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The aim of this study is to statistically identify and distinguish wood samples corresponding to different areas of annual rings in trees of temperate regions, using the corresponding thermogravimetric (TG) and their first TG derivative (DTG) curves, and specifically to verify whether late and early wood chestnut samples are different with statistical significance, taking into account their TG and DTG curves. These significant differences are sought by applying statistical procedures based on functional data analysis (FDA), such as the functional ANOVA and the FDA classification methods. Each TG curve is firstly smoothed using the local polynomial regression estimator, and its first derivative is estimated. Then, functional ANOVA based on random projections (RP) is used to identify significant differences between TG or DTG curves of early and late wood samples. In order to know the extent of the differences between early and late wood samples, they are discriminated (and the correct classification proportion obtained) by employing a kernel nonparametric functional data analysis technique, based on the Bayes' rule, as well as functional generalized linear models and functional generalized additive models, allowing to classify materials using more than one type of thermal curves simultaneously. The results are compared with those obtained using some classical multivariate supervised classification methods: linear discriminant analysis, naive Bayes (NBC) and quadratic classification (QDA). The partial least squares (PLS) dimension reduction procedure was previously applied to the TG curves in order to employ these multivariate methods. The application of RP ANOVA shows significant differences between late and early wood regarding mass loss and mass loss rate under combustion. The use of PLS multivariate methods or FDA classification approaches applied to the TG and DTG curves allows to distinguish very accurately between late and early wood. The proposed method could be applied to other species to identify thermooxidative differences, combined with other experimental methods to find their chemical and physical causes.

The aim of this study is to propose an alternative methodology to classify wood species using the first (DTG), second (2DTG), and third (3DTG) derivatives of the thermogravimetric curves (TG). Accordingly, the main contribution of this new procedure consists on classifying materials (wood) taking into account the mass loss rate and acceleration with respect to temperature. In our research, each TG curve is firstly smoothed using the local polynomial regression estimator, and the first, second, and third derivatives are estimated. The application of the local polynomial regression estimator provides a reliable way to obtain the TG derivatives, overcoming the noise problem in the TG derivative estimation. Then, using these estimated curves, the different wood classes are discriminated employing a nonparametric functional data analysis (NPFDA) technique, based on the Bayes rule and the Nadaraya-Watson regression estimator, and also novel functional generalized additive models (GAM). The latter allows to classify materials using simultaneously more than one type of thermal curves. The results are compared with those obtained using classical and machine learning multivariate supervised classification methods, such as Linear discriminant analysis, Quadratic classification, Naive Bayes, Logistic regression, \(k\) Nearest neighbors, Neural networks, and Support vector machines. A regression model consisting of the mixture of the first derivatives of four generalized logistic components, one per principal wood constituent (water, hemicellulose, cellulose, and lignin), is applied to fit the DTG curves. The resulting 16 parameters from this fit characterize each curve and are used as datasets to apply the multivariate supervised classification methods. The use of the TG derivatives jointly with the TG curves has proved to be an optimal discriminating feature, when the new functional GAM techniques are employed.

This study presents an alternative to simple estimation of parametric fitting models used in thermal analysis. The addressed problem consists in performing an alternative optimization method to fit thermal analysis curves, specifically TG curves and their first derivatives. This proposal consists in estimating the optimal parameters corresponding to fitting kinetic models applied to thermogravimetric (TG) curves, using evolutionary algorithms: differential evolution (DE), simulated annealing and covariance matrix adapting evolutionary strategy. This procedure does not need to include a vector with the initial values of the parameters, as is currently required. Despite their potential benefits, the application of these methods is by no means usual in the context of thermal analysis curve’s estimation. Simulated TG curves are obtained and fitted using a generalized logistic mixture model, where each logistic component represents a thermal degradation process. The simulation of TG curves in four different scenarios taking into account the extent of processes overlapping allows us to evaluate the final results and thus to validate the proposed procedure: two degradation processes non-overlapped simulated using two generalized logistics, two processes overlapped, four processes non-overlapped and four processes overlapped two by two. The mean square error function is chosen as objective function and the above algorithms have been applied separately and together, i.e., taking the final solution of the DE algorithm is the initial solution of the remaining. The results show that the evolutionary algorithms provide a good solution for adjusting simulated TG curves, better than that provided by traditional methods.

