• Journal of Semiconductors
  • Vol. 40, Issue 2, 022803 (2019)
References

[1] M Kumar, A K Gupta, D Kumar. Mg-doped TiO2 thin films deposited by low cost technique for CO gas monitoring. Ceram Int, 42, 405(2015).

[2] U Diebold. The surface science of titanium dioxide. Surf Sci Rep, 48, 53(2003).

[3] B Yacoubi, L Samet, J Bennaceur et al. Properties of transition metal doped-titania electrodes: Impact on efficiency of amorphous and nanocrystalline dye-sensitized solar cells. Mater Sci Semicond Process, 30, 361(2015).

[4]

[5] M Hamadanian, S Karimzadeh, V Jabbari et al. Synthesis of cysteine, cobalt and copper-doped TiO2 nanophotocatalysts with excellent visible-light-induced photocatalytic activity. Mater Sci Semicond Process, 41, 168(2016).

[6] S A Ahmed. Annealing effects on structure and magnetic properties of Mn-doped TiO2. J Magn Magn Mater, 402, 178(2016).

[7] L Kernazhitsky, V Shymanovska, T Gavrilko et al. Photoluminescence of Cr-doped TiO2 induced by intense UV laser excitation. J Lumin, 166, 253(2015).

[8] T Potlog, P Dumitriu, M Dobromir et al. Nb-doped TiO2 thin films for photovoltaic applications. Mater Des, 85, 558(2015).

[9] A Arunachalam, S Dhanapandian, C Manoharan et al. Physical properties of Zn doped TiO2 thin films with spray pyrolysis technique and its effects in antibacterial activity. Spectrochim. Acta - Part A Mol Biomol Spectrosc, 138, 105(2015).

[10] M Mollavali, C Falamaki, S Rohani. Preparation of multiple-doped TiO2 nanotube arrays with nitrogen, carbon and nickel with enhanced visible light photoelectrochemical activity via single-step anodization. Int J Hydrogen Energy, 40, 12239(2015).

[11] A Siddiqa, D Masih, D Anjum et al. Cobalt and sulfur co-doped nano-size TiO2 for photodegradation of various dyes and phenol. J Environ Sci (China), 37, 100(2015).

[12] C McManamon, J O’Connell, P Delaney et al. A facile route to synthesis of S-doped TiO2 nanoparticles for photocatalytic activity. J Mol Catal.A Chem, 406, 51(2015).

[13]

[14] K O Akande, T O Owolabi, S O Olatunji et al. A novel homogenous hybridization scheme for performance improvement of support vector machines regression in reservoir characterization. Appl Comput Intell Soft Comput, 2016, 1(2016).

[15] T O Owolabi, K O Akande, S O Olatunji. Computational intelligence method of estimating solid- liquid interfacial energy of materials at their melting temperatures. J Intell Fuzzy Syst, 31, 519(2016).

[16] T O Owolabi, K O Akande, S O Olatunji. Computational intelligence approach for estimating superconducting transition temperature of disordered MgB2 superconductors using room temperature resistivity. Appl Comput Intell Soft Comput, 2016, 1709827(2016).

[17] T O Owolabi, K O Akande, S O Olatunji. Estimation of average surface energies of transition metal nitrides using computational intelligence technique. Soft Comput, 21, 6175(2017).

[18] T O Owolabi, M Faiz, S O Olatunji et al. Computational intelligence method of determining the energy band gap of doped ZnO semiconductor. Mater Des, 101, 277(2016).

[19] M A Suleiman, T O Owolabi, H B Adeyemo et al. Modeling of autoignition temperature of organic energetic compounds using hybrid intelligent method. Process Saf Environ Prot, 120, 79(2018).

[20] M Ghorbani, G Zargar, H Jazayeri-Rad. Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms. Petroleum, 2, 301(2016).

[21] L Zhou, K K Lai, Yu L. Credit scoring using support vector machines with direct search for parameters selection. Soft Comput, 13, 149(2009).

[22] P J G Nieto, J R A Fernández, V M G Suárez et al. A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain. Appl Math Comput, 260, 170(2015).

