Density functional study of structural, electronic, and optical properties of small bimetallic ruthenium-copper clusters

The structural, electronic, bonding, magnetic, and optical properties of bimetallic [Cu(n)Ru(m)](+/0/-) (n + m ≤ 3; n, m = 0-3) clusters were computed in the framework of the density functional theory (DFT) and time-dependent DFT (TD-DFT) using the full-range PBE0 nonlocal hybrid GGA functional combined with the Def2-QZVPP basis sets. Several low-lying states have been investigated and the stability of the ground state spinomers was estimated with respect to all possible fragmentation schemes. Molecular orbital and population analysis schemes along with computed electronic parameters illustrated the details of the bonding mechanisms in the [Cu(n Ru(m)](+/0/-) clusters. The TD-DFT computed UV-visible absorption spectra of the bimetallic clusters have been fully analyzed and assignments of all principal electronic transitions were made and interpreted in terms of contribution from specific molecular orbital excitations.     

Induced-gravity GUT-scale Higgs inflation in supergravity

Models of induced-gravity inflation are formulated within Supergravity employing as inflaton the Higgs field which leads to a spontaneous breaking of a \(U(1)_{B-L}\) symmetry at \(M_\mathrm{GUT}=2\cdot 10^{16}~{\mathrm{GeV}}\). We use a renormalizable superpotential, fixed by a U(1) R symmetry, and Kähler potentials which exhibit a quadratic non-minimal coupling to gravity with or without an independent kinetic mixing in the inflaton sector. In both cases we find inflationary solutions of Starobinsky type whereas in the latter case, others (more marginal) which resemble those of linear inflation arise too. In all cases the inflaton mass is predicted to be of the order of \(10^{13}~{\mathrm{GeV}}\). Extending the superpotential of the model with suitable terms, we show how the MSSM \(\mu \) parameter can be generated. Also, non-thermal leptogenesis can be successfully realized, provided that the gravitino is heavier than about \(10~{\mathrm{TeV}}\).     

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.     

Development and Validation of an Automated Zone Fluidics-Based Sensor for In Vitro Dissolution Studies of Captopril Using Total Error Concept

In the present research, a zone fluidics-based automated sensor for the analysis of captopril in in vitro dissolution samples is reported. Captopril is reacted under flow conditions with Ni(II) (10 mmol L⁻¹) in alkaline medium (0.15% v/v NH₃) to form a stable derivate, which is monitored spectrophotometrically at 340 nm. The chemical and instrumental parameters were carefully investigated and optimized. The validation of the developed method was performed in the range of 5 to 120% of the expected maximum concentration using the accuracy profiles as a graphical decision-making tool. The β-expectation tolerance intervals did not exceed the acceptance criteria of ±10%, which means that 95% of future results will be encompassed in the defined bias limits. The variation of the relative bias ranged between −2.3% and 3.5% and the RSD values for repeatability and intermediate precision were lower than 2.3% in all cases. The limit of detection (LOD), and the lower and the upper limit of quantification (LLOQ, ULOQ) were satisfactory and found to be 1%, 5% and 120% (corresponding to 0.6, 2.78 and 66.67 μg mL⁻¹ in dissolution medium). The developed method was successfully applied for the analysis of captopril in dissolution tests of two commercially available batches.