This study investigates the mechanical properties of coconut sawdust powder combined with polypropylene (PP). The effect of compatibility content, wood powder (WP) content, and injection molding parameters on the properties of coconut wood powder composite (WPC) is evaluated. The results could be used to figure out the optimal mechanical properties such as tensile strength, elongation, elastic modulus, and flexural strength by selecting suitable parameters and composition. The bonding between the WP particles and the PP matrix is good, and the WP is uniformly distributed across the composite matrix, as indicated in the scanning electron microscopy (SEM) results. Interestingly, with the presence of the compatibilizer oleamide, increasing the WP content from 20 wt.% to 40 wt.% did not result in WP accumulation in the composite matrix. Notably, at 20 wt.% WP, the elongation is the highest (at 7.40 wt.%), while at 30 wt.% WP, the elastic modulus reaches the highest value. The maximum ultimate tensile strength (UTS) value is obtained at 35 wt.% WP. Higher WP mostly results in greater flexural strength and shore D hardness. At 40 wt.% WP, the WPC achieves its peak shore D hardness of 77.6. The Taguchi results suggest that WP content is the most critical factor in the UTS value of coconut WPCs. The filling pressure ranks second, followed by the packing pressure. Finally, unlike the other characteristics, the melt temperature has a minimal impact on the UTS value.
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http://dx.doi.org/10.3390/polym16091225 | DOI Listing |
Sci Rep
December 2024
Department of Physics, Indian Institute of Technology, Patna, 801106, Bihar, India.
A highly effective method for creating a supramolecular metallogel of Ni(II) ions (NiA-TA) has been developed in our work. This approach uses benzene-1,3,5-tricarboxylic acid as a low molecular weight gelator (LMWG) in DMF solvent. Rheological studies assessed the mechanical properties of the Ni(II)-metallogel, revealing its angular frequency response and thixotropic behaviour.
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December 2024
School of Electrical Engineering, Aalto University, P.O. Box 15500, Aalto, FI-00076, Finland.
Engineering plastics are finding widespread applications across a broad temperature spectrum, with additive manufacturing (AM) having now become commonplace for producing aerospace-grade components from polymers. However, there is limited data available on the behavior of plastic AM parts exposed to elevated temperatures. This study focuses on investigating the tensile strength, tensile modulus and Poisson's ratio of parts manufactured using fused filament fabrication (FFF) and polyetheretherketone (PEEK) plastics doped with two additives: short carbon fibers (SCFs) and multi-wall carbon nanotubes (MWCNTs).
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December 2024
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, China.
The dolomite dust-emulsified asphalt composite (DAC) with excellent mechanical properties was successfully prepared using alkali activation. The effects of different alkali concentrations and emulsified asphalt contents on the mechanical properties of the materials were studied. And the micro-mechanisms of its mechanical performance changes were analyzed through SEM and XRD characterization.
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December 2024
Department of Mechanical Engineering, Sejong University, Seoul, Republic of Korea.
Nonthermal plasma has been extensively utilized in various biomedical fields, including surface engineering of medical implants to enhance their biocompatibility and osseointegration. To ensure robustness and cost effectiveness for commercial viability, stable and effective plasma is required, which can be achieved by reducing gas pressure in a controlled volume. Here, we explored the impact of reduced gas pressure on plasma properties, surface characteristics of plasma-treated implants, and subsequent biological outcomes.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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