Vibrational Analysis of Manganese(II) Oxalates Hydrates: An In Silico Statistical Approach

The experimental and computational vibrational study for three different manganese(II) oxalates hydrates was explored. The elucidation of IR and Raman spectra were discussed based on their structural singularity; in the same way, they establish some interesting relations between them in the field of computational and statistical approaches. The density functional theory (DFT) computational approach was conducted for accurate prediction and interpretation of the intermolecular effects based on experimental and calculated IR and Raman spectra in the solid-state data in combination with multivariate statistical technique. The proposed computational scheme was also explored for the case of the isolated-molecule model. The goals of the study were to access the accuracy of the proposed procedure for solid-state calculations along with electron calculations for the isolated molecules and to reveal the similarities within the groups of objects by the cluster analysis (CA) techniques and two-way CA for the data. The presented simulation procedure should be very valuable for exploring and to classify other oxalate compounds

Iron oxide nanoparticles - in vivo/in vitro biomedical applications and in silico studies

The review presents a broad overview of the biomedical applications of surface functionalized iron oxide nanoparticles (IONPs) as magnetic resonance imaging (MRI) agents for sensitive and precise diagnosis tool and synergistic combination with other imaging modalities. Then, the recent progress in therapeutic applications, such as hyperthermia is discussed and the available toxicity data of magnetic nanoparticles concerning in vitro and in vivo biomedical applications are addressed. This review also presents the available computer models using molecular dynamics (MD), Monte Carlo (MC) and density functional theory (DFT), as a basis for a complete understanding of the behaviour and morphology of functionalized IONPs, for improving NPs surface design and expanding the potential applications in nanomedicine

Chemometric Expertise Of Clinical Monitoring Data Of Prolactinoma Patients

The present investigation indicates hidden relationships between the several clinical parameters usually monitored on prolactinoma patients using non-hierarchical cluster analysis. The major goal of the chemometric data mining is to offer a possible mode of optimization of the monitoring procedure by selecting a reduced number of health status indicators. The intelligent data analysis reveals the formation of three patterns of prolactinoma patients each one of them described by a set of clinical parameters. Thus, better strategies for considering patients with this diagnosis could be developed and clinically applied

Growth of MoSe2 thin films and use in electrochemical hydrogen evolution

We present the chemical vapor deposition (CVD) approach to grow MoSe2 thin films using colloidal molybdenum nanoparticles (Mo NPs). The synthetic protocol of Mo NPs was achieved using a wet-chemical method. The obtained Mo NPs were spin-coated on graphite substrates and heat-treated in the presence of selenium vapors at several temperatures (≥750 °C). The electrocatalytic activities of heat-treated MoSe2 thin films were studied for hydrogen evolution reaction (HER) in 0.5 M sulfuric acid (H2SO4). The lowest recorded overpotential of 218 mV at 10 mA cm−2 vs. a reversible hydrogen electrode was achieved with MoSe2−800°C catalyst. In addition, electrochemical impedance spectroscopy (EIS) was performed to access the chargetransfer resistance of the MoSe2 films. The colloidal approach combined with CVD is a promising route to produce carbon supported MoSe2 electrocatalyst for HER

Chemometric Assessment of Soil Pollution and Pollution Source Apportionment for an Industrially Impacted Region around a Non-Ferrous Metal Smelter in Bulgaria

The present study deals with the assessment of pollution caused by a large industrial facility using multivariate statistical methods. The primary goal is to classify specific pollution sources and to apportion their involvement in the formation of the total concentration of the chemical parameters being monitored. This aim is accomplished by intelligent data analysis based on cluster analysis, principal component analysis and principal component regression analysis. Five latent factors are found to explain over 80% of the total variance of the system being conditionally named “organic”, “non-ferrous smelter”, “acidic”, “secondary anthropogenic contribution” and “natural” factor. The apportionment models designate the contribution of the identified sources quantitatively and help in the interpretation of risk assessment and management actions. Since the study takes into account pollution uptake from soil to a cabbage plant, the data interpretation could help in introducing biomonitoring aspects of the assessment. The chemometric expertise helps in revealing hidden relationships between the objects and the variables involved to achieve a better understanding of specific pollution events in the soil of a severely industrially impacted region