A First-Principles Study of C3 N Nanostructures: Control and Engineering of the Electronic and Magnetic Properties of Nanosheets, Tubes and Ribbons.

Using first-principles calculations we systematically investigate the atomic, electronic and magnetic properties of novel two-dimensional materials (2DM) with a stoichiometry C3 N which has recently been synthesized. We investigate how the number of layers affect the electronic properties by considering monolayer, bilayer and trilayer structures, with different stacking of the layers. We find that a transition from semiconducting to metallic character occurs which could offer potential applications in future nanoelectronic devices. We also study the affect of width of C3 N nanoribbons, as well as the radius and length of C3 N nanotubes, on the atomic, electronic and magnetic properties. Our results show that these properties can be modified depending on these dimensions, and depend markedly on the nature of the edge states. Functionalization of the nanostructures by the adsorption of H adatoms is found induce metallic, half-metallic, semiconducting and ferromagnetic behavior, which offers an approach to tailor the properties, as can the application of strain. Our calculations give insight into this new family of C3 N nanostructures, which reveal unusual electronic and magnetic properties, and may have great potential in applications such as sensors, electronics and optoelectronic at the nanoscale.

Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation.

PURPOSE: The purpose of this study was to report procedures developed to annotate abdominal computed tomography (CT) images from subjects without pancreatic disease that will be used as the input for deep convolutional neural networks (DNN) for development of deep learning algorithms for automatic recognition of a normal pancreas. MATERIALS AND METHODS: Dual-phase contrast-enhanced volumetric CT acquired from 2005 to 2009 from potential kidney donors were retrospectively assessed. Four trained human annotators manually and sequentially annotated 22 structures in each datasets, then expert radiologists confirmed the annotation. For efficient annotation and data management, a commercial software package that supports three-dimensional segmentation was used. RESULTS: A total of 1150 dual-phase CT datasets from 575 subjects were annotated. There were 229 men and 346 women (mean age: 45±12years; range: 18-79years). The mean intra-observer intra-subject dual-phase CT volume difference of all annotated structures was 4.27mL (7.65%). The deep network prediction for multi-organ segmentation showed high fidelity with 89.4% and 1.29mm in terms of mean Dice similarity coefficients and mean surface distances, respectively. CONCLUSIONS: A reliable data collection/annotation process for abdominal structures was developed. This process can be used to generate large datasets appropriate for deep learning.

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features.

PURPOSE: The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Eighty-nine patients with AIP (65 men, 24 women; mean age, 59.7±13.9 [SD] years; range: 21-83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1±12.3 [SD] years; range: 36-86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5mm thickness/increment) were compared with thick-slices images (3 or 5mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing. RESULTS: The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8-100%), 83.9% (52:67; 95% CI: 74.7-93.0%) and 77.4% (48/62; 95% CI: 67.0-87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6-100%) and 100% specificity (33/33; 95% CI: 93-100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8-100%) and area under the curve of 0.975 (95% CI: 0.936-1.0). CONCLUSIONS: Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.

Ultrasound induced strain in ultrasmall CoFe2O4@polyvinyl alcohol nanocomposites.

Cobalt ferrite/Polyvinyl alcohol (PVA) nanocomposites were prepared with and without ultrasonic treatment by co-precipitation technique, characterized by X-ray diffraction (XRD), Vibrating sample magnetometer (VSM), Fourier transform infrared spectroscopy (FT-IR) and Transmission Electron Microscopy (TEM). The obtained results revealed that ultrasound irradiation induces strain in the structure and increases the magnetic anisotropy energy of the 7nm magnetic nanoparticles (MNPs). The diffraction peak broadening and shift to lower angle due to the lattice strain were analyzed by means of Williamson-Hall (W-H) method. TEM images also show that ultrasound irradiated sample contain nano-crystallite cobalt ferrite which have completely dispersed and embedded in PVA matrix. These ultrasmall MNPs whit nonzero coercivity are promising for high density data storage devices.

Magnetic properties of the spin S = 1/2 Heisenberg chain with hexamer modulation of exchange.

We consider the spin-1/2 Heisenberg chain with alternating spin exchange in the presence of additional modulation of exchange on odd bonds with period 3. We study the ground state magnetic phase diagram of this hexamer spin chain in the limit of very strong antiferromagnetic (AF) exchange on odd bonds using the numerical Lanczos method and bosonization approach. In the limit of strong magnetic field commensurate with the dominating AF exchange, the model is mapped onto an effective XXZ Heisenberg chain in the presence of uniform and spatially modulated fields, which is studied using the standard continuum-limit bosonization approach. In the absence of additional hexamer modulation, the model undergoes a quantum phase transition from a gapped phase into the only one gapless Lüttinger liquid (LL) phase by increasing the magnetic field. In the presence of hexamer modulation, two new gapped phases are identified in the ground state at magnetization equal to [Formula: see text] and [Formula: see text] of the saturation value. These phases reveal themselves also in the magnetization curve as plateaus at corresponding values of magnetization. As a result, the magnetic phase diagram of the hexamer chain shows seven different quantum phases, four gapped and three gapless, and the system is characterized by six critical fields which mark quantum phase transitions between the ordered gapped and the LL gapless phases.