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In some industrial sectors such as naval construction, the use of adhesives is still limited to some specific applications. However, shipbuilders, academia and classification societies are cooperating to expand the field of certificated applications of adhesive joints. As a part of a validation study, thermal and rheological studies of the curing process and of the cured adhesives should be included. While a neat glass transition and other relaxation processes can be normally identified by ramp temperature tests performed both on a differential scanning calorimeter or on a rheometer, there are some adhesive systems in which several glass transitions or melting or crystallization processes overlap. Applying a thermal treatment to delete the thermal history and conditioning are common practices to clarify what happens in complex systems. However, although that practices usually help, there are still some complexities due to overlapping processes that cannot be easily understood. An important point of this work is to show how differential scanning calorimetry and rheology complement to each other in order to demonstrate several thermal relaxations and to obtain a better understanding of the cure process. The use of two different techniques along with a careful election of the setup parameter values allows to better interpret the thermal events. In addition, thermogravimetry helps to understand some rheological behaviors. In the end, this work shows how a good insight of the adhesive properties can be obtained by means of the combined use of DSC, rheology and TG.

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.

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.

It has been shown that introducing carbon nanotubes (CNT) into a polymer matrix has a beneficial effect on thermal stability of composites. While the specific effects noted differ depending on many parameters, the general trend is an increase in the degradation temperature and reduction in the degradation rate, quantified as the mass decline over time. The purpose of this study is to evaluate how CNTs influence the main degradation process of composites made with polystyrene containing 2, 3 and 5 % of CNTs. Thermogravimetric experiments are performed, with nitrogen purge, at multiple linear heating ramps. The effects of the nanotubes on the degradation of polystyrene are evaluated. Insightful kinetic parameters were obtained for the main process making use of a recently developed model, which is adapted to the thermogravimetric context. The model allows the means to separate the main process from other processes, which could interfere with the kinetic analysis, and also subtract the residual mass, which could produce an apparent stabilizing effect. The main degradation process is clearly stabilized by the presence of nanotubes, although the stabilization is more pronounced at the lowest of the filler contents considered. Clear effects of nanotubes on kinetic parameters were observed.

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.

Calcium sulfate dihydrate has been widely characterized by both differential scanning calorimetry and thermogravimetry (TG). Two dehydration processes were reported to be partially overlapping. High resolution TG and water vapor self generated atmosphere pin-hole lid strategies were used to increase the resolution of both dehydration processes. In this study, isobaric experiments were carried out in a pressure differential scanning calorimetry cell. The approach consisted in combining the pin-hole lid with different pressures with nitrogen atmosphere. Resolution was improved at moderately low pressures. At higher pressures other processes were observed.

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 development of photopolymers was helped by the development of photocalorimetry, which is now a basic technique for the study of these materials. This work shows how to obtain vitrification times in single isothermal curing experiments by monitoring the reversing heat capacity along time in modulated temperature DSC–photocuring systems, overcoming the time-consuming problem of standard DSC. The effects of the light intensity and the isothermal curing temperature on the vitrification time of a photocurable system were evaluated. The results obtained at a given curing temperature with different light intensities indicate that the UV-light affects the molecular mobility hindering the vitrification process. The effects of the curing temperature on the vitrification time, the conversion at the vitrification time and the maximum conversion were also evaluated.

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.