Facile Control of C₂H₅OH Sensing Characteristics by Decorating Discrete Ag Nanoclusters on SnO₂ Nanowire Networks

The effect of Ag decoration on the gas sensing characteristics of SnO2 nanowire (NW) networks was investigated. The Ag layers with thicknesses of 5–50 nm were uniformly coated on the surface of SnO2 NWs via e-beam evaporation, which were converted into isolated or continuous configurations of Ag islands by heat treatment at 450 °C for 2 h. The SnO2 NWs decorated by isolated Ag nano-islands displayed a 3.7-fold enhancement in gas response to 100 ppm C2H5OH at 450 °C compared to pristine SnO2 NWs. In contrast, as the Ag decoration layers became continuous, the response to C2H5OH decreased significantly. The enhancement and deterioration of the C2H5OH sensing characteristics by the introduction of the Ag decoration layer were strongly governed by the morphological configurations of the Ag catalysts on SnO2 NWs and their sensitization mechanism

Highly Conductive Coaxial SnO₂−In₂O₃ Heterostructured Nanowires for Li Ion Battery Electrodes

Novel SnO2−In2O3 heterostructured nanowires were produced via a thermal evaporation method, and their possible nucleation/growth mechanism is proposed. We found that the electronic conductivity of the individual SnO2−In2O3 nanowires was 2 orders of magnitude better than that of the pure SnO2 nanowires, due to the formation of Sn-doped In2O3 caused by the incorporation of Sn into the In2O3 lattice during the nucleation and growth of the In2O3 shell nanostructures. This provides the SnO2−In2O3 nanowires with an outstanding lithium storage capacity, making them suitable for promising Li ion battery electrodes

Synergistic Integration of Machine Learning with Microstructure/Composition-Designed SnO₂ and WO₃ Breath Sensors

A high-performance semiconductor metal oxide gas sensing strategy is proposed for efficient sensor-based disease prediction by integrating a machine learning methodology with complementary sensor arrays composed of SnO2- and WO3-based sensors. The six sensors, including SnO2- and WO3-based sensors and neural network algorithms, were used to measure gas mixtures. The six constituent sensors were subjected to acetone and hydrogen environments to monitor the effect of diet and/or irritable bowel syndrome (IBS) under the interference of ethanol. The SnO2- and WO3-based sensors suffer from poor discrimination ability if sensors (a single sensor or multiple sensors) within the same group (SnO2- or WO3-based) are separately applied, even when deep learning is applied to enhance the sensing operation. However, hybrid integration is proven to be effective in discerning acetone from hydrogen even in a two-sensor configuration through the synergistic contribution of supervised learning, i.e., neural network approaches involving deep neural networks (DNNs) and convolutional neural networks (CNNs). DNN-based numeric data and CNN-based image data can be exploited for discriminating acetone and hydrogen, with the aim of predicting the status of an exercise-driven diet and IBS. The ramifications of the proposed hybrid sensor combinations and machine learning for the high-performance breath sensor domain are discussed

Production of transgenic pigs coexpressing hCD55, hCD59, and HT.

<p>(A) human CD55, CD59, and HT-transgenic piglets produced by SCNT. Left panel: female clones, middle and right panel: male reclones (B) Two different primer sets were used to genotype for the transgene. (C) M, size marker; PC, Triple plasmid vector as a positive control; 1–15 triple-transgenic piglets; N, non-transgenic pigs as a negative control.</p

Transgene expression levels at different organs of a transgenic pig.

<p>(A) Each value derived from transcripts of the transgene in various organs of a transgenic pig (#1), after normalization relative to β-Actin (internal control) genes, were compared with that of a human liver sample defined as 1. (B) The expression of hCD55, hCD59, and HT proteins was analyzed by Western blotting. The results showed that the transgenes were detectable in most, but not all organs examined (heart, liver, kidney, pancreas, spleen, and GB).</p

Immunohistochemical analysis in tissue sections of a transgenic piglet.

<p>Cryosections of the tissues were subjected to liver, heart, kidney and pancreas from a piglet. Frozen tissues were sectioned and immunostained with antibodies (green) as described under the Materials and Methods section. Nuclei were visualized by DAPI staining (blue). The images were taken using an inverted epifluorescence microscope. hCD55 and HT staining were barely detectable in tissue sections from non Tg, which were not included. Scale bar, 200 µm.</p

Primary Swine Umbilical Vein Endothelial Cells from triple-transgenic pigs.

<p>Primary cultured SUVECs were characterized by immunostaining with CD31 (red) and actin filaments (green), and the nuclei was counterstained with DAPI (blue).</p

The <i>in vitro</i> development of cloned embryos from normal and transgenic fetal fibroblasts.

<p>The number of replicates was 6. The cleavage and blastocyst rates were counted on day 2 and at day 7, respectively. Data expressed show mean values ± SEM.</p><p>There was no significant difference in developmental rates among the groups.</p

mRNA expression patterns of the transgenes in transgenic SUVECs.

<p>Scattered dot plots represent mRNA transcript levels for hCD55 (left), HT (middle), and hCD59 (right) in individual SUVECs. Each value derived from transcripts of the transgene in Tg SUVECs after normalization relative to β-Actin (internal control) genes, were compared with that of a HUVEC defined as 1.</p

Human serum-mediated cytolysis of SUVECs.

<p>SUVECs from non-Tg (n = 2) and Tg (n = 7) were incubated for 6 h in MEM culture medium containing serial dilutions of normal human serum. Cytolysis rate was calculated based on the counts of live and dead cells. Mean ± SEM tested in three independent experiments are presented (P<0.001).</p