Thermal and Mechanical Properties of Reprocessed Polylactide/Titanium Dioxide Nanocomposites for Material Extrusion Additive Manufacturing.

Polylactic acid (PLA) is a biodegradable polymer that can replace petroleum-based polymers and is widely used in material extrusion additive manufacturing (AM). The reprocessing of PLA leads to a downcycling of its properties, so strategies are being sought to counteract this effect, such as blending with virgin material or creating nanocomposites. Thus, two sets of nanocomposites based respectively on virgin PLA and a blend of PLA and reprocessed PLA (rPLA) with the addition of 0, 3, and 7 wt% of titanium dioxide nanoparticles (TiO2) were created via a double screw extruder system. All blends were used for material extrusion for 3D printing directly from pellets without difficulty. Scanning electron micrographs of fractured samples' surfaces indicate that the nanoparticles gathered in agglomerations in some blends, which were well dispersed in the polymer matrix. The thermal stability and degree of crystallinity for every set of nanocomposites have a rising tendency with increasing nanoparticle concentration. The glass transition and melting temperatures of PLA/TiO2 and PLA/rPLA/TiO2 do not differ much. Tensile testing showed that although reprocessed material implies a detriment to the mechanical properties, in the specimens with 7% nano-TiO2, this effect is counteracted, reaching values like those of virgin PLA.

Manufacture and Characterization of Polylactic Acid Filaments Recycled from Real Waste for 3D Printing.

This paper studies the thermal, morphological, and mechanical properties of 3D-printed polylactic acid (PLA) blends of virgin and recycled material in the following proportions: 100/0, 25/75, 50/50, and 75/25, respectively. Real waste, used as recycled content, was shredded and sorted by size without a washing step. Regular dog-bone specimens were 3D printed from filaments, manufactured in a single screw extruder. Thermogravimetric analysis indicated that adding PLA debris to raw material did not significantly impact the thermal stability of the 3D-printed samples and showed that virgin and recycled PLA degraded at almost the same temperature. Differential scanning calorimetry revealed a significant reduction in crystallinity with increasing recycled content. Scanning electron microscopy showed a more homogenous structure for specimens from 100% pure PLA, as well as a more heterogeneous one for PLA blends. The tensile strength of the PLA blends increased by adding more recycled material, from 44.20 ± 2.18 MPa for primary PLA to 52.61 ± 2.28 MPa for the blend with the highest secondary PLA content. However, this study suggests that the mechanical properties of the reprocessed parts and their basic association are unique compared with those made up of virgin material.

A Machine Learning Approach for Modelling Cold-Rolling Curves for Various Stainless Steels.

Stainless steel is a cold-work-hardened material. The degree and mechanism of hardening depend on the grade and family of the steel. This characteristic has a direct effect on the mechanical behaviour of stainless steel when it is cold-formed. Since cold rolling is one of the most widespread processes for manufacturing flat stainless steel products, the prediction of their strain-hardening mechanical properties is of great importance to materials engineering. This work uses artificial neural networks (ANNs) to forecast the mechanical properties of the stainless steel as a function of the chemical composition and the applied cold thickness reduction. Multiple linear regression (MLR) is also used as a benchmark model. To achieve this, both traditional and new-generation austenitic, ferritic, and duplex stainless steel sheets are cold-rolled at a laboratory scale with different thickness reductions after the industrial intermediate annealing stage. Subsequently, the mechanical properties of the cold-rolled sheets are determined by tensile tests, and the experimental cold-rolling curves are drawn based on those results. A database is created from these curves to generate a model applying machine learning techniques to predict the values of the tensile strength (Rm), yield strength (Rp), hardness (H), and elongation (A) based on the chemical composition and the applied cold thickness reduction. These models can be used as supporting tools for designing and developing new stainless steel grades and/or adjusting cold-forming processes.

Effect of Thermal and Hydrothermal Accelerated Aging on 3D Printed Polylactic Acid.

In the new transformation of 'Industry 4.0', additive manufacturing technologies have become one of the fastest developed industries, with polylactic acid (PLA) playing a significant role. However, there is an increasing amount of garbage generated during the printing process and after prototypes or end-of-life parts. Re-3D printing is one way to recycle PLA waste from fused filament fabrication. To do this process successfully, the properties of the waste mixture should be known. Previous studies have found that PLA degrades hydrolytically, but the time at which this process occurs for 3D printed products is not specified. This work aims to establish the baseline of the degradation kinetics of 3D printed PLA products to predict the service time until which these properties are retained. To achieve this, 3D printed specimens were thermally and hydrothermally aged during several time intervals. Thermal and mechanical properties were also determined. This study reveals that tensile strength decreases after 1344 h of hydrothermal ageing, simulating 1.5-2.5 years of real service time. PLA therefore has the same thermo-mechanical properties before reaching 1.5-years of age, so it could be recycled.

