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	<dc:title xml:lang="en-US">Metal(II) triazole complexes: Synthesis, biological evaluation, and analytical characterization using machine learning-based validation</dc:title>
	<dc:creator>Arshad, Muhammad Yousaf</dc:creator>
	<dc:creator>Rashid, Aqsa</dc:creator>
	<dc:creator>Mahmood, Faisal</dc:creator>
	<dc:creator>Saeed, Salaha</dc:creator>
	<dc:creator>Ahmed, Anam Suhail</dc:creator>
	<dc:subject xml:lang="en-US">Complexes</dc:subject>
	<dc:subject xml:lang="en-US">Fungal strains</dc:subject>
	<dc:subject xml:lang="en-US">Transition metal</dc:subject>
	<dc:subject xml:lang="en-US">Machine learning</dc:subject>
	<dc:subject xml:lang="en-US">K-Nearest neighbors</dc:subject>
	<dc:description xml:lang="en-US">The synthesis of many transition metal complexes containing 3,5-diamino-1,2,4-triazole (Hdatrz) as a ligand with different counter anions Br⎺, Cl⎺, ClO4⎺ and SO42- has been studied extensively, but the chemistry of transition metal nitrate and acetate compounds and their reactivity are relatively unexplored. In this research work, two new series of metal(II) complexes (M = Ni, Co, and Zn) {[Ni3(Hdatrz)6(H2O)6](NO3)6 (1), [Co3(Hdatrz)6(H2O)6](NO3)6 (2), [Zn3(Hdatrz)6(H2O)6](NO3)6 (3), [Ni3(Hdatrz)6(H2O)6](OAc)6 (4), [Co3(Hdatrz)6(H2O)6] (OAc)6 (5) and [Zn3(Hdatrz)6(H2O)6](OAc)6 (6)} have been synthesized. These synthesized complexes were characterized by various physicochemical techniques such as UV-vis spectroscopy, Fourier transform infrared spectroscopy, and magnetic susceptibility measurements. All six complexes were found to be trinuclear and bridged through the Hdatrz ligand. Spectral data suggested a distorted octahedral environment around the central metal ions in these complexes. It also revealed that the NH and OH groups are involved in hydrogen bonding. These complexes were tested against the fungal strains Colletotrichum gloeosporioides and Aspergillus niger. These synthesized complexes have not been observed to have antifungal activities. The machine learning K-nearest neighbours evaluates the analytical characteristics and solubility behavior of the metal complexes. Machine learning models provide results with 75% precision.</dc:description>
	<dc:publisher xml:lang="en-US">Atlanta Publishing House LLC</dc:publisher>
	<dc:date>2023-03-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://www.eurjchem.com/index.php/eurjchem/article/view/2396</dc:identifier>
	<dc:identifier>10.5155/eurjchem.14.1.155-164.2396</dc:identifier>
	<dc:source xml:lang="en-US">European Journal of Chemistry; Vol. 14 No. 1 (2023): March 2023; 155-164</dc:source>
	<dc:source>2153-2257</dc:source>
	<dc:source>2153-2249</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://www.eurjchem.com/index.php/eurjchem/article/view/2396/pdf_2396</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Authors</dc:rights>
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