Inhibition of chemical interaction of molybdenum and silicon in a Mo/Si multilayer structure by the formation of intermediate compounds

2021 ◽  
Author(s):  
Elena O. Filatova ◽  
Sergei S. Sakhonenkov ◽  
Aidar U. Gaisin ◽  
Aleksei S. Konashuk ◽  
Ratibor G. Chumakov ◽  
...  
Keyword(s):  
Multilayer Structure ◽  
Chemical Interaction ◽  
Barrier Layers

In the present study, the formation of intermediate compounds in the Mo/Si multilayer was realized by the introduction of barrier layers at the interfaces.

Mechanisms of Dopant Depth Profile Modification During Mass Spectrometric Analysis of Multilayer Structure

2015 ◽  
Vol 60 (6) ◽  
pp. 511-520 ◽  
Author(s):  
A.A. Efremov ◽  
◽  
V.G. Litovchenko ◽  
V.P. Melnik ◽  
O.S. Oberemok ◽  
...  
Keyword(s):  
Depth Profile ◽  
Multilayer Structure ◽  
Mass Spectrometric ◽  
Mass Spectrometric Analysis ◽  
Spectrometric Analysis ◽  
Profile Modification

The Correlation of Adhesion Strength with Barrier Structure in Cu Metallization

2003 ◽  
Vol 766 ◽  
Author(s):  
A. Sekiguchi ◽  
J. Koike ◽  
K. Ueoka ◽  
J. Ye ◽  
H. Okamura ◽  
...  
Keyword(s):  
Thin Films ◽  
Adhesion Strength ◽  
Nitrogen Concentration ◽  
Scratch Test ◽  
Barrier Structure ◽  
Barrier Layers ◽  
Cu Metallization ◽  
Microstructure Observation ◽  
Sputter Deposited ◽  
Sulfur Containing

AbstractAdhesion strength in sputter-deposited Cu thin films on various types of barrier layers was investigated by scratch test. The barrier layers were Ta1-xNx with varied nitrogen concentration of 0, 0.2, 0.3, and 0.5. Microstructure observation by TEM indicated that each layer consists of mixed phases of β;-Ta, bcc-TaN0.1, hexagonal-TaN, and fcc-TaN, depending on the nitrogen concentration. A sulfur- containing amorphous phase was also present discontinuously at the Cu/barrier interfaces in all samples. Scratch test showed that delamination occurred at the Cu/barrier interface and that the overall adhesion strength increased with increasing the nitrogen concentration. A good correlation was found between the measured adhesion strength and the composing phases in the barrier layer.

In Situ Assembly of LDPE/PA6 Multilayer Structure by Stirring

2020 ◽  
Vol 35 (1) ◽  
pp. 58-69
Author(s):  
P. Pongmuksuwan ◽  
W. Harnnarongchai
Keyword(s):  
Multilayer Structure

Barrier layers and tropical Atlantic SST biases in coupled GCMs

2008 ◽  
Author(s):  
W. -P. Breugem ◽  
P. Chang ◽  
C. J. Jang ◽  
J. Mignot ◽  
W. Hazeleger
Keyword(s):  
Tropical Atlantic ◽  
Barrier Layers

Solubility Enhancement of Poorly Water-Soluble Antifungal Drug Acyclovir by Nanocrystal Formulation Approach

2018 ◽  
Vol 11 (2) ◽  
pp. 4036-4042
Author(s):  
Prakash Goudanavar ◽  
Ankit Acharya ◽  
Vinay C.H
Keyword(s):  
Chemical Interaction ◽  
Research Work ◽  
Antiviral Drug ◽  
In Vitro Release ◽  
Oral Route ◽  
Water Soluble ◽  
Precipitation Method ◽  
Dsc Studies ◽  
Ft Ir

Administration of an antiviral drug, acyclovir via the oral route leads to low and variable bioavailability (15-30%). Therefore, this research work was aimed to enhance bioavailability of acyclovir by nanocrystallization technique. The drug nanocrystals were prepared by anti-solvent precipitation method in which different stabilizers were used. The formed nanocrystals are subjected to biopharmaceutical characterization including solubility, particle size and in-vitro release. SEM studies showed nano-crystals were crystalline nature with sharp peaks. The formulated drug nanocrystals were found to be in the range of 600-900nm and formulations NC7 and NC8 showed marked improvement in dissolution velocity when compared to pure drug, thus providing greater bioavailability. FT-IR and DSC studies revealed the absence of any chemical interaction between drug and polymers used. 

Fuel-Cladding Chemical Interaction of a Prototype Annular U-10Zr Fuel with Fe-12Cr Ferritic/Martensitic Cladding

2020 ◽  
Author(s):  
Xiang Liu ◽  
Luca Capriotti ◽  
Tiankai Yao ◽  
Jason Harp ◽  
Michael T. Benson ◽  
...  
Keyword(s):  
Chemical Interaction ◽  
Fuel Cladding

Convolutional Neural Networks for ATC Classification

2019 ◽  
Vol 24 (34) ◽  
pp. 4007-4012 ◽  
Author(s):  
Alessandra Lumini ◽  
Loris Nanni
Keyword(s):  
Classification System ◽  
Feature Vector ◽  
Chemical Interaction ◽  
Anatomical Therapeutic Chemical ◽  
World Health ◽  
Therapeutic Effects ◽  
True Rate ◽  
Unknown Compound ◽  
The Absolute ◽  
Learned Features

Background: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound’s possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. Methods: In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound’s chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Results: Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Conclusion: Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

Modulation of Gut Microbiota through Dietary Phytochemicals as a Novel Anti-infective Strategy

2020 ◽  
Vol 17 (4) ◽  
pp. 498-506 ◽  
Author(s):  
Pavan K. Mujawdiya ◽  
Suman Kapur
Keyword(s):  
Microbial Community ◽  
Quorum Sensing ◽  
Population Density ◽  
Gut Microbiota ◽  
Biofilm Formation ◽  
Chemical Interaction ◽  
Bacterial Species ◽  
Critical Role ◽  
Bacterial Cells ◽  
Gut Microbes

: Quorum Sensing (QS) is a phenomenon in which bacterial cells communicate with each other with the help of several low molecular weight compounds. QS is largely dependent on population density, and it triggers when the concentration of quorum sensing molecules accumulate in the environment and crosses a particular threshold. Once a certain population density is achieved and the concentration of molecules crosses a threshold, the bacterial cells show a collective behavior in response to various chemical stimuli referred to as “auto-inducers”. The QS signaling is crucial for several phenotypic characteristics responsible for bacterial survival such as motility, virulence, and biofilm formation. Biofilm formation is also responsible for making bacterial cells resistant to antibiotics. : The human gut is home to trillions of bacterial cells collectively called “gut microbiota” or “gut microbes”. Gut microbes are a consortium of more than 15,000 bacterial species and play a very crucial role in several body functions such as metabolism, development and maturation of the immune system, and the synthesis of several essential vitamins. Due to its critical role in shaping human survival and its modulating impact on body metabolisms, the gut microbial community has been referred to as “the forgotten organ” by O`Hara et al. (2006) [1]. Several studies have demonstrated that chemical interaction between the members of bacterial cells in the gut is responsible for shaping the overall microbial community. : Recent advances in phytochemical research have generated a lot of interest in finding new, effective, and safer alternatives to modern chemical-based medicines. In the context of antimicrobial research various plant extracts have been identified with Quorum Sensing Inhibitory (QSI) activities among bacterial cells. This review focuses on the mechanism of quorum sensing and quorum sensing inhibitors isolated from natural sources.

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