Interest in polymeric materials with dispersed nanotubes has increased in recent years. There are several methods to characterize this kind of dispersions that may be based on evaluating the percolation concentration, the “goodness” of the dispersion, or the matrix-nanotube interphase. Among other techniques, rheology and conductivity are used to this aim. Commonly, the oscillatory rheology measurements are performed within the linear viscoelastic range, which is achieved by operating at small amplitude oscillation shear. Nevertheless, these measurements do not fully describe the behavior of the dispersion structure. In this work, we propose the use of medium amplitude oscillation shear and large amplitude oscillation shear to characterize the dispersion/structure of a thermoplastic polyurethane matrix filled with multiwalled carbon nanotubes. The Ewoldt framework mathematical approach is used to analyze the non-linear stress response. That approach allows obtaining physically grounded magnitudes from the experimental data. These magnitudes allow for a better understanding of the effects of the filler content.

Prediction of polymer properties at short and long observation times is usually performed through time–temperature superposition (TTS) models, which make use of some calculated shift factors. Although TTS principle has been used for many decades, no firm rules have been developed for obtaining the master curves. In the absence of reliable long-term data, it has been a common practice to try to minimize the discrepancy between the individual shifted curves. It was reported that a TTS method is more reliable as that discrepancy is minimized. In this study, a new method for obtaining the shift factors is presented. The optimal shift factors were estimated by minimizing the distance between the single curve derivatives with respect to the derivative of the curve at the reference temperature. That shift factors were tested with some classical models. The data were analyzed by statistical methods, making use of bootstrap resampling and spline estimation. The shift factors obtained from the proposed method allow for obtaining smooth master curves. The accuracy of the estimations was evaluated.

The present study suggests a new approach, based on the utilization of temperature modulated differential scanning calorimetry (TMDSC) technique, for identifying and characterizing the organic–inorganic interphase of two materials: an epoxy–fumed silica nanocomposite and a thermoplastic polyurethane (TPU)–multiwalled nanotube (MWNT) composite. The approach used here makes use of TMDSC data and basically consists of using the phase angle or the derivative of the reversing heat flow instead of the reversing heat flow curve itself. In the case of epoxy–fumed silica composites, two glass transition regions were identified. The glass transition temperature (Tg) of the composite was observed to vary as a consequence of the filler content. This study shows that the Tg variation is due to the formation of an organic–inorganic interphase, with its own glass transition temperature, which is different from the epoxy matrix Tg. In the case of TPU–MWNT composites, two relaxations and an additional first order transition were observed: the first relaxation corresponds to the hard segment, the second is related to an interaction between filler and matrix and the third process may be connected to the partial melting of the hard segment. The addition of 0.5 wt% MWNT causes a small reduction in Tg of the TPU. A major nanotube addition, 10 wt%, induces the appearance of a new relaxation that may be associated with the existence of an interface. In general, a better separation between the matrix and interphase glass transitions was obtained by the TMDSC phase angle signal.

Poly(l-lactic acid) (PLLA) is one of the most studied biopolymers nowadays. Due to its good performance, it constitutes an alternative to petrochemical-derived polymers. It was largely studied by differential scanning calorimetry (DSC) and temperature-modulated DSC. Nevertheless, there is an ongoing debate of what happens at the overlapping melting processes. In the present work, the experimental setups are discussed. Different modulation conditions are proposed for the study of the glass transition, cold crystallization, and the two reported melting processes. Finally, the experimental results allowed to measure the heat capacity change at the cold crystallization and a correct interpretation of what happens at the reported double melting peak of PLLA, which involves the existence of three crystalline structures.

Thermogravimetry has been widely applied to the study of wood and cellulose materials. There is a general agreement that decomposition of hemicellulose, cellulose, and ligning take place in a relatively narrow range of temperature, partially overlapping. There is no a definitive demonstration of which thermal feature corresponds to each component. In this study, three hardwood and two softwood species were considered: Castannea sativa, Eucaliptus globulus, Quercus robur, Pinus pinaster, and Pinus sylvestris. Thermogravimetric analysis of wood powder, ethanol-extracted wood, holocellulose, and lignin, obtained from those species revealed some important differences between hardwood and softwood holocelluloses and an important role of the ethanol-extractives, which explain the different behavior observed in both kinds of wood. FTIR spectra obtained from the evolved gases helped to clarify some degradation steps.

The use of pressure cell attached to a temperature modulated differential scanning calorimeter (TMDSC) is investigated to perform modulated DSC experiments at high pressures (TMPDSC). No previous reports were found on the use of TMPDSC. In this study, the proposed method is applied to the study of the pressure effect on the curing reaction of an epoxy system. Curing quasi-isothermal modulated experiments were performed at different pressures to evaluate the vitrification time. Linear heating modulated tests were also successfully performed at different pressures to separate the reversing glass transition effect from the residual exothermic cure reaction. The curing enthalpy, conversion versus temperature, and glass transition of the fully cured thermoset were also evaluated. All the studied parameters resulted to be affected by the pressure in the range from atmospheric pressure to 35 bar. It was observed that the curing enthalpy, the reaction rate and the conversion at any given time increase with any pressure increment. The usefulness of TMDSC to characterize the curing of thermosets is extended by PTMDSC to situations, i.e., aeronautics industry, where pressure curing is needed.

Vegetable oils of different types and qualities are widely used in homemade cooking and food industry. Most of the safety concerns were related to possible oxidation processes produced at the relatively high temperatures used when frying. Thus, the thermal stability to oxidation is an important parameter for edible oils. Oils from the Arbequina, Picual, Hojiblanca and Cornicabra olive varieties, corn, soybean and sunflower are studied in this work by means of pressure differential scanning calorimetry (PDSC). The general aim of this work is to evaluate the thermooxidative stability of these vegetable oils by the ASTM onset oxidation temperature (OOT) method. In addition, the ability of some parameters to identify different oils and some relations between the chemical composition and the OOT results are investigated.

In this study, thermogravimetric (TG) and differential scanning calorimetry (DSC) curves, obtained by means of a simultaneous TG/DSC analyzer, and statistical functional nonparametric methods are used to classify different wood species. The temperature ranges, where the highest probability of correct classification is reached, are also computed. As each observation is a curve, a nonparametric functional discriminant technique based on the Bayes rule and the Nadaraya–Watson regression estimator is used. It assigns a future observation to the highest probability predefined class (supervised classification). The smoothing parameter needed in this nonparametric method is selected according to the cross-validation technique. The method proposed is applied to a sample of 49 wood items (7 per wood class) and also to classify between hardwoods and softwoods. In all the cases, the samples have been successfully classified, obtaining better results with the TG curves. The results are compared with those obtained with other nonparametric methods based on boosting algorithm. A discussion about the relation of the obtained results with the referenced wood component degradation temperature ranks is presented.

The logistic mixture model was successfully studied previously in the separation of overlapping steps in some polymeric systems by the authors. In the present work, this method is applied to a polyesther-polyurethane degradation under air and inert atmospheres at several heat rates (5, 10, 15, 20 and 25 °C/min) in dynamic TGA. Every logistic component is fitted by reaction order, Johnson-Mehl-Avrami and Sestak-Berggren kinetics equations in order to calculate its kinetic parameters (activation energy, frequency factor and exponents). The reaction order model gives a good fitting and reproduces accurtelly the experimental curves. Johnson-Mehl-Avrami and Sestak- Berggren equations resulted to be not suitables because of the activation energy values obtained.

The aim of this paper is to evaluate and explain the fitting of dynamic TG curves by a mixture of logistic functions. This model assumes that more than one physical process may be involved in each mass loss step and that each physical process may extend along all the experiment. One of the main sources of difficulties in TG is that, very often, different stages of decomposition substantially overlap each other. Several real and simulated TG curves were analysed in this paper. An optimal fitting of the TG curves was obtained by a mixture of logistics. In many cases the optimal fitting reproduces accurately the TG curve. Accordingly, the TG curve can be understood as a sum of parallel reactions, where each single reaction is represented by one or a small number of logistic components. Additionally, making use of the analytical derivative of the fitting, a mixture of Arrhenius reaction order equations was applied to the same curves. In all the cases, the fitting obtained with the mixture of Arrhenius was worse than the obtained with the mixture of logistics. A software was developed to automatically perform these tasks. The physical meaning of the fitting was explained.

A diglycidyl-ether of bisphenol-A (DGEBA)/Triethylenetetramine (TETA) system was studied by non-isothermal differential scanning calorimetry (DSC) to establish its kinetics of cure. The DGEBA resin was Araldite GZ 601 X75 used in the marine coatings formulations. Previously, the optimum resin/hardener ratio was determined by the reaction heat measuring (.Hc) calculated from the curing exothermic peak. Tests at different heating rates (10, 15, 20, 25 and 30°C/min) under inert atmosphere were carried out in order to study the reaction kinetics. The activation energy of the cure (Ea) was obtained from these tests data by Borchardt-Daniels, autocatalytic, Duswalt and isoconversional Ozawa methods. Once the activation energy was determined, the master curves method was applied to find the kinetic model which best describes the measured DSC data. The Sestak-Berggren model SB (m,n) was found to be the most adequate for the system studied.

