Separation of Hydrogen from Water Molecules by Ion Implantation into Thin Ti Films

The potential of hydrogen as primary gas source has generated considerable interest in hydrogen separation technologies. In the present work, the method of ion implantation has been used to separate hydrogen from energetic water molecules penetrating into Ti films. According to the results of the present study, the technique and method of implantation are capable of splitting water molecular ions into their constituent atoms with accommodation of oxygen and hydrogen atoms in interstitials of Ti film. The experimental distribution profiles are fitted with the simulated results based on the analysis of solutions of rate equations including processes of molecular ion implantation and diffusion. The dominant mechanisms transporting incident particles from the surface into the bulk are discussed. The obtained results are compared to literature data on the widely studied titanium–hydrogen bulk system. The experimental and simulation results are in consistency that molecular ions upon entering the substrate break up into constituent atoms and separation of hydrogen occurs. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3541

Oxygen implantation and behaviour into Ті thin films from water vapour plasma

The behavior of O atoms in Ti film is investigated under high-flux, low-energy molecular water ion implantation. After 10 min of irradiation at room temperature, the anomalously deep penetration of oxygen without formation of new chemical compounds observable by XRD has been registered in Ti films using Auger spectroscopy analysis. It is shown that the surface energy increases under ion irradiation, and the relaxation processes minimizing the surface energy initiate the redistribution of atoms. Two surface energy relaxation processes are considered: (i) the mixing of atoms on the surface resulting in annihilation of surface vacancies; and (ii) the annihilation of surface vacancies by atoms transported from the bulk. The theoretical considerations are in agreement with the experimental results if to assume that the mass-transport in the bulk is controlled by the processes on the surface and the adsorption of reactive atoms or molecules leads to local and long-range restructuring and adatom relocation at the surface. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2081

Analysis of TihxOy Films Produced by Physical Vapor Deposition Method

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JOSA: Joint surface-based registration and atlas construction of brain geometry and function

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal contribution. arXiv admin note: text overlap with arXiv:2303.0159

Single-Trial Decoding of Scalp EEG under Natural Conditions

There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding

Structure and Photocatalytic Activity of Copper and Carbon-Doped Metallic Zn Phase-Rich ZnO Oxide Films

ZnO is one of the most important industrial metal oxide semiconductors. However, in order to fully realise its potential, the electronic structure of ZnO has to be modified to better fit the needs of specific fields. Recent studies demonstrated that reactive magnetron sputtering under Zn-rich conditions promotes the formation of intrinsic ZnO defects and allows the deposition of metallic Zn phase-rich ZnO films. In photocatalytic efficiency tests these films were superior to traditional ZnO oxide, therefore, the purposeful formation of intrinsic ZnO defects, namely Zn interstitials and oxygen vacancies, can be considered as advantageous self-doping. Considering that such self-doped ZnO remains a semiconductor, the natural question is if it is possible to further improve its properties by adding extrinsic dopants. Accordingly, in the current study, the metallic Zn phase-rich ZnO oxide film formation process (reactive magnetron sputtering) was supplemented by simultaneous sputtering of copper or carbon. Effects of the selected dopants on the structure of self-doped ZnO were investigated by X-ray diffractometer, scanning electron microscope, X-ray photoelectron spectroscope and photoluminescence techniques. Meanwhile, its effect on photocatalytic activity was estimated by visible light activated bleaching of Methylene Blue. It was observed that both dopants modify the microstructure of the films, but only carbon has a positive effect on photocatalytic efficiency

EEG data (from real-time neurofeedback experiment)

EEG_epochs_sample.npy: numPy array of size [1200, 550, 32] corresponding to [trials, samples, channels]. The data is recorded with a sampling frequency of 500Hz using Enobio 32 (Neuroelectrics), i.e. 1100 ms of data (100 ms pre-stimulus onset, 1000 ms post-stimulus onset). The channels are ordered as: ['P7', 'P4', 'Cz', 'Pz', 'P3', 'P8', 'O1', 'O2', 'T8', 'F8', 'C4', 'F4', 'Fp2', 'Fz', 'C3', 'F3', 'Fp1', 'T7', 'F7', 'Oz', 'PO3', 'AF3', 'FC5', 'FC1', 'CP5', 'CP1', 'CP2', 'CP6', 'AF4', 'FC2', 'FC6', 'PO4']. y_categories_sample.npy: Contains binary category labels for each (visual) trial. 0 corresponds to a "scene" category. 1 corresponds to a "face" category. More information here: https://github.com/gretatuckute/ClosedLoop/blob/master/README.m

