Quantum Mechanical Approaches for Piezoelectricity Study in Perovskites

In this chapter, we show the procedures we have been used to theoretically investigate the piezoelectric effects in perovskites. The construction of extended basis sets using the generator coordinate Hartree-Fock (GCHF) method is shown, as well as the strategies used to contract extended basis sets and to evaluate their quality in molecular calculations. Besides, we show adequate procedures to choice polarization and diffuse functions to best represent the studied crystal. In addition, we also discuss conditions under which GCHF basis sets and standard basis sets from literature can be used to theoretical investigation of piezoelectricity in perovskites. We finalize the chapter presenting and discussing the results for investigations of piezoelectricity with standard basis sets for barium and lanthanum titanates. To conclude, we present evidences that BaTiO3 and LaTiO3 may have piezoelectric properties caused by electrostatic interactions

Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive Compounds

In this chapter, firstly, we briefly review aspects of the approximation of quantum chemistry, molecular electrostatic potential (MEP), and chemometrics techniques, which are accredited as important tools in the development of chemical science and are frequently used in the study and design of bioactive compounds. Ultimately, we use MEP and pattern recognition (PR) techniques as tools to design nitrofuran compounds with biological activity against Trypanosoma cruzi (T. cruzi). PR models (PCA, HCA, KNN, SDA, and SIMCA) were constructed and demonstrated that 23 nitrofurans can be classified into two classes or groups: more active and less active according to their degrees of activity against T. cruzi. Properties such as charge on the N atom of the nitro group (QN1); the difference between the highest occupied molecular orbital (HOMO) energy and the lowest unoccupied molecular orbital (LUMO) energy (GAP energy); molecular representation of structure based on electron diffraction code of signal 5, unweighted (Mor05u); and Moriguchi water–octanol partition coefficient (MlogP) are responsible for the classification into more active and less active studied nitrofurans. It is interesting to notice that these properties represent three distinct classes of interactions between the nitrofurans and the biological receptor: electronic (QN1 and GAP energy), steric (Mor05u), and hydrophobic (MlogP). The results of the application of PR models on the validation set evidenced two nitrofuran compounds (compounds 25 and 30) as more promising for synthesis and biological assays, which in the future can be used to validate our PR models

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio