Methods in carbon K-edge NEXAFS: Experiment and analysis

Near-edge X-ray absorption spectroscopy (NEXAFS) is widely used to probe the chemistry and structure of surface layers. Moreover, using ultra-high brilliance polarised synchrotron light sources, it is possible to determine the molecular alignment of ultra-thin surface films. However, the quantitative analysis of NEXAFS data is complicated by many experimental factors and, historically, the essential methods of calibration, normalisation and artefact removal are presented in the literature in a somewhat fragmented manner, thus hindering their integrated implementation as well as their further development. This paper outlines a unified, systematic approach to the collection and quantitative analysis of NEXAFS data with a particular focus upon carbon K-edge spectra. As a consequence, we show that current methods neglect several important aspects of the data analysis process, which we address with a combination of novel and adapted techniques. We discuss multiple approaches in solving the issues commonly encountered in the analysis of NEXAFS data, revealing the inherent assumptions of each approach and providing guidelines for assessing their appropriateness in a broad range of experimental situations.     

Normal Coordinate Analysis of Pyridine-N-Oxide

We have been interested for some time in the study of the hydrogen bonded complexes in the far-infrared region with special attention to the adducts between phenol and pyridine-N-oxides (1). In such systems the intermolecular mode vσ was identified and some intramolecular vibrations related to the N-O group were observed to be remarkably shifted consequent on hydrogen bonding. In this context we wished to obtain a force field for the complex which could fit the observational results. Unfortunately for one of the constituents i. e. pyridine-N-oxide a force field, suitable for our purposes, was not available and therefore preliminarily we determined it. We report here the results of such analysis.     

Salient region detection using background priors

In this paper, we introduce a novel salient region detection algorithm by using background priors. Because of the fact that superpixel is perceptually more meaningful than pixel, and which can reduce the complexity of image processing, we use the superpixel algorithm to reprocess original images. In addition, we hold the point that the colors in the image boundary could mainly represent all background colors, hence we compute the color contrast between the intern colors and the boundary colors. Since the nearer the patches are close to center, the more they affect other patches, we propose a new distribution-based model. Finally, experimental results demonstrate that the proposed method outperforms the state-of-the-art approaches.     

A combined approach of Kullback–Leibler divergence and background subtraction for moving object detection in thermal video

In this work, a robust method for moving object detection in thermal video frames has been proposed by including Kullback–Leibler divergence (KLD) based threshold and background subtraction (BGS) technique. A trimmed-mean based background model has been developed that is capable enough to reduce noise or dynamic component of the background. This work assumed that each pixel has normally distributed. The KLD has computed between background pixel and a current pixel with the help of Gaussian mixture model. The proposed threshold is useful enough to classify the state of each pixel. The post-processing step uses morphological tool for edge linking, and then the flood-fill algorithm has applied for hole-filling, and finally the silhouette of targeted object has generated. The proposed methods run faster and have validated over various real-time based problematic thermal video sequences. In the experimental results, the average value of F₁-score, area under the curve, the percentage of correct classification, Matthew’s correlation coefficient show higher values whereas total error and percentage of the wrong classification show minimum values. Moreover, the proposed-1 method achieved higher accuracy and execution speed with minimum false alarm rate that has been compared with proposed-2 as well as considered peer methods in the real-time thermal video.     

Separable convolution template (SCT) background prediction accelerated by CUDA for infrared small target detection

This paper presents a novel background prediction method for infrared small target detection (ISTD). Using a separable convolution template (SCT) to accelerate the traditional background prediction by graphic processing unit (GPU), the new method provides a significant improvement in the prediction speed, which enables the prediction process in real time. And experimental results show its high efficiency and practical application over previous work. The mathematical approach proposed here could be extended to accelerate the applications referred to image convolutions not only to the infrared field.     

