Oxidized Carbon Nitrides: Water‐Dispersible,Atomically Thin Carbon Nitride‐Based Nanodots and Their Performances as Bioimaging Probes

Three‐dimensional (3D) carbon nitride (C₃N₄)‐based materials show excellent performance in a wide range of applications because of their suitable band structures. To realize the great promise of two‐dimensional (2D) allotropes of various 3D materials, it is highly important to develop routes for the production of 2D C₃N₄ materials, which are one‐atom thick, in order to understand their intrinsic properties and identify their possible applications. In this work, water‐dispersible, atomically thin, and small carbon nitride nanodots were produced using the chemical oxidation of graphitic C₃N₄. Various analyses, including X‐ray diffraction, X‐ray photoelectron, Fourier‐transform infrared spectroscopy, and combustion‐based elemental analysis, and thermogravimetric analysis, confirmed the production of 3D oxidized C₃N₄ materials. The 2D C₃N₄ nanodots were successfully exfoliated as individual single layers; their lateral dimension was several tens of nanometers. They showed strong photoluminescence in the visible region as well as excellent performances as cell‐imaging probes in an in vitro study using confocal fluorescence microscopy.     

Liquid crystal aligning capabilities on surface-reformed indium-doped zinc oxide films via ion-beam exposure

We investigated the characteristics of a solution-processed indium-doped zinc oxide (In:ZnO) film formed via ion-beam (IB) irradiation as a liquid crystal (LC) alignment layer. The In:ZnO film was deposited using solution processing and cured at various temperatures. Uniform LC alignment was observed at all curing temperatures in cross-polarised optical microscopy images. A regular pre-tilt angle supported these results and showed homogeneous LC alignment. Several surface analyses were conducted to evaluate the effect of IB irradiation on the In:ZnO film surface. X-ray diffraction analysis showed an amorphous structure both before and after IB irradiation, and physical surface reformation was observed using atomic force microscopy. Root mean square surface roughness was reduced and a smooth surface was achieved after IB irradiation. X-ray photoelectron spectroscopy was used to detect chemical surface reformation. It was found that the IB irradiation broke the metal-oxide bonds and increased the occurrence of oxygen vacancies, which affected the van der Waals forces between the LC molecules and the In:ZnO film surface. Electrical performance was observed to identify the possibility of using the In:ZnO film in LC applications. Enhanced electro-optical performance was measured and zero residual DC voltage which was verified using a capacitance-voltage curve was achieved.     

The Origin of the Improved Efficiency and Stability of Triphenylamine‐Substituted Anthracene Derivatives for OLEDs: A Theoretical Investigation

Herein, we describe the molecular electronic structure, optical, and charge‐transport properties of anthracene derivatives computationally using density functional theory to understand the factors responsible for the improved efficiency and stability of organic light‐emitting diodes (OLEDs) with triphenylamine (TPA)‐substituted anthracene derivatives. The high performance of OLEDs with TPA‐substituted anthracene is revealed to derive from three original features in comparison with aryl‐substituted anthracene derivatives: 1) the HOMO and LUMO are localized separately on TPA and anthracene moieties, respectively, which leads to better stability of the OLEDs due to the more stable cation of TPA under a hole majority‐carrier environment; 2) the more balanceable hole and electron transport together with the easier hole injection leads to a larger rate of hole–electron recombination, which corresponds to the higher electroluminescence efficiency; and 3) the increasing reorganization energy for both hole and electron transport and the higher HOMO energy level provide a stable potential well for hole trapping, and then trapped holes induce a built‐in electric field to prompt the balance of charge‐carrier injection.     

Activation processes with memory

We propose a mathematical treatment of the activated processes governed by stochastic Langevin dynamics with a colored random force, corresponding to a noise generated by an Ornstein-Uhlenbeck process. Such non-Markovian dynamics take place in a variety of chemical and biological systems. Using the path integral approach, we constructed the conditional probability for passing between two stationary states in configurational space. Our relations can be used for Monte Carlo sampling of evolution trajectories for systems with many degrees of freedom as well as for determining the reaction coordinate used in transition state theory. On the basis of our relation for a conditional probability, we generalize the method of determining the most probable path to the case of colored random force. Using the simple three-hole potential, we examine numerically the effect of nonzero correlation time (memory) on the evolution of the most probable path for a finite temperature.     

