Logistic approach to polymer degradation in dynamic TGA

Cao, Ricardo; Naya, Salvador; Artiaga, Ramón; Garcı́a, Ana; Varela, Á.

doi:10.1016/j.polymdegradstab.2004.03.006

Cited by 61 publications

(39 citation statements)

References 14 publications

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“…The fitting was optimized by minimizing the Average Squared Error (ASE). It is a method that has been widely used [40][41][42]. ASE is the merit function used in this work.…”

Section: Optimal Fittingmentioning

confidence: 99%

Estimating the reversing and non‐reversing heat flow from standard DSC curves in the glass transition region

Artiaga

López-Beceiro

Tarrío-Saavedra

et al. 2011

Journal of Chemometrics

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A mathematical model for the total heat flow obtained in differential scanning calorimetry (DSC) experiments from polymers with enthalpic relaxation is proposed. It is limited to the glass transition and enthalpic relaxation range of temperature and to the cases where the enthalpic relaxation is the only non-reversing process taking place. The model consists of a mixture of functions representing the heat capacity heat flow of the glassy and non-glassy fractions, the glass transition progress and the enthalpic relaxation heat flow.Optimal fittings of the model were performed on the experimental total heat flow data, obtained from two thermoplastics with different aging times. Considering which functions of the mixture represent reversing and non-reversing processes, the reversing and non-reversing heat flows were also estimated. The estimated reversing and non-reversing signals were compared with the ones obtained by modulation. On the whole a good match was found, which was even better considering that the estimates are not affected by the frequency effect of the modulated temperature DSC (MTDSC) measurements. The model assumes linear trends for the heat capacity heat flow of the glassy and non-glassy structures. The glass transition progress is represented by a generalized logistic function and the enthalpic relaxation heat flow by the first derivative of another generalized logistic. It brings about a new approach to these phenomena, where the parameters of these functions represent the temperature at which each event is centered, the change of heat capacity (C p ) at the glass transition and the energy involved in the enthalpic recovery.

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“…The fitting was optimized by minimizing the Average Squared Error (ASE). It is a method that has been widely used [40][41][42]. ASE is the merit function used in this work.…”

Section: Optimal Fittingmentioning

confidence: 99%

Estimating the reversing and non‐reversing heat flow from standard DSC curves in the glass transition region

Artiaga

López-Beceiro

Tarrío-Saavedra

et al. 2011

Journal of Chemometrics

Self Cite

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“…As a consequence, using Condition A1 and expressions (34) and (35) it is easy to prove that the last two terms in Equation (28) are negligible for the purpose of the asymptotic distribution in Equation (34):…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…We now consider the logistic mixture model studied by Naya [32], Naya, Cao and Artiaga [33], Cao, Naya, Artiaga, García and Varela [34] and Naya, Cao, López-de-Ullibarri, Artiaga, Barbadillo and García [27]. The goodness-of-fit test presented in the previous section has been used to test the fitting of a dataset from a thermal analysis context to a logistic mixture model.…”

Section: Thermal Analysis Applicationmentioning

confidence: 99%

Nonlinear regression checking via local polynomial smoothing with applications to thermogravimetric analysis

Cao¹,

Naya²

2009

Journal of Chemometrics

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A goodness-of-fit test statistic for nonlinear regression models based on local polynomial estimation is proposed in this paper. The criterion used to construct the test is the distance between the parametric fit and the nonparametric regression estimation. The good performance of the test is shown via a simulation study. The method is applied to check a logistic mixture regression model for real data coming from a thermal analysis problem.

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“…The performance of reliability analysis depends strongly on the appropriateness of the model describing the degradation path. For many engineering applications, a nonlinear degradation path is observed with an S‐shaped curve . Previous studies proposed several deterministic models using logistic degradation paths, and the lifetime distribution and the reliability function were developed .…”

Section: Introductionmentioning

confidence: 99%

Reliability Modeling for Ultrathin Gate Oxides Subject to Logistic Degradation Processes with Random Onset Time

Peng

Feng

2012

Quality & Reliability Eng

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With device dimension downscaling, the oxide thickness reduction less than 3 nm in a metal-oxide-semiconductor structure has led to important changes in degradation mechanisms and failure modes. After the first breakdown event of ultrathin gate oxides, the leakage current presents a less dramatic and noisy continuous breakdown mode called progressive breakdown. In this article, we characterize the overall breakdown process of ultrathin gate oxides as a randomized logistic degradation process with a random onset time. The explicit result of the lifetime distribution is derived on the basis of this logistic degradation model. A numerical example is provided to calculate the lifetime distribution. The simulated lifetime data of our model fits lognormal distribution better than the Weibull distribution, which agrees with the experimental work in literature.

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Logistic approach to polymer degradation in dynamic TGA

Cited by 61 publications

References 14 publications

Estimating the reversing and non‐reversing heat flow from standard DSC curves in the glass transition region

Estimating the reversing and non‐reversing heat flow from standard DSC curves in the glass transition region

Nonlinear regression checking via local polynomial smoothing with applications to thermogravimetric analysis

Reliability Modeling for Ultrathin Gate Oxides Subject to Logistic Degradation Processes with Random Onset Time

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