Determination of pore space shape and size in porous systems using NMR diffusometry. Beyond the short gradient pulse approximation

The influence of finite length gradient pulses on NMR diffusion experiments on liquids confined to diffuse between two parallel planes is investigated. It is experimentally verified that the pore size decreases when determined using finite gradient pulses if the results are analyzed within the short gradient pulse approximation. The results are analyzed using the matrix formulation. The observed minima in the echo decay profiles are considerably less sharp than theoretical analysis would indicate and we suggest that this is due to the presence of a distribution of pore sizes in the sample. In addition, effects due to the presence of background gradients are discussed. It is argued that effects due to the finite length gradient pulses are relatively minor and in realistic applications the effects due to inhomogeneities in pore sizes and effects due to background gradients will constitute more serious problems in pore size determinations by means of NMR diffusometry.     

First nucleation steps during deposition of SiO2 thin films by plasma enhanced chemical vapour deposition

The initial nucleation stages during deposition of SiO₂ by remote plasma enhanced chemical vapour deposition (PECVD) have been monitored by XPS inelastic peak shape analysis. Experiments have been carried out on two substrates, a flat ZrO₂ thin film and a silicon wafer with a native silicon oxide layer on its surface. For the two substrates it is found that PECVD SiO₂ grows in the form of islands. When the SiO₂ particles reach heights close to 10 nm they coalesce and cover completely the substrate surface. The particle formation mechanism has been confirmed by TEM observation of the particles grown on silicon substrates. The kinetic Monte Carlo simulation of the nucleation and growth of the SiO₂ particles has shown that formation of islands is favoured under PECVD conditions because the plasma species may reach the substrate surface according to off-perpendicular directions. The average energy of these species is the main parameter used to describe their angular distribution function, while the reactivity of the surface is another key parameter used in the simulations.     

背景磁场下Y系超导带材临界电流的测量

高温超导电力装置一般工作于低磁场情况下,测量超导带材在低场下的临界电流特性非常重要.文中通过自行设计的实验平台,采用四引线法对Y系超导带材低场下的临界电流特性进行了测量,得到了带材临界电流随外加磁场变化的曲线,并与Bi系带材低场下的临界电流特性进行比较.     

原子吸收光谱分析背景校正技术的新进展

对在2004年推向市场的两种商品仪器所采用的两种新的背景校正方法,从方法的原理、演变与形成过程及分析性能等方面进行了较为详细的讨论和解析。     

The application of high-resolution mass spectrometry-based data-mining tools in tandem to metabolite profiling of a triple drug combination in humans

Patients are usually exposed to multiple drugs, and metabolite profiling of each drug in complex biological matrices is a big challenge. This study presented a new application of an improved high resolution mass spectrometry (HRMS)-based data-mining tools in tandem to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was metronidazole-pantoprazole-clarithromycin (MET-PAN-CLAR), which is widely used in clinic to treat ulcers caused by Helicobacter pylori. First, mass defect filter (MDF), as a targeted data processing tool, was able to recover all relevant metabolites of MET-PAN-CLAR in human plasma and urine from the full-scan MS dataset when appropriate MDF templates for each drug were defined. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, worked effectively except for several trace metabolites, which were buried in the remaining background signals. Third, an integrated strategy, i.e., untargeted BS followed by improved MDF, was effective for metabolite identification of MET-PAN-CLAR. Most metabolites except for trace ones were found in the first step of BS-processed datasets, and the results led to the setup of appropriate metabolite MDF template for the subsequent MDF data processing. Trace metabolites were further recovered by MDF, which used both common MDF templates and the novel metabolite-based MDF templates. As a result, a total of 44 metabolites or related components were found for MET-PAN-CLAR in human plasma and urine using the integrated strategy. New metabolic pathways such as N-glucuronidation of PAN and dehydrogenation of CLAR were found. This study demonstrated that the combination of accurate mass-based multiple data-mining techniques in tandem, i.e., untargeted background subtraction followed by targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drugs in vivo.