Dataset of process-structure-property feature relationships for AlSi10Mg material fabricated using laser powder bed fusion additive manufacturing.

This dataset reports microstructure and mechanical property features of AlSi10Mg manufactured using laser powder bed fusion over a wide range of processing conditions. Samples were fabricated with different combinations of laser power, scan speed, and hatch spacing to probe dense regimes as well as porous samples resulting from keyholing and lack of fusion. Pore and grain/sub-grain features for each processing set were quantified. Sample porosity was measured using Archimedes density measurements and X-ray computed tomography (XCT). XCT was also used to characterize the surface roughness of samples along with pore size and morphology. Electron backscatter diffraction (EBSD) was used to characterize the grain size and morphology while scanning electron microscope (SEM) imaging and was used to measure solidification cell size. Uniaxial tension tests were performed to ascertain yield and ultimate tensile strengths, elongation, and elastic modulus, and microhardness was measured using Vickers indentation.

Dataset of process-structure-property feature relationship for laser powder bed fusion additive manufactured Ti-6Al-4V material.

The processing, structure, and property features for Ti-6Al-4V additively manufactured using laser powder bed fusion (L-PBF) over a range of processing parameter combinations are reported. In terms of processing, laser power and laser scanning speed were varied over a wide range, to investigate dense processing space as well as regimes likely to result in keyhole, lack of fusion, and beading up defects, which can occur during L-PBF. Archimedes measurements were used to measure porosity, while X-ray computed tomography (XCT) was used to quantify pore sizes, pore morphologies, and overall porosity, and finally, optical microscopy was used to quantify prior-ß grain characteristics. Average pore size and shape, porosity, prior- ß grain size and aspect ratio, and surface roughness for each processing parameter set are reported. Uniaxial tension tests and microhardness measurements were performed, with elastic modulus, yield strength, ultimate tensile strength, elongation to necking, elongation to fracture, and Vickers microhardness reported.

A transfer learning approach for improved classification of carbon nanomaterials from TEM images.

The extensive use of carbon nanomaterials such as carbon nanotubes/nanofibers (CNTs/CNFs) in industrial settings has raised concerns over the potential health risks associated with occupational exposure to these materials. These exposures are commonly in the form of CNT/CNF-containing aerosols, resulting in a need for a reliable structure classification protocol to perform meaningful exposure assessments. However, airborne carbonaceous nanomaterials are very likely to form mixtures of individual nano-sized particles and micron-sized agglomerates with complex structures and irregular shapes, making structure identification and classification extremely difficult. While manual classification from transmission electron microscopy (TEM) images is widely used, it is time-consuming due to the lack of automation tools for structure identification. In the present study, we applied a convolutional neural network (CNN) based machine learning and computer vision method to recognize and classify airborne CNT/CNF particles from TEM images. We introduced a transfer learning approach to represent images by hypercolumn vectors, which were clustered via K-means and processed into a Vector of Locally Aggregated Descriptors (VLAD) representation to train a softmax classifier with the gradient boosting algorithm. This method achieved 90.9% accuracy on the classification of a 4-class dataset and 84.5% accuracy on a more complex 8-class dataset. The developed model established a framework to automatically detect and classify complex carbon nanostructures with potential applications that extend to the automated structural classification for other nanomaterials.

Fertilization increases paddy soil organic carbon density.

Field experiments provide an opportunity to study the effects of fertilization on soil organic carbon (SOC) sequestration. We sampled soils from a long-term (25 years) paddy experiment in subtropical China. The experiment included eight treatments: (1) check, (2) PK, (3) NP, (4) NK, (5) NPK, (6) 7F:3M (N, P, K inorganic fertilizers+30% organic N), (7) 5F:5M (N, P, K inorganic fertilizers+50% organic N), (8) 3F:7M (N, P, K inorganic fertilizers+70% organic N). Fertilization increased SOC content in the plow layers compared to the non-fertilized check treatment. The SOC density in the top 100 cm of soil ranged from 73.12 to 91.36 Mg/ha. The SOC densities of all fertilizer treatments were greater than that of the check. Those treatments that combined inorganic fertilizers and organic amendments had greater SOC densities than those receiving only inorganic fertilizers. The SOC density was closely correlated to the sum of the soil carbon converted from organic amendments and rice residues. Carbon sequestration in paddy soils could be achieved by balanced and combined fertilization. Fertilization combining both inorganic fertilizers and organic amendments is an effective sustainable practice to sequestrate SOC.