Adsorption of heavy metals on natural zeolites: A review.

Water pollution has jeopardized human health, and a safe supply of drinking water has been recognized as a worldwide issue. The increase in the accumulation of heavy metals in water from different sources has led to the search for efficient and environmentally friendly treatment methods and materials for their removal. Natural zeolites are promising materials for removing heavy metals from different sources contaminating the water. It is important to know the structure, chemistry, and performance of the removal of heavy metals from water, of the natural zeolites to design water treatment processes. This review focuses on critical analyses of the application of distinct natural zeolites for the adsorption of heavy metals from water, specifically, arsenic (As(III), As(V)), cadmium (Cd(II)), chromium (Cr(III), Cr(VI)), lead (Pb(II)), mercury(Hg(II)) and nickel (Ni(II)). The reported results of heavy-metal removal by natural zeolites are summarized, and the chemical modification of natural zeolites by acid/base/salt reagent, surfactants, and metallic reagents has been analyzed, compared, and described. Furthermore, the adsorption/desorption capacity, systems, operating parameters, isotherms, and kinetics for natural zeolites were described and compared. According to the analysis, clinoptilolite is the most applied natural zeolite to remove heavy metals. It is effective in removing As, Cd, Cr, Pb, Hg, and Ni. Additionally, an interesting fact is a variation between the natural zeolites from different geological origins regarding the sorption properties and capacities for heavy metals suggesting that natural zeolites from different regions of the world are unique.

Microstructural Characterization and Mechanical Properties of L-PBF Processed 316 L at Cryogenic Temperature.

Laser powder bed fusion (L-PBF) has attracted great interest in the aerospace and medical sectors because it can produce complex and lightweight parts with high accuracy. Austenitic stainless steel alloy 316 L is widely used in many applications due to its good mechanical properties and high corrosion resistance over a wide temperature range. In this study, L-PBF-processed 316 L was investigated for its suitability in aerospace applications at cryogenic service temperatures and the behavior at cryogenic temperature was compared with room temperature to understand the properties and microstructural changes within this temperature range. Tensile tests were performed at room temperature and at -196 °C to study the mechanical performance and phase changes. The microstructure and fracture surfaces were characterized using scanning electron microscopy, and the phases were analyzed by X-ray diffraction. The results showed a significant increase in the strength of 316 L at -196 °C, while its ductility remained at an acceptable level. The results indicated the formation of ε and α martensite during cryogenic testing, which explained the increase in strength. Nanoindentation revealed different hardness values, indicating the different mechanical properties of austenite (γ), strained austenite, body-centered cubic martensite (α), and hexagonal close-packed martensite (ε) formed during the tensile tests due to mechanical deformation.

Defects in Electron Beam Melted Ti-6Al-4V: Fatigue Life Prediction Using Experimental Data and Extreme Value Statistics.

Electron beam melting is a powder bed fusion (PBF) additive manufacturing (AM) method for metals offering opportunities for the reduction of material waste and freedom of design, but unfortunately also suffering from material defects from production. The stochastic nature of defect formation leads to a scatter in the fatigue performance of the material, preventing wider use of this production method for fatigue critical components. In this work, fatigue test data from electron beam melted Ti-6Al-4V specimens machined from as-built material are compared to deterministic fatigue crack growth calculations and probabilistically modeled fatigue life. X-ray computed tomography (XCT) data evaluated using extreme value statistics are used as the model input. Results show that the probabilistic model is able to provide a good conservative life estimate, as well as accurate predictive scatter bands. It is also shown that the use of XCT-data as the model input is feasible, requiring little investigated material volume for model calibration.