Belarusian National Technical University

Carbon-filled polylactic acid produced by fused deposition modeling is a sustainable feedstock for lightweight structural parts, but performance depends on process-controlled porosity and interlayer fusion. The coupled effects of layer thickness, infill density, and print-path speed on simultaneous tensile, impact, and hardness performance in carbon–PLA systems remain insufficiently quantified. This study aims to define a multi-response analysis for carbon–PLA FDM. Eighteen coupon sets were fabricated in a factorial design spanning 0.15-0.25 mm layer thickness, 20-60

A special investigation was carried out to characterize the differences in microstructure, heat treatment response, and mechanical properties of alloys fabricated by different additive manufacturing (AM) processes. A systematic study was conducted base on a high γ′-content AM-ed Ni superalloy AMSC-DB fabricated by Laser Powder Bed Fusion (L-PBF) and Laser Direct Energy Deposition (L-DED) processes. Microstructural results show that the L-PBF sample exhibits refined grains, suppression of precipitation mechanism of the γ′ phase and carbides formation, and significant accumulation of residual stress compared with the as-printed L-DED sample, primarily due to the extremely rapid cooling rate in the L-PBF process. Based on the microstructural differences, the heat treatment processes have been optimized accordingly, which manage to manipulate the precipitation and recrystallization behavior in the formed layers. The coarser grains and carbides in the L-DED samples were shown to effectively hinder crack propagation along grain boundaries, thereby yielding superior creep-rupture performance at 900 °C/200 MPa compared with the case of L-PBF sample. As a result, this research shed light on the design of heat treatment processes for the used type of compositionally complex Ni superalloys and the application of AM technology/material combinations for the two most widely applied AM techniques with intensive laser heating.

Proton-exchange membrane fuel cell and water electrolyzer (PEMFC and PEMWE) with high conversion efficiency and zero-carbon emission stand out as an attractive strategy for efficient conversion between hydrogen energy and renewable electricity. As a key component, efficient oxygen electrocatalyst for promoting sluggish reaction kinetics of oxygen reduction and evolution reaction (ORR and OER) under harsh operation conditions severely limited progress of these devices. Among various candidates, Pt-group (Pt, Ir, and Ru)-based electrocatalysts are still the most active ORR/OER catalysts. However, the scarcity, high cost, and questionable stability restrict the widespread applications and the commercialization of PEMWE/PEMFC. Progresses in synthesizing atomically dispersed single/multiple-atom catalysts (SACs/MACs) offer new opportunities to Pt-group ORR/OER catalysts owing to nearly 100% metal utilization and high catalytic activities. Extensive efforts have been continuously devoted to optimizing the local structure of Pt-group OER/ORR catalysts at atom-level for further enhancing stability and activity. In this review, universal synthesis methods to prepare Pt-group SACs are discussed first, highlighting crucial factors which affect the structure and catalytic performance. Afterward, advanced characterization techniques for directly confirming atomic dispersed metal atoms were introduced, including aberration-corrected high-angle-annular-dark-field scanning transmission electron microscopy and X-ray absorption spectroscopy. Importantly, considerations for rational catalyst design and typical Pt-group SACs/MACs are summarized regarding the regulation strategy of atomically dispersed metal sites and various supports, and effects of metal-support interaction on the catalytic performance. Finally, key challenges and proposed perspectives for future development of atomically dispersed Pt-group oxygen electrocatalysts for fuel cell and electrolyzer are briefly discussed.

The goal of the research is to present the results of algorithms, that enable the detection of various types of anomalous values, and the construction of a Poincar’e ellipse with different color coding for anomalies. To achieve the stated goal, the Wolfram Mathematica computer system is used. This system offers a wide range of capabilities for analyzing anomalous values, using optimized algorithms. The considered functions allow solving real-world problems and creating applications for processing data of various natures, including the detection of anomalous values in samples of different types. The analysis of values revealed non-extreme anomalous values. Ignoring such values or using them in analysis can distort conclusions if these values are not detected and a decision on how to handle them within a specific task is not made. Furthermore, non-extreme anomalous values may indicate new, unexplored patterns. Extreme anomalous values, on the other hand, more often indicate problems in the data collection system or exceptional external events. During processing, such values are almost always removed. Non-extreme anomalies are cleaned if they are errors, or retained and accounted for if they are part of a real phenomenon.