Bismuth incorporation and the role of ordering in GaAsBi/GaAs structures.

The structure and composition of single GaAsBi/GaAs epilayers grown by molecular beam epitaxy were investigated by optical and transmission electron microscopy techniques. Firstly, the GaAsBi layers exhibit two distinct regions and a varying Bi composition profile in the growth direction. In the lower (25 nm) region, the Bi content decays exponentially from an initial maximum value, while the upper region comprises an almost constant Bi content until the end of the layer. Secondly, despite the relatively low Bi content, CuPtB-type ordering was observed both in electron diffraction patterns and in fast Fourier transform reconstructions from high-resolution transmission electron microscopy images. The estimation of the long-range ordering parameter and the development of ordering maps by using geometrical phase algorithms indicate a direct connection between the solubility of Bi and the amount of ordering. The occurrence of both phase separation and atomic ordering has a significant effect on the optical properties of these layers. PACS: 78.55.Cr III-V semiconductors; 68.55.Nq composition and phase identification; 68.55.Ln defects and impurities: doping, implantation, distribution, concentration, etc; 64.75.St phase separation and segregation in.

Impact of N on the atomic-scale Sb distribution in quaternary GaAsSbN-capped InAs quantum dots.

The use of GaAsSbN capping layers on InAs/GaAs quantum dots (QDs) has recently been proposed for micro- and optoelectronic applications for their ability to independently tailor electron and hole confinement potentials. However, there is a lack of knowledge about the structural and compositional changes associated with the process of simultaneous Sb and N incorporation. In the present work, we have characterized using transmission electron microscopy techniques the effects of adding N in the GaAsSb/InAs/GaAs QD system. Firstly, strain maps of the regions away from the InAs QDs had revealed a huge reduction of the strain fields with the N incorporation but a higher inhomogeneity, which points to a composition modulation enhancement with the presence of Sb-rich and Sb-poor regions in the range of a few nanometers. On the other hand, the average strain in the QDs and surroundings is also similar in both cases. It could be explained by the accumulation of Sb above the QDs, compensating the tensile strain induced by the N incorporation together with an In-Ga intermixing inhibition. Indeed, compositional maps of column resolution from aberration-corrected Z-contrast images confirmed that the addition of N enhances the preferential deposition of Sb above the InAs QD, giving rise to an undulation of the growth front. As an outcome, the strong redshift in the photoluminescence spectrum of the GaAsSbN sample cannot be attributed only to the N-related reduction of the conduction band offset but also to an enhancement of the effect of Sb on the QD band structure.

Calculation of integrated intensities in aberration-corrected Z-contrast images.

Inclusion of spatial incoherence has been shown to give quantitative agreement between non-aberration-corrected high-angle annular dark-field scanning transmission electron microscopy images and theoretical simulations. Here we show that, using the same approach, a significant improvement in the correlation between calculated and experimental normalized integrated intensities is obtained in the InAsP ternary semiconductor alloy, but residual discrepancies remain. We have demonstrated, in good agreement with experimental intensities obtained in calibrated samples, that normalized integrated intensities show a low dependence on the sample thickness over a wide range of thickness values. This behaviour does not occur in conventional (non-aberration-corrected) images and constitutes a powerful tool for straightforward interpretation of high-resolution images in terms of atomic column-resolved compositional maps.

A method to determine the strain and nucleation sites of stacked nano-objects.

We determine the compositional distribution with atomic column resolution in a horizontal nanowire from the analysis of aberration-corrected high resolution Z-contrast images. The strain field in a layer capping the analysed nanowire is determined by anisotropic elastic theory from the resulting compositional map. The reported method allows preferential nucleation sites for epitaxial nanowires to be predicted with high spatial resolution, as required for accurate tuning of desired optical properties. The application of this method has been exemplified in this work for stacked InAs(P) horizontal nanowires grown on InP separated by 3 nm thick InP layers, but we propose it as a general method to be applied to other stacked nano-objects.