Experimental Studies on TiO2 NT with Metal Dopants through Co-Precipitation, Sol–Gel, Hydrothermal Scheme and Corresponding Computational Molecular Evaluations

Author:

Estévez Ruiz Eduardo Patricio12ORCID,Lago Joaquín López2ORCID,Thirumuruganandham Saravana Prakash1ORCID

Affiliation:

1. Centro de Investigación de Ciencias Humanas y de la Educación (CICHE), Universidad Indoamérica, Ambato 180103, Ecuador

2. Grupo de Polímeros, Departamento de Física y Ciencias de la Tierra, Escuela Universitaria Politécnica, Universidade da Coruña, 15471 Ferrol, Spain

Abstract

In the last decade, TiO2 nanotubes have attracted the attention of the scientific community and industry due to their exceptional photocatalytic properties, opening a wide range of additional applications in the fields of renewable energy, sensors, supercapacitors, and the pharmaceutical industry. However, their use is limited because their band gap is tied to the visible light spectrum. Therefore, it is essential to dope them with metals to extend their physicochemical advantages. In this review, we provide a brief overview of the preparation of metal-doped TiO2 nanotubes. We address hydrothermal and alteration methods that have been used to study the effects of different metal dopants on the structural, morphological, and optoelectrical properties of anatase and rutile nanotubes. The progress of DFT studies on the metal doping of TiO2 nanoparticles is discussed. In addition, the traditional models and their confirmation of the results of the experiment with TiO2 nanotubes are reviewed, as well as the use of TNT in various applications and the future prospects for its development in other fields. We focus on the comprehensive analysis and practical significance of the development of TiO2 hybrid materials and the need for a better understanding of the structural–chemical properties of anatase TiO2 nanotubes with metal doping for ion storage devices such as batteries.

Funder

Computational modeling of biomaterials and applications to bioengineering and classical and quantum machine learning for predicting social engineering

Publisher

MDPI AG

Subject

General Materials Science

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