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In this work, a tung oil-based thermosetting resin was synthesized via free radical polymerization and reinforced with thirteen different types of sand. The viability of this process inspired the adaptation of the resin for its use as a binder material in binder jetting, an additive manufacturing process. Firstly, it was shown that the resin could have its initial viscosity (~0.33 cP) increased upon heating to attain values compatible to existing printing systems. The curing kinetics of the resin was assessed via dielectric analysis (DEA), combining the utilization of heat and ultraviolet (UV) light, showing that a resin with a viscosity of 10 cP can be fully cured after 250 min at 90 ◦C, or 300 min at 75 ◦C, both under a 365 nm light exposure. Preliminary binder-jet tests successfully provided a solid object, which was post-cured, resulting in a hard specimen. The results presented herein suggest that the tung oil-based resin in question is a suitable bio-based binder for binder-jet 3D-printing applications. The novelty of the work reported lies in the conversion of an already established and effective bio-based thermosetting resin into a versatile photocurable binder that can be irrestrictively used with unsorted sands of different composition, making this technology broadly applicable to different isolated regions, using local resources available. The technology presented herein is potentially transformative and impactful, as binder jetting is typically associated to extremely well-sorted particles. 

In binder jet additive manufacturing (BJAM), uniformity and density of the powder layer impact green part quality. This study investigates the printability of unrefined sand using counter-roller spreading. Altair EDEM, a high-performance software powered by the Discrete Element Method (DEM), was used to simulate the BJAM process to evaluate powder bed homogeneity and density under various operating conditions, including roller rotational speed, traverse speed, powder layer thickness, and roller diameter. Utilizing high-performance computing (HPC) and graphics processing unit (GPU) clusters, time-efficient, and more realistic, simulations were performed simulating 300,000 grains. Detailed DEM simulations were executed by reconstructing representative particle shapes using two-dimensional images obtained using particle characterization equipment. The results highlight roller velocity and powder layer thickness as key determinants of sand spreadability. Optimal powder bed density (PBD) was achieved at a roller velocity of 20 mm/s with minimal deviation. A layer thickness exceeding 200 micrometers was found to prevent jamming and void formation, while percolation led to size segregation. The findings indicate that producing uniform and dense layers of unrefined sand is feasible but may incur trade-offs in print resolution and increased printing times. This work contributes to the advancement of sustainable and/or remote BJAM technologies, ensuring progress in both environmental sustainability and accessibility. 

Catalytic torrefaction using potassium carbonate (K2CO3) impregnation is a pretreatment method demonstrated to catalyze wood powder’s thermal degradation for energy use. In this study, beech wood boards were impregnated with K2CO3, with the aim to scale up from the studies on wood powder found in the literature. The beech boards were impregnated with five different concentrations and torrefied at 300◦C for four durations (5–60 min). The impregnation procedure was successful with a linear increase of K content in wood from 0.103 wt% for raw to 0.207 wt% for the 0.012 M sample. The weight loss during torrefaction increased with the increasing potassium (K) content in wood, reaching a maximum increase of 27.17% between 0.012 M and washed (no K2CO3) after 30 min. For the longest duration, the extent of the catalytic action of K decreased, similar to what is observed in wood powder. After 60 min torrefaction, potassium increased the torrefaction severity index by up to 10% and the higher heating value (HHV) by up to 55%. Potassium efficiently increasedthe fixed carbon and decreased the volatile matter to values comparable to coal by catalyzing the devolatilization during torrefaction. The atomic H/C and O/C ratios shifted to similar ratios as coal. The energy yield (EY) was above 80% for the shorter durations but dropped drastically at 30 and 60 min torrefaction. The prediction of the solid yield (SY), energy yield (EY), and enhancement factor of the HHV (EF) through an artificial neural network was robust with a fit quality R2≥0.999. The proposed method for catalytic torrefaction on wood boards was efficient and could be used prior to grinding and transportation for bioenergy production. This process could decrease the production costs of biomass fuel to compete with fossil fuels.