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Crystal Clear
Published in Sharon Ann Holgate, Understanding Solid State Physics, 2021
Metals cannot form covalently bonded structures because their atoms do not have enough electrons in the outermost shell. For example, the metals in Groups IA (which include lithium, sodium, and potassium) and IIA (which include magnesium and calcium) of the periodic table have one and two valence electrons, respectively, and four valence electrons are needed for a 3-D covalent solid to be formed. However, metallic bonding does involve the sharing of the valence electrons, so can be thought of as a form of covalent bonding in which all the valence electrons from every atom in the solid are shared between all of these atoms. One way to visualise this is to imagine (as depicted in Figure 2.6) a sea of valence electrons surrounding islands of positive ions that are created as a result of the valence electrons leaving their respective atoms. The attraction between these positive ions and the sea of negatively charged electrons forms the metallic bond which holds the solid together.
Semiconductor Devices
Published in Dale R. Patrick, Stephen W. Fardo, Electricity and Electronics Fundamentals, 2020
Dale R. Patrick, Stephen W. Fardo
In metallic bonding there is a type of electrostatic force between positive ions and electrons. In a sense, electrons float around in a cloud that covers the positive ions. This floating cloud bonds the electrons randomly to the ions. Figure 3-3 shows an example of the metallic bonding of copper.
Physical Factors in Phase Formation
Published in Daniel D. Pollock, PHYSICAL PROPERTIES of MATERIALS for ENGINEERS 2ND EDITION, 2020
It has been proposed that as the electronegativity differences of compounds of a series of this kind decrease, the sizes of the ions increase, and the wavefunctions of the valence electrons have greater spatial extents. This could lead to an increased degree of metallic bonding as the probability that the valence electrons increases for their associations with increasing numbers of ions.
Solute-second phase interaction for Mg, Ag and Zn in Al–Li alloys
Published in Philosophical Magazine, 2020
Jian-Gang Yao, Rong-Kai Pan, Yong Jiang, Deng-Feng Yin, Hua Wang
Metallic bonding typically has a mixed nature of covalent and ionic interactions. The covalent features of local metallic bonding changes have been manifested by differential charge densities in Figure 6. The ionic contribution can be revealed by Bader charge analysis. Table 2 compares the calculated Bader charges of Mg, Ag and Zn in δ′ bulk and on the interface. The net charge here is calculated by subtracting the Bader charge from the atomic valance charge. Positive net charge measures the amount of electrons transferring from the solute atom to its local surrounding. Clearly, Mg in δ′ acts as electron donor. The net charge of Mg (+1.44e) on the interface is higher than that in δ′ bulk (+1.23e), indicating that Mg is more chemically attracted to the interface. By contrast, both Ag and Zn act as electron acceptor in both places, and the net charges in δ′ bulk are predicted to be more negative for both the two solutes, indicating that they are more chemically attracted to δ′ bulk. The Bader charge and the differential charge density analyses are well complementary to each other.
Phase transition of ZrN under pressure
Published in Philosophical Magazine, 2019
Group IVB transition metal nitrides, promising engineering materials, have been drawn considerable attentions due to their various high tech applications. They possess an unusual bonding character (a mixture of ionic, covalent and metallic bonding) [1–4], and hence they acquire metallic and non-metallic features, for example, extreme hardness and high melting points like covalent material and conductivity and even in some cases superconductivity like metals. Such a bonding character is believed to be indeed responsible for their astonishing mechanical and chemical properties [5–13], which lead to various high-tech applications such as microelectronics, magnetic recording, corrosion, cutting tools, low-temperature fuel cells, resistant coating, optoelectronic devices, etc.
Machine learning models for occurrence form prediction of heavy metals in tailings
Published in International Journal of Mining, Reclamation and Environment, 2023
Jiashuai Zheng, Mengting Wu, Zaher Mundher Yaseen, Chongchong Qi
More specifically, the atomic mass generally correlated positively with F1–F4 and negatively with F7. For F5 and F6, the atomic mass tended to be stable in general; only one fluctuation was recorded around a normalised atomic mass of 0.18, marked by a sudden increase followed by a decrease to the original trend. The above results suggest that an increase in atomic mass might result in greater element mobility in tailings. With increasing EC, F1–F4 first decreased and then increased. In contrast, F7 remained unchanged at normalised EC < 0.2; subsequently, F7 first increased and then decreased as the EC value increased. The reason for this observation is that F7 is a difficult fraction to extract, whereas fractions F1 to F4 are relatively easy to extract; therefore, the influence mechanisms of input variables on fractions F1–F4 were often the opposite of those for fraction F7. Similarly, in the study by Qi et al. [55], it was shown that when EC exceeded a certain limit (more than 2, which is 0.85 of the normalised data in our Figure 10), the corresponding F1 levels increased. This is because when the EC is large, fractions F1–F4 increase because metals are more likely to form ionic or covalent bonds. This is because metallic bonding occurs when metal atoms share their valence electrons to form a lattice of positive ions surrounded by a sea of delocalised electrons [52,56]. Metals with low EC values are more likely to form metallic bonds because their valence electrons are weakly attracted to the nucleus, thus allowing them to move freely throughout the metal lattice. In general, as the EC of an element increases, it tends to form more ionic or covalent bonds rather than metallic bonds [52,57]. Furthermore, we also found that with increasing total element concentration, there was relatively little change in occurrence forms, indicating that total element concentration was a relatively less important factor. These findings suggest that PDPs could be applied to assess the overall impact of input variables on HM occurrence forms.