Controlling pyridinic, pyrrolic, graphitic, and molecular nitrogen in multi-wall carbon nanotubes using precursors with different N/C ratios in aerosol assisted chemical vapor deposition

Nitrogen-containing multi-wall carbon nanotubes (N-MWCNTs) were synthesized using aerosol assisted chemical vapor deposition (CVD) techniques in conjunction with benzylamine:ferrocene or acetonitrile: ferrocene mixtures. Different amounts of toluene were added to these mixtures in order to change the N/C ratio of the feedstock. X-ray photoelectron and near-edge X-ray absorption fine structure spectroscopy detected pyridinic, pyrrolic, graphitic, and molecular nitrogen forms in the N-MWCNT samples. Analysis of the spectral data indicated that whilst the nature of the nitrogen-containing precursor has little effect on the concentrations of the different forms of nitrogen in N-MWCNTs, the N/C ratio in the feedstock appeared to be the determining factor. When the N/C ratio was lower than ca. 0.01, all four forms existed in equal concentrations, for N/C ratios above 0.01, graphitic and molecular nitrogen were dominant. Furthermore, higher concentrations of pyridinic nitrogen in the outer shells and N2 molecules in the core of the as-produced N-MWCNTs suggest that the precursors were decomposed into individual atoms, which interacted with the catalyst surface to form CN and NH species or in fact diffused through the bulk of the catalyst particles. These findings are important for a better understanding of possible growth mechanisms for heteroatom-containing carbon nanotubes (CNTs) and therefore paving the way for controlling the spatial distribution of foreign elements in the CNTs using CVD processes

Carbon States in Carbon-Encapsulated Nickel Nanoparticles Studied by Means of X-Ray Absorption, Emission, and Photoelectron Spectroscopies

Electronic structure of nickel nanoparticles encapsulated in carbon was characterized by photoelectron, X-ray absorption, and X-ray emission spectroscopies. Experimental spectra are compared with the density of states calculated in the frame of the density functional theory. The carbon shell of Ni nanoparticles has been found to be multilayer graphene with significant (about 6%) amount of Stone--Wales defects. Results of the experiments evidence protection of the metallic nanoparticles from the environmental degradation by providing a barrier against oxidation at least for two years. Exposure in air for 2 years leads to oxidation only of the carbon shell of Ni@C nanoparticles with coverage of functional groups.Comment: 16 pages, 6 figures, accepted in J. Phys. Chem.

Non-High Density Lipoprotein Cholesterol: A Modern Benchmark for Assessing Lipid Metabolism Disorders

Aim. To perform a population analysis of Non-High Density Lipoprotein Cholesterol level (non-HDL-c) in Russian population and to evaluate its association with cardiovascular events.Material and Methods. The material consisted of results obtained from 11 regions of the ESSE-RF1 Study and from 4 regions of the ESSE-RF2 Study. Study protocols were identical. The studies were performed in 2012-2014 and 2017, respectively. Endpoints were assessed in 19041 people aged 35-64 years. The median follow-up was 6.5 years in ESSE RF (1) and 3.8 years in ESSE RF(2). Analysis was performed for three lipid variables: total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and non-HDLC in two samples: the general population sample and the same sample without individuals with coronary heart disease (CHD), myocardial infarction (MI) and/or stroke history and not taking statins (the population sample of "without a history of cardiovascular diseases [CVD]". The analysis of nonlinear associations was performed using the generalized additive Cox model. The combined cardiovascular endpoint was represented by cardiovascular death and nonfatal MI and stroke. Traditional and laboratory FRs, socio-demographic parameters were analyzed. The significance level for all tested hypotheses was set to be 0.05.Results. The prevalence of elevated non-HDL-C level (>3.7 mmol/l) was found to be 74.6%. No gender differences were found: there was 74.6% for men and 74.5% for women. Both mean values and prevalence of elevated non-HDL-C were increased with age in women, and its level was slightly decreased in men after 55 years old. Almost all analyzed RFs were significantly associated with elevated non-HDL-C in these two population samples. In both samples elevated total CH and elevated LDL-C were associated with all-cause mortality after correction for all RFs. On the contrary, the non-HDL-C was associated with CVD combined end pints. It has been shown that the risk of these end points increases uniformly with increase in levels of non HDL cholesterol, no nonlinear associations were found.Conclusion. The results of a population-based analysis of non-HDL-C performed in the Russian population for the first time confirmed that elevated non-HDL-C levels contribute significantly to determining the risk of cardiovascular events in the medium term. It can be assumed that the new risk scales (SCORE2 and SCORE OP) proposed by the European Society of Cardiology and the European Society of Preventive Cardiology, which include non-HDL C instead of TC, will allow adequate assessment of 10-year cardiovascular risk for Russians. However, continued monitoring of endpoints in order to obtain stable associations is required

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants