Atom’s Nuclear Structure and the Periodic Table of the Chemical Elements

A new method for the identification of the chemical Elements isotopes takes advantage of the isotope Neutron Excess (NE) number. The repre-sentation of the natural isotopes in the Z-NE plane reveals a surprising correspondence between atom’s nuclear and electronic structures. Nuclear directs the atom electronic structure in spite of the alternative set of numbers ruling the two main atom’s compartments. These compartments appear better integrated than actually considered. The Mendeleev periodic table is rooted in the atom’s nuclear structure. Two recent studies arrive to identical conclusions.

Nucleus Directs the Electronic Structure of the Atom

The present study supports the provocative idea that the nucleus directs the atom’s electronic structure. With the progress of the atomic number the Atomic Molar Volume evolution of the chemical elements obeys the atom’s electronic structure rules, fitting at the same time the concomitant specular evolution of the Neutron Excess addition to the nuclei. Details such as the Atomic Molar Volume contraction of the d blocks transition metals or of the Eu and Yb atomic volume anomaly of the lanthanoid metals respond to the nuclear in addition to the atom’s electronic structure. Atom’s nuclei are synthetized in the star interior and capture the electrons only after migration to the star’s periphery, to become stable atoms: nuclei are prior to atoms. Nuclear structure elements, like the 50 and 82 neutron and proton magic numbers, are geared to the noble gases, the central elements of the electronic structure.

Bohr’s Spectrum of Quantum States in the Atomic Hydrogen Deduced from the Uncertainty Principle for Energy and Time

A modified uncertainty principle coupling the intervals of energy and time can lead to the shortest distance attained in course of the excitation process, as well as the shortest possible time interval for that process. These lower bounds are much similar to the interval limits deduced on both the experimental and theoretical footing in the era when the Heisenberg uncertainty principle has been developed. In effect of the bounds existence, a maximal nuclear charge Ze acceptable for the Bohr atomic ion could be calculated. In the next step the velocity of electron transitions between the Bohr orbits is found to be close to the speed of light. This result provides us with the energy spectrum of transitions similar to that obtained in the Bohr’s model. A momentary force acting on the electrons in course of their transitions is estimated to be by many orders larger than a steady electrostatic force existent between the atomic electron and the nucleus.

Big-data-accelerated aperiodic Si/Ge superlattice prediction for quenching thermal conduction via pattern analysis

Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal property,machine learning algorithm has been widely adopted.However,in the optimization of multivariable material structure such as different lengths or proportions,the machine learning algorithm may be required to be recon-ducted again and again for each variable,which will consume a lot of computing resources.Recently,it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural ran-domness,which can also be reflected in their local pattern structures.Inspired by these,we propose a new pattern analysis method,in which machine learning only needs to be carried out for one time,and through which the optimal structure of different variables with low thermal conductivity can be obtained.To verify the method,we compare the thermal conductivities of the structure obtained by pattern analysis,conventional machine learning,and previous literature,respectively.The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.