Potential CO2 Emission Reduction by Development of Non-Grain-Based Bioethanol in China

Assessment of the potential CO2 emission reduction by development of non-grain-based ethanol in China is valuable for both setting up countermeasures against climate change and formulating bioethanol policies. Based on the land occupation property, feedstock classification and selection are conducted, identifying sweet sorghum, cassava, and sweet potato as plantation feedstocks cultivated from low-quality arable marginal land resources and molasses and agricultural straws as nonplantation feedstocks derived from agricultural by-products. The feedstock utilization degree, CO2 reduction coefficient of bioethanol, and assessment model of CO2 emission reduction potential of bioethanol are proposed and established to assess the potential CO2 emission reduction by development of non-grain-based bioethanol. The results show that China can obtain emission reduction potentials of 10.947 and 49.027 Mt CO2 with non-grain-based bioethanol in 2015 and 2030, which are much higher than the present capacity, calculated as 1.95 Mt. It is found that nonplantation feedstock can produce more bioethanol so as to obtain a higher potential than plantation feedstock in both 2015 and 2030. Another finding is that developing non-grain-based bioethanol can make only a limited contribution to China's greenhouse gas emission reduction. Moreover, this study reveals that the regions with low and very low potentials for emission reduction will dominate the spatial distribution in 2015, and regions with high and very high potentials will be the majority in 2030.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Enzyme Colorimetric Assay Using Unmodified Silver Nanoparticles

Colorimetric assay based on the unique surface plasmon resonance properties of metallic nanoparticles has received considerable attention in bioassay due to its simplicity, high sensitivity, and low cost. Most of colorimetric methods previously reported employed gold nanoparticles (GNPs) as sensing elements. In this work, we develop a sensitive, selective, simple, and label-free colorimetric assay using unmodified silver nanoparticle (AgNP) probes to detect enzymatic reactions. Enzymatic reactions concerning adenosine triphosphate (ATP) dephosphorylation by calf intestine alkaline phosphatase (CLAP) and peptide phosphorylation by protein kinase A (PKA) were studied.

Ultrasensitive colorimetric detection of protein by aptamer - Au nanoparticles conjugates based on a dot-blot assay

A simple, rapid and ultrasensitive colorimetric detection of protein using aptamer-Au nanoparticles (AuNPs) conjugates based on a dot-blot array has been developed, which was combined with the unique optical properties of AuNPs, enabling the visual detection of protein within minutes without any instrument.

Sensitive, Label-Free Protein Assay Using 1-ethyl-3-methylimidazolium Tetrafluoroborate Supported Microchip Electrophoresis with Laser-Induced Fluorescence Detection

Based on the dimer-monomer equilibrium movement of the fluorescent dye Pyronin Y (PY), a rapid, simple, highly sensitive, label-free method for protein detection was developed by microchip electrophoresis with LIF detection. PY formed a nonfluorescent dimer induced by the premicellar aggregation of an anionic surfactant, SDS, however, the fluorescence intensity of the system increased dramatically when proteins such as BSA, bovine hemoglobin, cytochrome c, and trypsin were added to the solution due to the transition of dimer to fluorescent monomer. Furthermore, 1-ethyl-3-methylimidazolium tetrafluoroborate (EMImBF(4)) instead of PBS was applied as running buffers in microchip electrophoresis.

Selective, peroxidase substrate based "signal-on" colorimetric assay for the detection of chromium (VI)

Due to the potentially adverse effects of the chromium (VI) on the human health and also on the environment, the quantitative determination of Cr(VI) is of particular interest. This work herein reports a facile, selective and rapid colorimetric determination of Cr(VI) based on the peroxidase substrate-2,2'-azino-bis(3-ethylbenzo-thiazoline-6-sulfonic acid) diammonium salt (ABTS) as the color developing agent. ABTS, which was usually acted as peroxidase substrate for the enzyme linked immunosorbent assay, is used here for the first time to fabricate the "signal-on" colorimetric Assay for Cr(VI).

Carbon nanotubes decorated with Pt nanocubes by a noncovalent functionalization method and their role in oxygen reduction

Combined with polymer wrapping and layer-by-layer techniques, a noncovalent functionalization method is developed to disperse Pt nanocubes (NCs) onto carbon nanotubes (CNTs). By adjusting the relative ratio of Pt NCs to CNTs, nanotubes with different Pt NC loadings are produced. The composites exhibit excellent electrocatalytic activity towards oxygen reduction.

Carbon-supported Pd nanocatalyst modified by non-metal phosphorus for the oxygen reduction reaction

A carbon-supported palladium catalyst modified by non-metal phosphorus(PdP/C) has been developed as an oxygen reduction catalyst for direct methanol fuel cells.The PdP/C catalyst was prepared by the sodium hypophosphite reduction method. The as-prepared Pd nanoparticles have a narrow size distribution with an average diameter of 2 nm. Energy dispersive X-ray analysis (EDX), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) results indicate that P enters into the crystal lattice of Pd and forms an alloy.

Gold nanoparticle-based surface enhanced Raman scattering spectroscopic assay for the detection of protein-protein interactions

In the present work, a sensitive spectroscopic assay based on surface-enhanced Raman spectroscopy (SERS) using gold nanoparticles as substrates was developed for the rapid detection protein-protein interactions. Detection is achieved by specific binding biotin-modification antibodies with protein-stabilized 30 nm gold nanoparticles, followed by the attachment of avidin-modification Raman-active dyes. As a proof-of-principle experiment, a well-known biomolecular recognition system, IgG with protein A, was chosen to establish this new spectroscopic assay. Highly selective recognition of IgG down to 1 ng/ml in solution has been demonstrated.

The synthesis of ionic-liquid-functionalized multiwalled carbon nanotubes decorated with highly dispersed Au nanoparticles and their use in oxygen reduction by electrocatalysis

Multiwalled carbon nanotube (MWCNT)/ionic liquid/gold nanoparticle hybrid materials have been prepared by a chemical route that involves functionalization of MWCNT with amine-terminated ionic liquids followed by deposition of Au. Transmission electron microscopy revealed well-distributed Au with a narrow size distribution centered around 3.3 nm. The identity of the hybrid material was confirmed through Raman and X-ray photoelectron spectroscopy.

Green synthesis of 1-2 nm gold nanoparticles stabilized by amine-terminated ionic liquid and their electrocatalytic activity in oxygen reduction

Stable gold nanoparticles with average size 1.7 nm synthesized by an amine-terminated ionic liquid showed enhanced electrocatalytic activity and high stability.