Effect of substrate temperature on structural, optical and electrical properties of ZnO thin films deposited by pulsed laser deposition

ZnO thin films were grown by the pulse laser deposition (PLD) method using Si (100) substrates at various substrate temperatures. The influence of the substrate temperature on the structural, optical, and electrical properties of the ZnO thin films was investigated. All of the thin films showed c-axis growth perpendicular to the substrate surface. At a substrate temperature of 500 °C, the ZnO thin film showed the highest (002) peak with a full width at half maximum (FWHM) of 0.39°. The X-ray Photoelectron Spectroscopy (XPS) study showed that Zn was in excess irrespective of the substrate temperature and that the thin film had a nearly stoichiometrical composition at a substrate temperature of 500 °C. The photoluminescence (PL) investigation showed that the narrowest UV FWHM of 15.8 nm and the largest ratio of the UV peak to the deep-level peak of 32.9 were observed at 500 °C. Hall effect measurement systems provided information about the carrier concentration, mobility and resistivity. At a substrate temperature of 500 °C, the Hall mobility was the value of 37.4 cm²/Vs with carrier concentration of 1.36 × 10¹⁸ cm⁻³ and resistivity of 2.08 × 10⁻¹ Ω cm.     

Advances in Soft Materials for Sustainable Electronics

In the current shift from conventional fossil-fuel-based materials to renewable energy,ecofriendly mate-rials have attracted extensive research interest due to ...     

Dynamic response analysis of pyroelectric sensitive element for thermal imaging

Temperature distributions under periodic thermal excitations and the responsivity of a pyroelectric device consisting of a cover layer, infrared absorber, metal contact, sensitive pyroelectric element, interconnecting column, and bulk silicon are found. Some results of numerical thermal modeling and analysis of exact expressions for a few extreme cases are presented. Pyroelectric responses of real structures are compared with the response of a single pyroelectric element in air as a limiting case of maximum sensitivity. The analytical approximations and numerical simulation show that the frequency response of the multilayered structure consists of different parts with simple frequency dependencies. In the region of high frequencies of light modulation, the responsivity is proportional to /spl omega//sup -1/, at low frequencies /spl sim/ /spl omega//sup -0.5/, and, in the region of intermediate frequencies, the voltage responsivity is independent of frequency.     

Development of a biodegradable sirolimus-eluting stent coated by ultrasonic atomizing spray

In this study, poly(D,L lactic-co-glycolic acid) (PLGA) was used as a drug carrier to generate two types of stents loaded with different concentrations of sirolimus. These stents were prepared by ultrasonic atomizing spray coating. Ultrasonic atomizing spray nozzle uses a low-pressure air/gas to produce a soft, highly focused beam of small spray drops. An isolated hypotube delivers liquid to the nozzle's atomizing surface while air/gas, delivered through the nozzle orifice at a fixed low pressure, shapes the atomized drops into a very precise, targeted spray. The stent was moved both in the traverse direction and rotated during the spraying process. The morphology of the sirolimus-eluting stents was examined by scanning electron microscopy (SEM) which indicated that the coating was very smooth and uniform. The coating was found to have the ability to withstand the compressive and tensile strains imparted without cracking during the stent inflation process. Release profile of sirolimus was measured by high performance liquid chromatography (HPLC). The release behavior of sirolimus from the stent surface had a two phase release profile with a burst release period of about 2 days, followed by a sustained and slow release phase. The mass loss behavior of PLGA appeared linear throughout most of the degradation period. At 28 days, neointimal formation was found to be significantly decreased for both sirolimus-eluting stents as compared to bare-metal stents (BMS). Assessment of vascular healing revealed an absence of increased inflammation in both sirolimus-eluting stents. Inflammation is commonly observed in drug-eluting stents (DES) with nonbiodegradable polymeric coatings. Taking these results into account, these novel sirolimus-eluting stents may be good candidates to resolve in-stent restenosis.     

Cactus‐Like Hollow Cu2‐xS@Ru Nanoplates as Excellent and Robust Electrocatalysts for the Alkaline Hydrogen Evolution Reaction

