Composites from a forest biorefinery byproduct and agrofibers: Lignosulfonate-phenolic type matrices reinforced with sisal fibers

The replacement of phenol with sodium lignosulfonate and formaldehyde with glutaraldehyde in the preparation of resins resulted in a new resol-type phenolic resin, sodium lignosulfonate-glutaraldehyde resin, in addition to sodium lignosulfonate-formaldehyde and phenol-formaldehyde resins. These resins were then used to prepare thermosets and composites reinforced with sisal fibers. Different techniques were used to characterize raw materials and/or thermosets and composites, including inverse gas chromatography, thermogravimetric analysis, and mechanical impact and flexural tests. The substitution of phenol by sodium lignosulfonate in the formulation of the composite matrices increased the impact strength of the respective composites from approximately 400 Jm(-1) to 800 J m(-1) and 1000 J m(-1), showing a considerable enhancement from the replacement of phenol with sodium lignosulfonate. The wettability of the sisal fibers increased when the resins were prepared from sodium lignosulfonate, generating composites in which the adhesion at the fiber-matrix interface was stronger and favored the transference of load from the matrix to the fiber during impact. Results suggested that the composites experienced a different mechanism of load transfer from the matrix to the fiber when a bending load was applied, compared to that experienced during impact. The thermogravimetric analysis results demonstrated that the thermal stability of the composites was not affected by the use of sodium lignosulfonate as a phenolic-type reagent during the preparation of the matrices.

IDM - A New Parallel Methodology to Calculate the Determinant of Matrices of the Order n, with Computational Complexity O(n)

This paper presents a new parallel methodology for calculating the determinant of matrices of the order n, with computational complexity O(n), using the Gauss-Jordan Elimination Method and Chio's Rule as references. We intend to present our step-by-step methodology using clear mathematical language, where we will demonstrate how to calculate the determinant of a matrix of the order n in an analytical format. We will also present a computational model with one sequential algorithm and one parallel algorithm using a pseudo-code.

Interference of inorganic ions on phenol degradation by the Fenton reaction

The addition of Cu2+ ions to the classical Fenton reaction (Fe2+ plus H2O2 at pH 3) is found to accelerate the degradation of organic compounds. This synergic effect causes an approximately 15 % additional reduction of the total organic carbon (TOC), representing an overall improvement of the efficiency of the mineralization of phenol. Although Fe2+ exhibits a high initial rate of degradation, the degradation is not complete due to the formation of compounds refractory to the hydroxyl radical. The interference of copper ions on the degradation of phenol by the Fenton reaction was investigated. In the presence of Cu2+, the degradation is slower, but results in a greater reduction of TOC at the end of the reaction (t = 120 min). In the final stages of the reaction, when the Fe3+ in the solution is complexed in the form of ferrioxalate, the copper ions assume the role of the main catalyst of the degradation

Matrix-vector multiplication and triangular linear solver using GPGPU for symmetric positive definite matrices derived from elliptic equations

The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific computation, and several challenges in developing a high performance kernel with the two modules is investigated. The main interest it to solve linear systems derived from the elliptic equations with triangular elements. The resulting linear system has a symmetric positive definite matrix. The sparse matrix is stored in the compressed sparse row (CSR) format. It is proposed a CUDA algorithm to execute the matrix vector multiplication using directly the CSR format. A dependence tree algorithm is used to determine which variables the linear triangular solver can determine in parallel. To increase the number of the parallel threads, a coloring graph algorithm is implemented to reorder the mesh numbering in a pre-processing phase. The proposed method is compared with parallel and serial available libraries. The results show that the proposed method improves the computation cost of the matrix vector multiplication. The pre-processing associated with the triangular solver needs to be executed just once in the proposed method. The conjugate gradient method was implemented and showed similar convergence rate for all the compared methods. The proposed method showed significant smaller execution time.