Effect of Interlayer Coupling on Ultrafast Charge Transfer from Semiconducting Molecules to Mono- and Bilayer Graphene

Graphene is used as flexible electrodes in various optoelectronic devices. In these applications, ultrafast charge transfer from semiconducting light absorbers to graphene can impact the overall device performance. Here, we propose a mechanism in which the charge-transfer rate can be controlled by varying the number of graphene layers and their stacking. Using an organic semiconducting molecule as a light absorber, the charge-transfer rate to graphene is measured by using time-resolved photoemission spectroscopy. Compared to graphite, the charge transfer to monolayer graphene is about 2 times slower. Surprisingly, the charge transfer to A−B–stacked bilayer graphene is slower than that to both monolayer graphene and graphite. This anomalous behavior disappears when the two graphene layers are randomly stacked. The observation is explained by a charge-transfer model that accounts for the band-structure difference in mono- and bilayer graphene, which predicts that the charge-transfer rate depends nonintuitively on both the layer number and stacking of graphene

Application Of Second-Order Histograms In Noise Reduction And Contrast Measurement Of Intensity Images

This research focuses on a type of information provided by the pixels in intensity images. The types of images that are the focus of this research are scanning electron microscopy (SEM) and magnetic resonance imaging (MRI) images. These two types of images were chosen for the amount of details that they can present using intensity values. In turn, the details that they provide lead to the use of the secondorder derivatives of the values of pixels. The second-order derivatives provide further information about the images, specifically their levels of noise and contrast. The second-order derivatives are obtained from applying a Laplacian operator on the pixels in the image. The second-order derivatives of the pixels can be collected into histograms, which are henceforth referred to as “second-order histograms”. These histograms have shape profiles with certain characteristics that are common to all images with adequate contrast and low levels of noise, e.g. profiles that approximately resemble Laplace distribution profiles. The main theory in this research is that any deviation from these presence is an indicator of the presence of factors that affect the quality of an image. One of the characteristics of the Laplace distribution profile is the convergence of two concave and non-contiguous curves into a sharp peak. The peak contains data on image details that have uniform intensity, such as regions of the same material in SEM images. Therefore, the absence of the peak in the second-order histogram of an image indicates the presence of factors that affect those details, such as noise. This is the idea behind a method to reduce noise through the restoration of the Laplace distribution profile by changing the pixel values of an image

Application of optical character recognition in the processing in the processing of thermal images

The present-day reliance on metadata in obtaining information from thermal images means that in the absence of metadata, estimating temperatures from thermal images would have to be done with user intervention, which is time-consuming when there are many thermal images to consider. This research intends to develop a program to estimate temperatures from thermal images without metadata, so that a wider range of sources of thermal images, such as scans from documents, can be handled in large numbers. To facilitate this research, thermal images of various subjects are used as sample images to test the program. A method to estimate temperatures from thermal images without metadata and without the need for manual input has been developed, by making use of optical character recognition to have the program automatically read the temperature scales on thermal images for the necessary information. This method utilizes correlation coefficients to compare images of objects with images of known characters. There were problems that arose from the small text in thermal images captured using present-day commercial thermal imagers, and solutions have been devised for these to ensure that the program is accurate and persistent

Conditional Noise Filter for MRI Images with Revised Theory on Second-order Histograms

Previous research by the author has the theory that histograms of second-order derivatives are capable of determining differences between pixels in MRI images for the purpose of noise reduction without having to refer to ground truth. However, the methodology of the previous research resulted in significant false negatives in determining which pixel is affected by noise. The theory has been revised in this article through the introduction of an additional Laplace curve, leading to comparisons between the histogram profile and two curves instead of just one. The revised theory is that differences between the first curve and the histogram profile and the differences between the second curve and the profile can determine which pixels are to be selected for filtering in order to improve image clarity while minimizing blurring. The revised theory is tested with a modified average filter versus a generic average filter, with PSNR and SSIM for scoring. The results show that for most of the sample MRI images, the theory of pixel selection is more reliable at higher levels of noise but not as reliable at preventing blurring at low levels of noise

Termination Factor for Iterative Noise Reduction in MRI Images Using Histograms of Second-order Derivatives

Histograms of second-order derivatives are generated from the pixel data of MRI images. The histograms are then used to calculate a factor that is to be used for iterative processing. The factor is intended to limit the number of iterations, with the goal of preventing further loss of detail. The factor uses two conditions that depend on the profiles of the histograms. The methodology uses sample MRI images and versions of these images with Rician noise introduced into them. The noisy images are subjected to iterative noise reduction with a recursive averaging filter. The control tests in the methodology use the ground truth images to limit the number of iterations, with PSNR and SSIM peaks used as the measurements for determining when the iterations stop. The other tests use the proposed termination factor for the limitation. The results of the tests are compared to determine the effectiveness of the termination factor. The proposed termination factor does not cause divergence, but there are still different numbers of iterations in the case of MRI images with image subjects that have discrete regions and details resembling noise. The tests also reveal that differences between the histograms of derivatives and Laplace curves have to be retained in order to prevent loss of information

Experimental method to pre-process fuzzy bit planes before low-level feature extraction in thermal images

Noise affects the values of pixels in a thermal image. As the values of pixels will determine the appearance of the bit planes of thermal images, noise will affect the bit planes as well. Noisy thermal images have bit planes which resembles static. These bit planes will not be useful for the purpose of extracting low-level details, e.g. detecting edges and blobs. The method described in this article is intended to be utilized as a pre-processing step to identify these bit planes before any further processing steps which make use of bit planes take place

Dynamical Localization Limiting the Coherent Transport Range of Excitons in Organic Crystals

Exciton or energy transport in organic crystals is commonly described by a series of incoherent hoppings. This picture is no longer valid if the transport range is on the order of the exciton coherent (or delocalization) size. However, coherent effects are often neglected because the exciton wave function generally localizes to a few molecules within an ultrafast time scale (<1 ps) after photoexcitation. Here, by using time-resolved photoemission spectroscopy and nanometer-thick zinc phthalocyanine crystals, we are able to observe a transition from the coherent to incoherent transport regime while the exciton coherent size is decreasing as a function of time. During the transition, a distinct phonon mode is excited, which suggests that the electron–vibrational interaction localizes the exciton and reduces its coherent size. It is anticipated that the coherent transport range can be increased by controlling the electron–vibrational coupling. An enhanced coherent transport range can be advantageous in applications such as organic photovoltaics

Pixel Filtering and Reallocation with Histograms of Second-Order Derivatives of Pixel Values for Electron Microscope Images

The proposed method uses second-order derivatives to derive more information from the gray values of the pixels in a scanning electron microscope image. It represents this information as a histogram of second-order values, which expresses the variation in pixel values caused by image details and noise. The method uses the histogram as the basis for the targeting of pixels for filtering and reallocation to restore the image. The method controls the number of target pixels to minimize blurring of edges by imposing a Laplace curve on the histogram and using it together with other equations to select pixels based on the differences between their second-order derivatives and those of their neighbour