Jorge López-Beceiro

Thermogravimetric analysis of wood, holocellulose, and lignin from five wood species

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.

Role of temperature and pressure on the multisensitive multiferroic dicyanamide framework [TPrA][Mn(dca)(3)] with perovskite-like structure

A multistimuli response to temperature and pressure is found in the hybrid inorganic-organic perovskite-like [TPrA][Mn(dca)3] compound, which is related to a first-order structural phase transition near room temperature, Tt ≈ 330 K. This phase transition involves a transformation from room temperature polymorph I, with the noncentrosymmetric space group P4̅21c, to the high temperature polymorph II, with the centrosymmetric space group I4/mcm, and it implies ionic displacements, order-disorder phenomena, and a large and anisotropic thermal expansion (specially along the c-axis). As a consequence, [TPrA][Mn(dca)3] exhibits a dielectric anomaly, associated with the change from a cooperative to a noncooperative electric behavior (antiferroelectric (AFE)-paraelectric (PE) transition). The former implies an AFE distribution of electric dipoles in polymorph I, related to the described off-shift of the apolar TPrA cations and the order-disorder of the polar dca ligands mechanisms, that are different from those reported, up to now, for others perovskite-type hybrid compounds. Such cooperative electric order, below Tt ≈ 330 K, coexisting with long-range antiferromagnetic ordering below T = 2.1 K render the [TPrA][Mn(dca)3] a new type-I multiferroic material. In addition, the obtained experimental results reveal that this compound is also a multistimuli-responsive material, with a very large sensitivity toward temperature and applied external pressure, δTt/δP ≈ 24 K kbar(-1), even for small values of pressure (P < 2 kbar). Therefore, this material opens up a potential interest for future technological applications, such as temperature/pressure sensing.

Multiple phase and dielectric transitions on a novel multi-sensitive [TPrA][M(dca)3] (M: Fe2+, Co2+ and Ni2+) hybrid inorganic–organic perovskite family

The hybrid inorganic–organic [TPrA][M(dca)3] (M: Fe2+, Co2+ and Ni2+) compounds, where TPrA is the tetrapropylammonium cation and dca is the dicyanamide anion, are unique multi-sensitive compounds that display multiple phases and dielectric transitions. These materials exhibit up to three first-order structural transitions (between the polymorphs I, Ia, Ib and II) associated with the same number of dielectric transitions in the temperature range of 210–360 K. The mechanisms responsible for these dielectric responses are found to be novel within the hybrid perovskites, involving ionic displacements of the A-site cations (TPrA) and order/disorder processes of the X anions (dca). In addition, the phase transitions and dielectric transition temperatures can be tuned by applying external hydrostatic pressure or by inducing internal pressure by modifying the tolerance factor through ionic substitution in the B-sites. This multi-sensitive response towards temperature, external and internal pressure opens up promising technological applications for this family of materials, such as dielectric transductors or multistimuli-sensors, whose response can be modulated in a wide range of temperatures and pressures.

Simulation study for generalized logistic function in thermal data modeling

The principal aim of the present study is to describe, analyze, and compare from a statistical standpoint the generalized logistic model with some well-known models used in the solid-state kinetics: power law, Avrami–Erofeev, and reaction order. For this purpose, synthetic conversion curves that simulate the kinetic processes were generated using the power law, Avrami–Erofeev, and reaction order models, where the Arrhenius equation was assumed in all the cases. This comprehensive simulation study allows to describe the relationship between the parameters belonging to the proposed generalized logistic model and the pointed traditional models’ parameters, and also to validate the performance of the generalized logistic model in a wide variety of cases where other methods can be applied. Performing this analysis has been necessary to employ some new statistical techniques in thermal analysis modeling as the generalized additive models, and to perform global optimization evolutionary algorithms as the differential evolution for solving the non-linear regression problem. In order to implement these techniques, R statistical software routines were developed and applied.

Giant barocaloric effect in the ferroic organic-inorganic hybrid [TPrA][Mn(dca)3] perovskite under easily accessible pressures

The fast growing family of organic–inorganic hybrid compounds has recently been attracting increased attention owing to the remarkable functional properties (magnetic, multiferroic, optoelectronic, photovoltaic) displayed by some of its members. Here we show that these compounds can also have great potential in the until now unexplored field of solid-state cooling by presenting giant barocaloric effects near room temperature already under easily accessible pressures in the hybrid perovskite [TPrA][Mn(dca)3] (TPrA: tetrapropylammonium, dca: dicyanamide). Moreover, we propose that this will not be an isolated example for such an extraordinary behaviour as many other organic–inorganic hybrids (metal-organic frameworks and coordination polymers) exhibit the basic ingredients to display large caloric effects which can be very sensitive to pressure and other external stimuli. These findings open up new horizons and great opportunities for both organic–inorganic hybrids and for solid-state cooling technologies.

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

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.

New method for estimating shift factors in time–temperature superposition models

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.

Classification of wood using differential thermogravimetric analysis

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, Naïve Bayes, Logistic regression,

Wood identification using pressure DSC data

k