The development of Pt‐free electrocatalysts for the hydrogen evolution reaction (HER) recently is a focus of great interest. While several strategies are developed to control the structural properties of non‐Pt catalysts and boost their electrocatalytic activities for the HER, the generation of highly reactive defects or interfaces by combining a metal with other metals, or with metal oxides/sulfides, can lead to notably enhanced catalytic performance. Herein, the preparation of cactus‐like hollow Cu_2‐x S@Ru nanoplates (NPs) that contain metal/metal sulfide heterojunctions and show excellent catalytic activity and durability for the HER in alkaline media is reported. The initial formation of Ru islands on presynthesized Cu_1.94S NPs, via cation exchange between three Cu⁺ ions and one Ru³⁺, induces the growth of the Ru phase, which is concomitant with the dissolution of the Cu_1.94S nanotemplate, culminating in the formation of a hollow nanostructure with numerous thin Ru pillars. Hollow Cu_2‐x S@Ru NPs exhibit a small overpotential of 82 mV at a current density of ?10 mA cm^?2 and a low Tafel slope of 48 mV dec^?1 under alkaline conditions; this catalyst is among state‐of‐the‐art HER electrocatalysts in alkaline media. The excellent performance of hollow Cu_2‐x S@Ru NPs originates from the facile dissociation of water in the Volmer step.     

Content-based image retrieval using joint correlograms

The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.

A Fully-Human Antibody Specifically Targeting a Membrane-Bound Fragment of CADM1 Potentiates the T Cell-Mediated Death of Human Small-Cell Lung Cancer Cells

Small-cell lung cancer (SCLC) is the most aggressive form of lung cancer and the leading cause of global cancer-related mortality. Despite the earlier identification of membrane-proximal cleavage of cell adhesion molecule 1 (CADM1) in cancers, the role of the membrane-bound fragment of CAMD1 (MF-CADM1) is yet to be clearly identified. In this study, we first isolated MF-CADM1-specific fully human single-chain variable fragments (scFvs) from the human synthetic scFv antibody library using the phage display technology. Following the selected scFv conversion to human immunoglobulin G1 (IgG1) scFv-Fc antibodies (K103.1–4), multiple characterization studies, including antibody cross-species reactivity, purity, production yield, and binding affinity, were verified. Finally, via intensive in vitro efficacy and toxicity evaluation studies, we identified K103.3 as a lead antibody that potently promotes the death of human SCLC cell lines, including NCI-H69, NCI-H146, and NCI-H187, by activated Jurkat T cells without severe endothelial toxicity. Taken together, these findings suggest that antibody-based targeting of MF-CADM1 may be an effective strategy to potentiate T cell-mediated SCLC death, and MF-CADM1 may be a novel potential therapeutic target in SCLC for antibody therapy.     

HDAC8-Selective Inhibition by PCI-34051 Enhances the Anticancer Effects of ACY-241 in Ovarian Cancer Cells

HDAC6 is overexpressed in ovarian cancer and is known to be correlated with tumorigenesis. Accordingly, ACY-241, a selective HDAC6 inhibitor, is currently under clinical trial and has been tested in combination with various drugs. HDAC8, another member of the HDAC family, has recently gained attention as a novel target for cancer therapy. Here, we evaluated the synergistic anticancer effects of PCI-34051 and ACY-241 in ovarian cancer. Among various ovarian cancer cells, PCI-34051 effectively suppresses cell proliferation in wild-type p53 ovarian cancer cells compared with mutant p53 ovarian cancer cells. In ovarian cancer cells harboring wild-type p53, PCI-34051 in combination with ACY-241 synergistically represses cell proliferation, enhances apoptosis, and suppresses cell migration. The expression of pro-apoptotic proteins is synergistically upregulated, whereas the expressions of anti-apoptotic proteins and metastasis-associated proteins are significantly downregulated in combination treatment. Furthermore, the level of acetyl-p53 at K381 is synergistically upregulated upon combination treatment. Overall, co-inhibition of HDAC6 and HDAC8 through selective inhibitors synergistically suppresses cancer cell proliferation and metastasis in p53 wild-type ovarian cancer cells. These results suggest a novel approach to treating ovarian cancer patients and the therapeutic potential in developing HDAC6/8 dual inhibitors.     

Composition pattern oriented tag extraction from short documents using a structural learning method

With the rapid growth of web, automatic tagging that detects informative terms from a document becomes an important problem for information aggregation and sharing services. In particular, automatic tagging for short documents becomes more interesting as many users are increasingly publishing information through social media services which encourage users to create the documents of short length. In this paper, we propose a novel automatic tagging model for short text documents from social media services, following the framework of supervised learning. We redefine traditional frequency-based term features so that they can address the properties of the documents created under length limitation and consider sequential dependencies between successive terms in a document based on a structural support vector machine. In addition, our proposed approach incorporates composition patterns by which users put informative terms into their documents. Extensive experiments have been conducted to validate the presented approach, and it was found that the proposed term features were effective for extracting tags, and the tag extractor trained by considering the sequential dependencies and composition patterns achieved superior performance results over the existing alternative methods.