Largely Enhanced Ferromagnetism in Bare CuO Nanoparticles by a Small Size Effect

Insights into the Synthesis Parameters Effects on the Structural, Morphological, and Magnetic Properties of Copper Oxide Nanoparticles

The present study aims at the integration of the "oxalic conversion" route into "green chemistry" for the synthesis of copper oxide nanoparticles (CuO-NPs) with controllable structural, morphological, and magnetic properties. Two oxalate-containing precursors (H₂C₂O₄.2H₂O and (NH₄)₂C₂O₄.H₂O) and different volume ratios of a mixed water/glycerol solvent were tested. First, the copper oxalates were synthesized and then subjected to thermal decomposition in air at 400 °C to produce the CuO powders. The purity of the samples was confirmed by X-ray powder diffraction (XRPD), and the crystallite sizes were calculated using the Scherrer method. The transmission electron microscopy (TEM) images revealed oval-shaped CuO-NPs, and the scanning electron microscopy (SEM) showed that morphological features of copper oxalate precursors and their corresponding oxides were affected by the glycerol (V/V) ratio as well as the type of C₂O₄^2- starting material. The magnetic properties of CuO-NPs were determined by measuring the temperature-dependent magnetization and the hysteresis curves at 5 and 300 K. The obtained results indicate the simultaneous coexistence of dominant antiferromagnetic and weak ferromagnetic behavior.

Observation of Cu Spin Fluctuations in High-Tc Cuprate Superconductor Nanoparticles Investigated by Muon Spin Relaxation

The nano-size effects of high-T_c cuprate superconductor La_2-xSr_xCuO₄ with x = 0.20 are investigated using X-ray diffractometry, Transmission electron microscopy, and muon-spin relaxation (μSR). It is investigated whether an increase in the bond distance of Cu and O atoms in the conducting layer compared to those of the bulk state might affect its physical and magnetic properties. The μSR measurements revealed the slowing down of Cu spin fluctuations in La_2-xSr_xCuO₄ nanoparticles, indicating the development of a magnetic correlation at low temperatures. The magnetic correlation strengthens as the particle size reduces. This significantly differs from those observed in the bulk form, which show a superconducting state below T_c. It is indicated that reducing the particle size of La_2-xSr_xCuO₄ down to nanometer size causes the appearance of magnetism. The magnetism enhances with decreasing particle size.

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Metal Deposition Induced by the Step Region of Si (111)-(7 × 7) Surface

Scanning tunneling microscope results showed that Au and Fe atoms were steamed on the Si (111)-(7 × 7) substrate surface, with or without the step region. The experimental comparison proved that the induced effect of the step region is a controllable process, which CH3OH can adjust. In this paper, the latest progress on the dynamic phenomenon on the step region is discussed, including three deposition types: strong deposition, weak deposition, and the new quasi deposition. With a relatively weak interaction between Au and Si atoms, the linearity of the weak deposition is present in the step region. In contrast, Fe atoms tend to form a strong deposition along the boundary line between the flat and step regions. Different depositions correspond to different surface potential energy: a newly formed surface is stabilized by a quasi-potential made by breaking, and a metal atomic structure can be stabilized by forming several quasi depositions. After discussing the good adsorption properties, CH3OH can be used as an intermediate layer on the step region. As an important result of quasi deposition, a regular linear Fe cluster structure is created, which is perpendicular to the boundary line.