EEG data (from real-time neurofeedback experiment)

EEG_epochs_sample.npy: numPy array of size [1200, 550, 32] corresponding to [trials, samples, channels]. The data is recorded with a sampling frequency of 500Hz using Enobio 32 (Neuroelectrics), i.e. 1100 ms of data (100 ms pre-stimulus onset, 1000 ms post-stimulus onset). The channels are ordered as: ['P7', 'P4', 'Cz', 'Pz', 'P3', 'P8', 'O1', 'O2', 'T8', 'F8', 'C4', 'F4', 'Fp2', 'Fz', 'C3', 'F3', 'Fp1', 'T7', 'F7', 'Oz', 'PO3', 'AF3', 'FC5', 'FC1', 'CP5', 'CP1', 'CP2', 'CP6', 'AF4', 'FC2', 'FC6', 'PO4']. y_categories_sample.npy: Contains binary category labels for each (visual) trial. 0 corresponds to a "scene" category. 1 corresponds to a "face" category. More information here: https://github.com/gretatuckute/ClosedLoop/blob/master/README.m

Context-sensitive features predict sentence memorability in the absence of memorable words

What makes some sentences more memorable than others? In this work, we treat the problem of recognizing previously seen sentences as a comparison between a target stimulus and noisy memory representations of previously presented stimuli. Building on past work in image and word memorability, we conduct a large-scale memorability experiment with 500 participants and 2,500 target sentences, eliciting variation in how accurately participants recognize repeated sentences. We predict the memorability of sentences from a) empirically established word-level memorability scores, and b) sentence-level distinctiveness and surprisal features that capture the compositional semantics of sentences. We find that the presence of individually memorable words is highly predictive of sentence memorability, but that sentence-level features also predict sentence memorability – especially in the absence of memorable words. This suggests that otherwise forgettable words can together create memorable compositional meanings that remain in memory and facilitate recognition

Floating Carbon-Doped TiO₂ Photocatalyst with Metallic Underlayers Investigation for Polluted Water Treatment under Visible-Light Irradiation

In the current study, we analysed the influence of metallic underlayers on carbon-doped TiO2 films for RhB decomposition and Salmonella typhimurium inactivation under visible-light irradiation. All the experiments were divided into two parts. First, layered M/C-doped-TiO2 film structures (M = Ni, Nb, Cu) were prepared by magnetron sputtering technique on borosilicate glass substrates in the two-step deposition process. The influence of metal underlayer on the formation of the carbon-doped TiO2 films was characterised by X-ray diffractometer, scanning electron microscope, and atomic force microscope. The comparison between the visible-light assisted photocatalytic activity of M/C-doped TiO2 structures was performed by the photocatalytic bleaching tests of Rhodamine B dye aqueous solution. The best photocatalytic performance was observed for Ni/C-doped-TiO2 film combination. During the second part of the study, the Ni/C-doped-TiO2 film combination was deposited on high-density polyethylene beads which were selected as a floating substrate. The morphology and surface chemical analyses of the floating photocatalyst were performed. The viability and membrane permeability of Salmonella typhimurium were tested in cycling experiments under UV-B and visible-light irradiation. Three consecutive photocatalytic treatments of fresh bacteria suspensions with the same set of floating photocatalyst showed promising results, as after the third 1 h-long treatment bacteria viability was still reduced by 90% and 50% for UV-B and visible-light irradiation, respectively. The membrane permeability and ethidium fluorescence results suggest that Ni underlayer might have direct and indirect effect on the bacteria inactivation process. Additionally, relatively low loss of the photocatalyst efficiency suggests that floating C-doped TiO2 photocatalyst with the Ni underlayer might be seen as the possible solution for the used photocatalyst recovery issue