[23] P J Garcia Nieto, E Garcia-Gonzalo, F Sanchez Lasheras et al. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliab Eng Syst Saf, 138, 219(2015).

[24] J Kennedy, R Eberhart. Particle swarm optimization. IEEE International Conference on Particle swarm optimization, 4, 1942(1995).

[25]

[26] X Zhang, P Wang, D Liang et al. A soft self-repairing for FBG sensor network in SHM system based on PSO-SVR model reconstruction. Opt Commun, 343, 38(2015).

[27] D Basak, S Pal, D C Patranabis. Support vector regression. Neural Inf Process Lett Rev, 11(2007).

[28] R D A Timoteo, L N Silva, D C Cunha et al. An approach using support vector regression for mobile location in cellular networks. Comput Networks, 95, 51(2016).

[29] A Nazari, J G Sanjayan. Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine. Ceram Int, 41, 12164(2015).

[30] M A Abido. Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst, 24, 563(2002).

[31] W Khan, S Ahmad, M M Hassan et al. Structural phase analysis , band gap tuning and fluorescence properties of Co doped TiO2 nanoparticles. Opt Mater (Amst), 38, 278(2016).

[32] E O Oseghe, P G Ndungu, S B Jonnalagadda. Photocatalytic degradation of 4-chloro-2-methylphenoxyacetic acid using W-doped TiO2. J Photochem Photobiol A Chem, 312, 96(2015).

[33] M Tahir, N S Amin. Photocatalytic CO2 reduction with H2 as reductant over copper and indium co-doped TiO2 nanocatalysts in a monolith photoreactor. Appl Catal A Gen, 493, 90(2015).

[34] I Rangel-Vazquez et al. Synthesis and characterization of Sn doped TiO2 photocatalysts: Effect of Sn concentration on the textural properties and on the photocatalytic degradation of 2,4-dichlorophenoxyacetic acid. J Alloys Compd, 643, S144(2015).

[35] Y H Lin, T K Tseng, H Chu. Photo-catalytic degradation of dimethyl disulfide on S and metal-ions co-doped TiO2 under visible-light irradiation. Appl Catal A Gen, 469, 221(2014).

[36] L Yu, X Yang, J He, Y He et al. One-step hydrothermal method to prepare nitrogen and lanthanum co-doped TiO2 nanocrystals with exposed {001} facets and study on their photocatalytic activities in visible light. J Alloys Compd, 637, 308(2015).

[37] X F Lei, X X Xue, H Yang. Preparation and characterization of Ag-doped TiO2 nanomaterials and their photocatalytic reduction of Cr(VI) under visible light. Appl Surf Sci, 321, 396(2014).

[38] T O Owolabi, K O Akande, S O Olatunji. Development and validation of surface energies estimator (SEE) using computational intelligence technique. Comput Mater Sci, 101, 143(2015).

[39] A Majid, A Khan, G Javed et al. Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression. Comput Mater Sci, 50, 363(2010).

[40] C Z Cai, G L Wang, Y F Wen et al. Superconducting transition temperature T c estimation for superconductors of the doped MgB2 system using topological index via support vector regression. J Supercond Nov Magn, 23, 745(2010).

[41] C Z Cai, T T Xiao, J L Tang et al. Analysis of process parameters in the laser deposition of YBa2Cu3O7 superconducting films by using SVR. Phys C Supercond, 493, 100(2013).

[42] T O Owolabi, K O Akande, S O Olatunji. Estimation of superconducting transition temperature T C for superconductors of the doped MgB2 system from the crystal lattice parameters using support vector regression. J Supercond Nov Magn(2014).

[43] A E Giannakas, M Antonopoulou, Y Deligiannakis et al. Preparation, characterization of N-I co-doped TiO2 and catalytic performance toward simultaneous Cr(VI) reduction and benzoic acid oxidation. Appl Catal B Environ, 140/141, 636(2013).

[44] M Tahir, N S Amin. Indium-doped TiO2 nanoparticles for photocatalytic CO2 reduction with H2O vapors to CH4. Appl Catal B Environ, 162, 98(2015).