Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys.

A deep learning framework is developed to predict the process-induced surface roughness of AlSi10Mg aluminum alloy fabricated using laser powder bed fusion (LPBF). The framework involves the fabrication of round bar AlSi10Mg specimens, surface topography measurement using 3D laser scanning profilometry, extraction, coupling, and streamlining of roughness and LPBF processing data, feature engineering to select the relevant feature set and the development, validation, and evaluation of a deep neural network model. A mix of core and contour-border scanning strategies are employed to fabricate four sets of specimens with different surface roughness conditions. The effects of different scanning strategies, linear energy density (LED), and specimen location on the build plate on the resulting surface roughness are discussed. The inputs to the deep neural network model are the AM process parameters (i.e., laser power, scanning speed, layer thickness, specimen location on the build plate, and the x,y grid location for surface topography measurements), and the output is the surface profile height measurements. The proposed deep learning framework successfully predicts the surface topography and related surface roughness parameters for all printed specimens. The predicted surface roughness ( S a ) measurements are well within 5% of experimental error for the majority of the cases. Moreover, the intensity and location of the surface peaks and valleys as well as their shapes are well predicted, as demonstrated by comparing roughness line scan results with corresponding experimental data. The successful implementation of the current framework encourages further applications of such machine learning-based methods toward AM material development and process optimization.

The synthesis of novel lanthanum hydroxyborate at extreme conditions.

The novel structure of lanthanum hydroxyborate La2B2O5(OH)2 was synthesized by the reaction of partially hydrolyzed lanthanum and boron oxide in a diamond anvil cell under high-pressure/high-temperature (HPHT) conditions of 30 GPa and ∼2,400 K. The single-crystal X-ray structure determination of the lanthanum hydroxyborate revealed: P3¯c1, a = 6.555(2) Å, c = 17.485(8) Å, Z = 6, R1 = 0.056. The three-dimensional structure consists of discrete planar BO3 groups and three crystallographically different La ions: one is surrounded by 9, one by 10, and one by 12 oxygen anions. The band gap was estimated using ab initio calculations to be 4.64 eV at ambient pressure and 5.26 eV at 30 GPa. The current work describes the novel HPHT lanthanum hydroxyborate with potential application as a deep-ultraviolet birefringent material.

New insights into submarine tailing disposal for a reduced environmental footprint: Lessons learnt from Norwegian fjords.

Submarine tailing disposal (STD) in fjords from land-based mines is common practice in Norway and takes place in other regions worldwide. We synthesize the results of a multidisciplinary programme on environmental impacts of STDs in Norwegian fjords, providing new knowledge that can be applied to assess and mitigate impact of tailing disposal globally, both for submarine and deep-sea activities. Detailed geological seafloor mapping provided data on natural sedimentation to monitor depositional processes on the seafloor. Modelling and analytical techniques were used to assess the behaviour of tailing particles and process-chemicals in the environment, providing novel tools for monitoring. Toxicity tests showed biological impacts on test species due to particulate and chemical exposure. Hypersedimentation mesocosm and field experiments showed a varying response on the benthos, allowing to determine the transition zone in the STD impact area. Recolonisation studies indicate that full community recovery and normalisation of metal leakage rates may take several decades due to bioturbation and slow burial of sulfidic tailings. The results are synthesised to provide guidelines for the development of best available techniques for STDs.

Catalytic Pathway of Nanozyme "Artificial Peroxidase" with 100-Fold Greater Bimolecular Rate Constants Compared to Those of the Enzyme.

We report on the kinetic mechanism of the catalytically synthesized Prussian Blue nanoparticles denoted as "artificial peroxidase". In contrast to the enzyme, whose active site first interacts with hydrogen peroxide forming the so-called Compound I, in the case of the nanozymes, H2O2 oxidizes their complex with reducing substrate. Slow release of the product (oxidized form of the latter) from the nanozymes has been registered. The interaction of substrates with the nanozymes is 100 times faster than with enzyme peroxidases, and the rate-limiting constant for the nanozymes is also 2 orders of magnitude greater: for pyrogallol k2 = 1.3 ± 0.1 × 108 M-1 s-1 and for ferrocyanide k2 = 1.9 ± 0.1 × 108 M-1 s-1. Thus, the discovered novel advantage of nanozymes over the corresponding enzymes is the 100-fold greater bimolecular rate constants, resulting, most probably, from their uniformly accessible surface, avoiding the effect of rotation on the diffusion-controlled rate.

Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan.

Number of cities worlwide experienced air quality improvements during COVID-19 lockdowns; however, such changes may have been different in places with major contributions from nontraffic related sources. In Almaty, a city-scale quarantine came into force on March 19, 2020, which was a week after the first COVID-19 case was registered in Kazakhstan. This study aims to analyze the effect of the lockdown from March 19 to April 14, 2020 (27 days), on the concentrations of air pollutants in Almaty. Daily concentrations of PM2.5, NO2, SO2, CO, O3, and BTEX were compared between the periods before and during the lockdown. During the lockdown, the PM2.5 concentration was reduced by 21% with spatial variations of 6-34% compared to the average on the same days in 2018-2019, and still, it exceeded WHO daily limit values for 18 days. There were also substantial reductions in CO and NO2 concentrations by 49% and 35%, respectively, but an increase in O3 levels by 15% compared to the prior 17 days before the lockdown. The concentrations of benzene and toluene were 2-3 times higher than those during in the same seasons of 2015-2019. The temporal reductions may not be directly attributed to the lockdown due to favorable meteorological variations during the period, but the spatial effects of the quarantine on the pollution levels are evidenced. The results demonstrate the impact of traffic on the complex nature of air pollution in Almaty, which is substantially contributed by various nontraffic related sources, mainly coal-fired combined heat and power plants and household heating systems, as well as possible small irregular sources such as garbage burning and bathhouses.