Experimental and Theoretical Studies of the Thermal Behavior of Titanium Dioxide−SnO<sub>2</sub> Based Composites

Voga, Geison P.; Coelho, M. G.; Lima, Geraldo Μ. de; Belchior, Jadson C.

doi:10.1021/jp111369f

Cited by 11 publications

(5 citation statements)

References 52 publications

(49 reference statements)

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“…Assuming the reaction process of the material is determined by two independent parameters, i.e., conversion rate (α) and temperature ( T ), the kinetics of heterogeneous reaction will obey the following equations. − normald normalα normald t = k · f ( α ) k = A .25em exp ( − E / R T ) normalφ = normald T normald t normald normalα normald T = A normalφ .25em exp ( − E / R T ) f ( α ) …”

Section: Theorymentioning

confidence: 99%

Kinetic Analysis of One-Step Solid-State Reaction for Li₄Ti₅O₁₂/C

Zhang²,

Yang

et al. 2011

J. Phys. Chem. A

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The kinetics of one-step solid-state reaction of Li(4)Ti(5)O(12)/C in a dynamic nitrogen atmosphere was first studied by means of thermogravimetric-differential thermal analysis technique at five different heating rates. According to the double equal-double steps method, the Li(4)Ti(5)O(12)/C solid-state reaction mechanism could be properly described as the Jander equation, which was a three-dimensional diffusion with spherical symmetry, and the reaction mechanism functions were listed as follows: f(α) = (3)/(2)(1 - α)(2/3)[1 - (1 - α)(1/3)](-1), G(α) = [1 - (1 - α)(1/3)](2). In FWO method, average activation energy, frequency factor, and reaction order were 284.40 kJ mol(-1), 2.51 × 10(18) min(-1), and 1.01, respectively. However, the corresponding values in FRL method were 271.70 kJ mol(-1), 1.00 × 10(17) min(-1), and 0.96, respectively. Moreover, the values of enthalpy of activation, Gibbs free energy of activation, and entropy of activation at the peak temperature were 272.06 kJ mol(-1), 240.16 kJ mol(-1), and 44.24 J mol(-1) K(-1), respectively.

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Section: Theorymentioning

confidence: 99%

Kinetic Analysis of One-Step Solid-State Reaction for Li₄Ti₅O₁₂/C

Zhang²,

Yang

et al. 2011

J. Phys. Chem. A

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“…Beyond ANNs, some other artificial intelligence approaches have been sporadically applied to the TA problems and are worth mentioning. They include the expert analysis of thermogravimetric data [ 121 ], genetic approach [ 122 ] for determining kinetic parameters, modeling with adaptive neuro-fuzzy inference system [ 96 , 123 , 124 ] to predict the mass loss data, extreme gradient boosting algorithm [ 125 ] for product yield evaluation. A wider application of machine learning in these areas is expected as the computational tools become more readily available.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

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Section: Theoretical Methodsmentioning

confidence: 99%

“…Assuming the reaction process of the precursor is determined by two independent parameters, i.e., conversion rate (α) and temperature ( T ), the kinetics of reaction will obey the following equations. , normald α normald t = k f ( α ) k = A .25em exp [ − E / ( R T ) ] β = normald T normald t normald α normald T = A β .25em exp [ − E / ( R T ) ] f ( α ) where k , t , f (α), A , β, E , and R are reaction rate constant, time, reaction mechanism function, pre-exponential factor, heating rate, apparent activation energy, and the universal gas constant, respectively. The conversion rate α is defined as α = W t − W normali W normali − W normalf where W t is the weight at any time t and where W i and W f respectively are the initial and final sample weights.…”

Section: Theoretical Methodsmentioning

confidence: 99%

Kinetic Studies on the Synthesis of Monoclinic Li₃V₂(PO₄)₃via Solid-State Reaction

Chen

et al. 2014

J. Phys. Chem. A

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The data obtained by thermogravimetry (TG), differential thermal analysis (DTA), and X-ray diffraction (XRD) measurements of the stoichiometric mixture of LiNO, NHVO, and NHHPO were analyzed by both the Flynn-Wall-Ozawa (FWO) and Friedman-Reich-Levi (FRL) methods. The whole solid state reaction process could be divided into four stages corresponding to α values of 0.03-0.20, 0.30-0.60, 0.70-0.80, and 0.90-0.99. The nucleation of LiVPO (α = 0.70-0.80) is a determining step of the four-reaction processes, for which the mechanism of random nucleation and subsequent growth, described by the Avrami-Erofeev equation (n = 4) G(α) = [-ln(1 - α)], was assumed. Moreover, the Gibbs free energy of activation ΔG*, enthalpy of activation ΔH*, and entropy of activation ΔS* at maximum temperature of the peak for the determining step were 99.24 kJ·mol, 199.97 kJ·mol, and 136.30 J·mol·K, respectively. Finally, XRD analyses confirmed that the LiV(PO) synthesized by the precursor route is a pure phase.

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Experimental and Theoretical Studies of the Thermal Behavior of Titanium Dioxide−SnO₂ Based Composites

Cited by 11 publications

References 52 publications

Kinetic Analysis of One-Step Solid-State Reaction for Li₄Ti₅O₁₂/C

Kinetic Analysis of One-Step Solid-State Reaction for Li₄Ti₅O₁₂/C

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Kinetic Studies on the Synthesis of Monoclinic Li₃V₂(PO₄)₃via Solid-State Reaction

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Experimental and Theoretical Studies of the Thermal Behavior of Titanium Dioxide−SnO2 Based Composites

Cited by 11 publications

References 52 publications

Kinetic Analysis of One-Step Solid-State Reaction for Li4Ti5O12/C

Kinetic Analysis of One-Step Solid-State Reaction for Li4Ti5O12/C

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Kinetic Studies on the Synthesis of Monoclinic Li3V2(PO4)3via Solid-State Reaction

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Experimental and Theoretical Studies of the Thermal Behavior of Titanium Dioxide−SnO₂ Based Composites

Kinetic Analysis of One-Step Solid-State Reaction for Li₄Ti₅O₁₂/C

Kinetic Analysis of One-Step Solid-State Reaction for Li₄Ti₅O₁₂/C

Kinetic Studies on the Synthesis of Monoclinic Li₃V₂(PO₄)₃via Solid-State Reaction