This study compares the abrasive wear resistance of a carbon steel, used in the linings of coal mills, and ferrous alloys with different chromium and carbon content. It also analyses the influence of chromium and carbon content on mechanical properties, such as hardness, and microstructure. The abrasive wear resistance of the different materials was determined by means of ASTM G 105, "Standard Test Method for Conducting Wet Sand/Rubber Wheel Abrasion Test". A blasting test was used to study erosive wear resistance.En este estudio se analiza la influencia, en las propiedades tribológicas, del contenido en carbono para distintas aleaciones férreas con similares contenidos en cromo, así como la influencia del cromo cuando el contenido en carbono es semejante. También, se analizó la influencia de estos elementos en propiedades como la dureza y en la microestructura de las aleaciones. Para el estudio tribológico se llevaron a cabo dos ensayos, el ensayo descrito en la norma ASTM G105: Standard Test Method for Conducting Wet Sand/Rubber Wheel Abrasion Test y ensayos de chorreo.

It is known that manganese steels respond well in situations where a high wear resistance is required. The manganese content provides an austenitic structure with both wear resistance and high tenacity values under wear. In its working life, this austenitic structure becomes martensitic as a result of impact and the phenomenon of friction, thus explaining an increase in hardness and its response under wear. Wear resistance of an austenitic steel with a 5% manganese content is determined following the regulation ASTM G105 “Standard test method for conducting wet sand/rubber wheel abrasion test”, consistent with subjecting samples to an abrasive wear test under controlled conditions. These wear tests were carried out on samples that had been previously subjected to various heat treatments. Once the wear tests were completed, the hardness of the tested samples was measured. The results were then compared with those obtained for samples without wear. Moreover, the microstructure and impact test of each sample were examined.

The method presented in this work allows for fitting an overall TGA curve by generalized logistic regression. It is assumed that overlapping processes may be involved in a single step of a TGA trace. Since the logistic functions reproduce very well the typical asymptoticity at the beginning and end of degradation processes, the authors consider that each single degradation process can be fitted by a single logistic function or by the linear combination of a small number of them. Since the fitting is very good, the generalized function can replace the original TGA curve. This way, it is possible to differentiate directly the overall TGA curve fitted, overcoming the problems of noise and over–under smoothing, typical in DTG calculation. On the other hand, the separation of overlapping processes in several functions suggests that each single overlapping degradative process could be explained by one or the linear combination of few logistic functions. Particularly, one of the parameters obtained with this kind of logistic mixture represents the point in the time (or temperature) axis where each process is “centered”, while another parameter is related to the degradation rate.

A method is proposed to find a function that fits traces resulting from the decomposition of polymers during a thermogravimetric analysis (TGA) experiment. The aim of this work is to find an expression that relates the weight of the sample with time (or temperature) and to study certain features like derivatives and critical points. It has been possible to keep in the asymptotic trend of the TGA trace by using a local logistic type regression. The typical asymptotic tendency of the weight loss is perfectly reproduced.

In this work, a method to calculate the baseline when weight loss processes take place is presented. Such method combines the information obtained by TGA with the simultaneous heat flow signal obtained in a Simultaneous Thermal Analyzer. Once the water evaporation step is identified, thermal parameters such as specific heat and enthalpy of vaporization can be combined with the instantaneous weight loss to subtract the heat flow corresponding to this component from the DSC baseline. In this paper the model was applied to water evaporation in forest litter samples.

This study investigated a methodology based on image processing and statistics to characterize and model the deformation upon controlled and uniform magnetic field and the relaxation under zero field of droplets observed in aqueous solutions of sodium alginate incorporating magnetic maghemite nanoparticles stabilized by adsorption of citrate ions. The changes of droplet geometry were statistically analyzed using a new approach based on the data obtained from optical microscopy, image processing, nonlinear regression, evolutionary optimization, analysis of variance and resampling. Image enhancement and then image segmentation (Gaussian mixture modeling) processes were applied to extract features with reliable information of droplets dimensions from optical micrographs. The droplets deformation and relaxation trends were accurately adjusted by the Kohlrausch-Williams-Watts (KWW) function and a mean relaxation time was obtained by fitting the time evolution of geometry parameters. It was found to be proportional to the initial radius of the spherical droplets and was associated to interfacial tension.

The application of nonparametric method of functional regression for the classification of different types of materials studied by thermo-gravimetric analysis is proposed in this work. The method is illustrated with two case studies for classifying different materials. Some simulation study shows the performance of the method when the correlation between groups parameters and the error variance change.