Partially positively charged silver nanoparticles prepared by p-benzoquinone

Partially positively charged silver nanoparticles were successfully prepared by interaction between p-benzoquinone and the surface of the nanoparticles. This result was primarily due to electron affinity of the carbonyl group in p-benzoquinone, as confirmed by FT-IR and X-ray photoelectron spectroscopy (XPS). In this study, p-benzoquinone acted as both a stabilizer and a reducing agent for silver nanoparticles. UV–vis spectra showed the formation of silver nanoparticles. TEM micrographs confirmed that most of silver nanoparticles exist in sizes less than 7 nm, and the average size of particle aggregates is approximately 20 nm.     

Melampolides from the leaves of Smallanthus sonchifolius and their inhibitory activity of lps-induced nitric oxide production

Two new melampolide-type sesquiterpene lactones, 8beta-epoxyangeloyloxy-9alpha-ethoxy-14-oxo-acanthospermolide (1) and 8beta-angeloyloxy-9alpha-ethoxy-14-oxo-acanthospermolide (2), were isolated from the leaves of yacon [Smallanthus sonchifolia (POEPP. et ENDL.) H. Robinson] along with eleven known melampolides, allo-schkuhriolide (3), enhydrin (4), polymatin A (5), fluctuanin (6), 8beta-angeloyloxy-9alpha-acetoxy-14-oxo-acanthospermolide (7), 8beta-angeloyloxy-14-oxo-acanthospermolide (8), 8beta-methacryloyloxymelampolid-14-oic acid methyl ester (9), uvedalin (10), polymatin B (11), 8beta-tigloyloxymelampolid-14-oic acid methyl ester (12), and sonchifolin (13). Their structures were established on the basis of spectroscopic evidence including 1D- and 2D-NMR experiments. All isolates were evaluated for inhibition of LPS-induced nitric oxide production in murine macrophage RAW 264.7 cells.     

Ultrathin Free‐Standing Ternary‐Alloy Nanosheets

A synthesis strategy for the preparation of ultrathin free‐standing ternary‐alloy nanosheets is reported. Ultrathin Pd‐Pt‐Ag nanosheets with a thickness of approximately 3 nm were successfully prepared by co‐reduction of the metal precursors in an appropriate molar ratio in the presence of CO. Both the presence of CO and the interplay between the constituent metals provide fine control over the anisotropic two‐dimensional growth of the ternary‐alloy nanostructure. The prepared Pd‐Pt‐Ag nanosheets were superior catalysts of ethanol electrooxidation owing to their specific structural and compositional characteristics. This approach will pave the way for the design of multicomponent 2D nanomaterials with unprecedented functions.     

Sparse Penalized Forward Selection for Support Vector Classification

We propose a new binary classification and variable selection technique especially designed for high-dimensional predictors. Among many predictors, typically, only a small fraction of them have significant impact on prediction. In such a situation, more interpretable models with better prediction accuracy can be obtained by variable selection along with classification. By adding an ?₁-type penalty to the loss function, common classification methods such as logistic regression or support vector machines (SVM) can perform variable selection. Existing penalized SVM methods all attempt to jointly solve all the parameters involved in the penalization problem altogether. When data dimension is very high, the joint optimization problem is very complex and involves a lot of memory allocation. In this article, we propose a new penalized forward search technique that can reduce high-dimensional optimization problems to one-dimensional optimization by iterating the selection steps. The new algorithm can be regarded as a forward selection version of the penalized SVM and its variants. The advantage of optimizing in one dimension is that the location of the optimum solution can be obtained with intelligent search by exploiting convexity and a piecewise linear or quadratic structure of the criterion function. In each step, the predictor that is most able to predict the outcome is chosen in the model. The search is then repeatedly used in an iterative fashion until convergence occurs. Comparison of our new classification rule with ?₁-SVM and other common methods show very promising performance, in that the proposed method leads to much leaner models without compromising misclassification rates, particularly for high-dimensional predictors.