Effect of Alkaline Earth Metal on AZrOx (A = Mg, Sr, Ba) Memory Application

Zr can be stabilized by the element selected, such as Mg-stabilized Zr (MSZ), thus providing MSZ thin films with potentially wide applications and outstanding properties. This work employed the element from alkaline earth metal stabilized Zr to investigate the electrical properties of sol–gel AZrOx (A = alkaline earth metal; Mg, Sr, Ba) as dielectric layer in metal-insulator–metal resistive random-access memory devices. In addition, the Hume–Rothery rule was used to calculate the different atomic radii of elements. The results show that the hydrolyzed particles, surface roughness, and density of oxygen vacancy decreased with decreased difference in atomic radius between Zr and alkaline earth metal. The MgZrOx (MZO) thin film has fewer particles, smoother surface, and less density of oxygen vacancy than the SrZrOx (SZO) and BaZrOx (BZO) thin films, leading to the lower high resistance state (HRS) current and higher ON/OFF ratio. Thus, a suitable element selection for the sol–gel AZrOx memory devices is helpful for reducing the HRS current and improving the ON/OFF ratio. These results were obtained possibly because Mg has a similar atomic radius as Zr and the MgOx-stabilized ZrOx.

Iodine value of tung biodiesel fuel using Wijs method is significantly lower than calculated value

The tung tree (Vernicia fordii) is a non-edible oil plant native to southern China and was introduced in Japan in the nineteenth century. The tree produces tung oil, which is composed of approximately 80% α-eleostearic acid (9c, 11t, 13t-octadecatrienoic acid), 7% linoleic acid, and 6% oleic acid. Tung oil may be a non-edible source of biodiesel fuel (BDF) production. The iodine value (IV) is one of parameters to guarantee BDF quality, and the most common method for the determination of IV is the Wijs method. The IV can be calculated from the average molecular weight and the number of double bonds from the GC–MS data. In this study, the IVs of olive, castor, soybean, linseed, and perilla BDF using the Wijs method were found to be almost the same as the calculated IV. On the other hand, the IV of tung BDF by the Wijs method indicated a significantly lower value than that of the calculated value. To determine the cause of this discrepancy, the samples before and after halogenation using the Wijs method, were analyzed by 1H NMR. The conjugated double bond signals did not disappear, and a broad double bond signal remained in the tung BDF spectrum after halogenation. These results demonstrated that iodine, with a large atomic radius, could not react completely with the three conjugated double bonds in α-eleostearic acid. Therefore, the IV of tung BDF was significantly lower than the calculated value.

Local Lattice Distortion in High-Entropy Carbide Ceramics

Using the ab initio calculations, we study the lattice distortion of HfNbTaTiVC5, HfNbTaTiZrC5 and MoNbTaTiVC5 high-entropy carbide (HEC) ceramics. Results indicate that the Bader atomic radius and charge transfer in HECs is very close to those from binary carbide. The degree of lattice distortion strongly depends on the alloying element. The Bader atomic radius can excellently describe the lattice distortion in HEC. Further, the corresponding atomic radius and formation enthalpy of binary carbides may be indicators to predict the single-phase HECs.

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

Heavy Metals Removal from Aqueous Solutions by Multiwall Carbon Nanotubes: Effect of MWCNTs Dispersion

Carbon nanotubes (CNTs) are one of the most studied nanoparticles due to their physical, chemical and electronic properties. However, strong Van der Waals bonds, which promote CNTs aggregation are usually present, affecting their unique properties. Avoiding CNTs aggregation is one of the main difficulties when using these nanoparticles. Regarding the adsorption capacity of CNTs, the tendency of CNTs to aggregate decreases the surface area available to retain contaminants. One way to overcome this issue is by changing the surface energy of CNTs through chemical (covalent and noncovalent methods) or mechanical stabilization, but there is not yet a unique solution to solve this problem. In this work, a chemical noncovalent method (addition of surfactants) combined with mechanical energy (ultrasounds) was applied for CNTs stabilization, and the influence in heavy metal ions removal, Pb (II), Cu (II), Ni (II) and Zn (II), an area of high environmental relevance, was evaluated. It was proved that high amounts of metals could be removed from water during the first eighteen hours. Competitive adsorption between heavy metals, during adsorption tests with the simultaneous presence of all ions, was also studied and it was possible to prove that the electronegativity and atomic radius of cations influence their removal. Pb (II) and Cu (II) were the metals removed in higher percentages, and Ni (II) and Zn (II) were the metals less removed during competitive adsorption. Finally, the results obtained show that MWCNTs, if adequately dispersed, present a good solution for the treatment of water contaminated with highly toxic heavy metals, even when using very low concentrations of Multiwall Carbon Nanotubes (MWCNTs).

Design and Production of a New FeCoNiCrAlCu High-Entropy Alloy: Influence of Powder Production Method on Sintering

The structure of FeCoNiCrAl1.8Cu0.5 high-entropy alloys (HEA) obtained by two different routes has been studied. The selection of the composition has followed the Hume–Rothery approach in terms of number of itinerant electrons (e/a) and average atomic radius to control the formation of specific phases. The alloys were obtained either from a mixture of elemental powders or from gas-atomised powders, being consolidated in both cases by uniaxial pressing and vacuum sintering at temperatures of 1200 °C and 1300 °C. The characterization performed in the sintered samples from both types of powder includes scanning electron microscopy, X-ray diffraction, differential thermal analysis, and density measurements. It was found that the powder production techniques give similar phases content. However, the sintering at 1300 °C destroys the achieved phase stability of the samples. The phases identified by all techniques and confirmed by Thermo-Calc calculations are the following: a major Co-Ni-Al-rich (P1) BCC phase, which stays stable after 1300 °C sintering and homogenising TT treatments; a complex Cr-Fe-rich (P2) B2 type phase, which transforms into a sigma phase after the 1300 °C sintering and homogenising TT treatments; and a very minor Al-Cu-rich (P3) FCC phase, which also transforms into Domain II and Domain III phases during the heating at 1300 °C and homogenising TT treatments.

High impact on students’ understanding of atomics radius on crystals geometry concept through implementation of JITT with 3D animation

This study was conducted to analyze the students 'initial and final understanding after the application of JITT with 3D animation, to identify students' responses and arguments, and to determine the impact of using JITT with 3D animation. This research involved 43 students of the 6th semester of the 2019-2020 academic year of the Physics Education study program of the Universitas PGRI Kanjuruhan Malang who took solid state physics course. Students' initial and final understanding was analyzed through responses and arguments presented during the pretest, while the impact of JITT application with 3D animation was analyzed based on the results of the pretest and posttest as well as student responses during the learning process expressed through short interviews and discussions. The qualitative and quantitative data generated from the mixed-method approach were analyzed simultaneously. The results show that the students understand that the atomic radius for all the different crystal lattices is the same, namely a/2. This was awakened by an early understanding of the general definition of the radius. However, after following the JITT stages with 3D animation, their understanding changed that the atomic radius of each crystal lattice is different in length. In addition, the results of statistical analysis showed that there was a very significant increase in the students' mastery of concepts from an average of 26.9 to 96.7. Meanwhile, the N-gain value is very high, namely 0.96 in the very effective category, which illustrates that JITT with 3D animation has had a high impact on students' understanding of atomic radius in the concept of crystal geometry.

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.