Abstract:The reactive gas flow ratio and substrate bias voltage are crucial sputtering parameters for fabricating transition metal nitride films. In this study, W–N films were prepared using sputtering with nitrogen flow ratios (f) of 0.1–0.5. W–N and W–Si–N films were then prepared using an f level of 0.4 and substrate bias varying from 0 to −150 V by using sputtering and co-sputtering, respectively. The variations in phase structures, bonding characteristics, mechanical properties, and wear resistance of the W–N and … Show more
“…We store all the data needed for analysis in a data frame as tidy data; i.e., every column is a variable, every row an observation, and every cell a single value. 37 Using an R6 38 configuration object ( Figure 1 ), we specify which variable is in which column making it easy to integrate new inputs in prolfqua if provided as tidy data. For example, to visualize tidy Spectronaut, 39 DiaNN, 10 Skyline 40 outputs, or data in MSstats ( 20 ) format, only a few lines of code are needed to update the prolfqua configuration.…”
Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/ prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.
“…We store all the data needed for analysis in a data frame as tidy data; i.e., every column is a variable, every row an observation, and every cell a single value. 37 Using an R6 38 configuration object ( Figure 1 ), we specify which variable is in which column making it easy to integrate new inputs in prolfqua if provided as tidy data. For example, to visualize tidy Spectronaut, 39 DiaNN, 10 Skyline 40 outputs, or data in MSstats ( 20 ) format, only a few lines of code are needed to update the prolfqua configuration.…”
Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/ prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.
“…With further increase in the Si contents (i.e., for CrSiN-3 (4.8 at.% Si), CrSiN-4 (7.1 at.% Si), and CrSiN-5 (9.2 at.% Si) coatings) the hardness of the coatings and elastic modulus steeply decreases and this can be attributed to the possible high volume fraction of the amorphous phase within the CrSiN-0 coating matrix. It has been reported that an increase in the volume fraction of an amorphous phase within the CrSiN matrix phase resulted in hardness and elastic modulus reduction [ 22 ]. Fig.…”
“…With further increase in the Si contents (i.e., for CrSiN-3 (4.8 at.% Si), CrSiN-4 (7.1 at.% Si), and CrSiN-5 (9.2 at.% Si) coatings) the hardness of the coatings and elastic modulus steeply decreases and this can be attributed to the possible high volume fraction of the amorphous phase within the CrSiN-0 coating matrix. It has been reported that an increase in the volume fraction of an amorphous phase within the CrSiN matrix phase resulted in hardness and elastic modulus reduction [22].…”
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
