Search Results (4)

The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes. Full article

A constituting feature of item response models is that item and person parameters share a latent scale and are therefore comparable. The Person–Item Map is a useful graphical tool to visualize the alignment of the two parameter sets. However, the “classical” variant has some shortcomings, which are overcome by the new RMX package (Rasch models—eXtended). The package provides the RMX::plotPIccc() function, which creates an extended version of the classical PI Map, termed “PIccc”. It juxtaposes the person parameter distribution to various item-related functions, like category and item characteristic curves and category, item, and test information curves. The function supports many item response models and processes the return objects of five major R packages for IRT analysis. It returns the used parameters in a unified form, thus allowing for their further processing. The R package RMX is freely available at osf.io/n9c5r. Full article

MXenes possess unique features that are useful for broader industrial development. However, although many different compositions of MXenes have been discovered, little research has been conducted on the optimal synthesis strategy for producing the best MXenes yield. Therefore, substantial work is performed on the synthesis’ structure and property relationship for direct methanol fuel cell (DMFC) applications since MXenes have been successfully hybridised with rice husk ash (RHA). In this study, to produce titanium-based MXene, Ti₃C₂ nanopowders are added to the rice husk ash matrix to synthesise hybrid RHA/MXene composites (R-MX). Using different weight percentages of MXene hybridised with rice husk ash (2 wt. % R-MX, 4 wt. % R-MX and 6 wt. % R-MX), different electrochemical properties are obtained. Meanwhile, electrochemical analysis is undertaken to investigate the methanol oxidation performance using Linear Sweep Voltammetry (LSV). The highest percentage of the R-MX hybrid composite, 6 wt. % MXene, showed the lowest Tafel slope (148 mV/dec) and the highest ionic exchange current density in the same Tafel analysis. Moreover, the incorporation of MXene into RHA produced good results from the chronoamperometry analysis (CA), with the highest percentage of the hybrid composite, R-6MX, showing the highest retention rate of 97.28%. Meanwhile, the Nyquist plot analysis showed an increasing semicircle arc diameter at the lower-frequency region, implying a lower interfacial charge resistance upon the addition of MXene into RHA. This outcome corresponded to the CA and LSV analysis findings, R-6MX showed a remarkable performance in terms of having the highest peak current density of 0.9454 mA/cm² and retention rate of 97.28%. Both of these values show that hybrid R-6MX was able to maintain a high current for the entire duration. The current is maintained in a stable form for some time, proving that R-6MX was the most stable, with a minimal corrosion reaction and tolerance in a methanol medium. The results from this study enabled an evaluation of the possibility of utilising low-cost, green RHA material for fuel cell applications to promote sustainability. The novelty of this work is that a cheap source of silica-based RHA, a type of waste material, is incorporated with MXene through hybridisation processes. Full article

This study describes the elaboration and characterization of plasticized starch composites based on lignocellulosic fibers. The transformation of native to plasticized starch (TPS) and the preparation of TPS blends were performed with a new lab-scale mixer based on an original concept. Firstly, the morphology and chemical composition of flax shives were analyzed to better understand the intrinsic properties of these natural fillers. Then, the impact of the processing parameters (temperature, speed screw) on the quality and the structural properties of plasticized starch were examined by SEM and DRX. After that, we focused on the elaboration of various formulations based on plasticized starch matrix by varying TPS formulation and filler content (from 10 to 30%). The viscoelastic and rheological properties of TPS/flax blends have been analyzed by TGA, SEM, and DMTA. Full article