the presence of low-frequency scanning distortions can introduce intra-image shifts to the data. Here, we describe an improved automated method of performing non-rigid registration customised for the challenges unique to scanned microscope data specifically addressing the issues of low-SNR data, images containing a large proportion of crystalline material and/or local features of interest such as dislocations or edges. Careful attention has been paid to artefact testing of the non-rigid registration method used, and the importance of this registration for the quantitative interpretation of feature intensities and positions is evaluated.

Multi-dimensional electron microscopy has recently gained considerable interest thanks to the advent of microscopes with unprecedented analytical and in situ capabilities. These information-rich imaging modes, though, are often subject to long acquisition times and large data generation. In this paper, we explore novel acquisition strategies and reconstruction algorithms to retrieve reliable reconstructions from datasets that are limited in terms of both per image and tilt series angular sampling. We show that inpainting techniques are capable of restoring scanning transmission electron microscopy images in which a very restricted number of pixels are scanned, while compressed sensing tomographic reconstruction is capable of minimising artefacts due to angular subsampling. An example of robust reconstruction from data constituting a dose reduction of 10× is presented, using an organic/inorganic core-shell nanowire as a test sample. The combination of these novel acquisition schemes and image recovery strategies provides new avenues to reduced-dose and high-speed imaging.

The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.

Electron tomography is widely used for nanoscale determination of 3-D structures in many areas of science. Determining the 3-D structure of a sample from electron tomography involves three major steps: acquisition of sequence of 2-D projection images of the sample with the electron microscope, alignment of the images to a common coordinate system, and 3-D reconstruction and segmentation of the sample from the aligned image data. The resolution of the 3-D reconstruction is directly influenced by the accuracy of the alignment, and therefore, it is crucial to have a robust and dependable alignment method. In this paper, we develop a new alignment method which avoids the use of markers and instead traces the computed paths of many identifiable ‘local’ center-of-mass points as the sample is rotated. Compared with traditional correlation schemes, the alignment method presented here is resistant to cumulative error observed from correlation techniques, has very rigorous mathematical justification, and is very robust since many points and paths are used, all of which inevitably improves the quality of the reconstruction and confidence in the scientific results.

Scanning transmission electron microscopy (STEM) provides sub-?ngstrom, atomic resolution images of crystalline structures. However, in many applications, the ability to extract information such as atom positions, from such electron micrographs, is severely obstructed by low signal-to-noise ratios of the acquired images resulting from necessary limitations to the electron dose. We present a denoising strategy tailored to the special features of atomic-resolution electron micrographs of crystals limited by Poisson noise based on the block-matching and 3D-filtering (BM3D) algorithm by Dabov et al. We also present an economized block-matching strategy that exploits the periodic structure of the observed crystals. On simulated single-shot STEM images of inorganic materials, with incident electron doses below 4 C/cm ², our new method achieves precisions of 7 to 15 pm and an increase in peak signal-to-noise ratio (PSNR) of 15 to 20 dB compared to noisy images and 2 to 4 dB compared to images denoised with the original BM3D.

Determining the precise atomic structure of materials’ surfaces, defects, and interfaces is important to help provide the connection between structure and important materials’ properties. Modern scanning transmission electron microscopy (STEM) techniques now allow for atomic resolution STEM images to have down to sub-picometer precision in locating positions of atoms, but these high-precision techniques generally require large electron doses, making them less useful for beam-sensitive materials. Here, we show that 1- to 2-pm image precision is possible by non-rigidly registering and averaging a high-angle dark field image series of a 5- to 6-nm Au nanoparticle even though a very coarsely sampled image and decreased exposure time was used to minimize the electron dose. These imaging conditions minimize the damage to the nanoparticle and capture the whole nanoparticle in the same image. The high-precision STEM image reveals bond length contraction around the entire nanoparticle surface, and no bond length variation along a twin boundary that separates the nanoparticle into two grains. Surface atoms at the edges and corners exhibit larger bond length contraction than atoms near the center of surface facets.

Understanding the fundamental processes taking place at the electrode-electrolyte interface in batteries will play a key role in the development of next generation energy storage technologies. One of the most fundamental aspects of the electrode-electrolyte interface is the electrical double layer (EDL). Given the recent development of high spatial resolution in-situ electrochemical fluid cells for scanning transmission electron microscopy (STEM), there now exists the possibility that we can directly observe the formation and dynamics of the EDL. In this paper we predict electrolyte structure within the EDL using classical models and atomistic Molecular Dynamics (MD) simulations. Classical models are found to greatly differ from MD in predicted concentration profiles. It is thus suggested that MD must be used in order to accurately predict STEM images of the electrode-electrolyte interface. Using MD and image simulation together for a high contrast electrolyte (the high atomic number CsCl electrolyte), it is determined that, for a smooth interface, concentration profiles within the EDL should be visible experimentally. When normal experimental parameters such as rough interfaces and low-Z electrolytes (like those used in Li-ion batteries) are considered, observation of the EDL appears to be more difficult.

The diffract-before-destroy method, using 50- to 100-fs x-ray pulses from a free-electron laser, was designed to determine the three-dimensional structure of biological macromolecules in close to their natural state. Here we explore the possibility of using short electron pulses for the same purpose and the related question of whether radiation damage can be outrun with electrons. Major problems include Coulomb repulsion within the incident beam and the need for high lateral coherence, difficulties that are discussed in terms of existing and future electron sources. Using longer pulses of electrons appears to make the attainment of near-atomic resolution more feasible, at least for nanocrystalline particles, whereas obtaining this information from single-molecule particles in an aqueous environment seems a more distant goal. We also consider the possibility of serial crystallography using a liquid jet injector with a continuous electron beam in a transmission electron microscope (TEM).