Valorization of royal palm tree agroindustrial waste by isolating cellulose nanocrystals.

A considerable increase in royal palm cultivation as a result of industrialization of canned heart of palm has generated large amounts of renewable lignocellulosic waste, but reuse is still rarely practiced. In this work, for the first time, cellulose nanocrystals (CNCs) were extracted from the leaf sheath discarded from the royal palm harvest. Chlorine-free purification methods were used and strong acid hydrolysis synthesis with different times and temperatures were performed. The purification treatments removed lignin successfully, reducing its content from 10.4% to 1.0%. The formation of spherical and rod-shaped CNCs reached yields between 7.9% and 48.8%, which was confirmed by a significant increase in crystallinity from 38.9% of natural fiber to 63.6% and 79.6% of CNCs, depending on temperature and synthesis duration. The production of CNCs from this underutilized waste has the potential to add value to royal palm tree crops, in addition to significantly reducing the volume of cumulative waste in the environment.

Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography - Part I (Theoretical).

An approach that is commonly used for calculating the retention time of a compound in GC departs from the thermodynamic properties ΔH, ΔS and ΔCp of phase change (from mobile to stationary). Such properties can be estimated by using experimental retention time data, which results in a non-linear regression problem for non-isothermal temperature programs. As shown in this work, the surface of the objective function (approximation error criterion) on the basis of thermodynamic parameters can be divided into three clearly defined regions, and solely in one of them there is a possibility for the global optimum to be found. The main contribution of this study was the development of an algorithm that distinguishes the different regions of the error surface and its use in the robust initialization of the estimation of parameters ΔH, ΔS and ΔCp.

Robust estimation of thermodynamic parameters (ΔH, ΔS and ΔCp) for prediction of retention time in gas chromatography - Part II (Application).

For this work, an analysis of parameter estimation for the retention factor in GC model was performed, considering two different criteria: sum of square error, and maximum error in absolute value; relevant statistics are described for each case. The main contribution of this work is the implementation of an initialization scheme (specialized) for the estimated parameters, which features fast convergence (low computational time) and is based on knowledge of the surface of the error criterion. In an application to a series of alkanes, specialized initialization resulted in significant reduction to the number of evaluations of the objective function (reducing computational time) in the parameter estimation. The obtained reduction happened between one and two orders of magnitude, compared with the simple random initialization.

Characterization of the mucilage extracted from jaracatiá (Carica quercifolia (A. St. Hil.) Hieron).

The mucilage of the jaracatiá fruit (Carica quercifolia (A. St. Hil.) Hieron) was extracted and for physicochemical characterization. The monosaccharide composition showed the presence of Rha, Ara, Xyl, Gal, Glc and GalA, being confirmed by GC-MS, FTIR and NMR. The mucilage was obtained in crude form by lyophilization of the extract and by precipitation, a process that resulted in a partial purification. Although not remarkable, it showed an antioxidant and antimicrobial potential. The thermogravimetric analysis indicated an easy handling at temperatures below 250°C. The natural reactivity of the material indicates for uses such as adsorbent or raw material for membranes.

Fast and accurate numerical method for predicting gas chromatography retention time.

Predictive modeling for gas chromatography compound retention depends on the retention factor (ki) and on the flow of the mobile phase. Thus, different approaches for determining an analyte ki in column chromatography have been developed. The main one is based on the thermodynamic properties of the component and on the characteristics of the stationary phase. These models can be used to estimate the parameters and to optimize the programming of temperatures, in gas chromatography, for the separation of compounds. Different authors have proposed the use of numerical methods for solving these models, but these methods demand greater computational time. Hence, a new method for solving the predictive modeling of analyte retention time is presented. This algorithm is an alternative to traditional methods because it transforms its attainments into root determination problems within defined intervals. The proposed approach allows for tr calculation, with accuracy determined by the user of the methods, and significant reductions in computational time; it can also be used to evaluate the performance of other prediction methods.