Nuno Correia

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency.

This study explores the preparation of lithium iron phosphate (LFP) electrodes for lithium-ion batteries (LIBs), focusing on electrode loadings, dispersion techniques, and drying methods. Using a three-roll mill for LFP slurry dispersion, good electrochemical properties were achieved with loadings of 5–8 mg·cm⁻² (0.8–1.2 mAh·cm⁻² areal capacity). Adding polyvinylidene fluoride (PVDF) during the final milling stage reduced performance due to premature solidification in-between rolls. Vacuum-free drying improved ionic conductivity, stability against lithium metal, and discharge capacity, whereas vacuum-dried samples exhibited higher initial resistance and lower capacity retention. These findings highlight critical parameters for enhancing LFP electrode performance, paving the way for high-performance, and sustainable energy-storage solutions.

Increasing economic and environmental concerns arising from the extensive exploration and dependence on fossil fuel-based materials have encouraged the search for eco-friendly alternatives. Fibers based on biomass-derived materials have been attracting growing interest. Among other features, the mechanical performance of bio-based fibers needs to be improved to effectively compete with their counterparts and emerge as viable substitutes. This review presents scientific advancements in the development of naturally derived fibers, and strategies for their production with tailored mechanical properties. The potential of natural precursor-based fibers for their conversion into high-performance carbon fibers is also emphasized. Studies reporting the mechanical properties of bio-based fibers developed by wet spinning are identified, analyzed, and discussed. These studies show that cellulose is the most studied material, while Ioncell technology is identified as the most suitable method for producing cellulose-based fibers with the highest tensile strength. Studies have also demonstrated that silk fibroin exhibits tensile strength and elongation at break ranging from 300 to 600 MPa and 30 to 50%. Although several novel processes have been explored, there are still challenges that need to be addressed for bio-based fibers to become feasible options, and to boost their usage across industries.