Xiang Zhang

Bio: Xiang Zhang is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 154, co-authored 1733 publications receiving 117576 citations. Previous affiliations of Xiang Zhang include University of California, Berkeley & University of Texas MD Anderson Cancer Center.

Polypyrrole-Coated Granules for Humic Acid Removal

Abstract: Granules with positive surface charges were prepared by coating glass beads with polypyrrole (PPy). The coated glass beads were found to possess high positive zeta potentials over a wide range of pH values. Batch and fixed bed humic acid adsorption experiments using the coated glass beads as adsorbents were conducted. Scanning electron microscope (SEM) was used to examine the surface morphology of the coated glass beads before and after humic acid adsorption. X-ray photoelectron spectroscopy (XPS) was applied to assess the protonation and charge transfer of the PPy coating with and without adsorbed humic acid. SEM images showed that the PPy coating considerably increased the surface roughness of the granules and a significant amount of humic acid was adsorbed on the PPy coating. From XPS analysis, it was found that 28% of the nitrogen atoms in the PPy coating were protonated, leading to a highly positively charged surface at pH<10.5. The results also showed that the amount of protonated nitrogen atoms decreased by up to 25% due to humic acid adsorption, suggesting that humic acid uptake by the PPy-coated glass beads was affected at least partly by charge neutralization. Humic acid adsorption also resulted in a reverse of the positive zeta potential of the PPy coating, indicating the importance of macromolecular adsorption in the process. The adsorption equilibrium data can be fitted with either the Langmuir or the Freundlich expression. Both pH and ionic concentration were found to affect the extent of humic acid adsorption by the PPy-coated granules.

Electrospun Fe2O3–carbon composite nanofibers as durable anode materials for lithium ion batteries

Abstract: Combination of metal oxides and carbon has been a favourable practice for their applications in high-rate energy storage mesoscopic electrodes. We report quasi 1D Fe2O3–carbon composite nanofibers obtained by the electrospinning method, and evaluate them as anodes for Li ion batteries. In the half-cell configuration, the anode exhibits a reversible capacity of 820 mA h g−1 at a current rate of 0.2 C up to 100 cycles. At a higher current density of 5 C, the cells still exhibit a specific capacity of 262 mA h g−1. Compared to pure electrospun Fe2O3 nanofibers, the capacity retention of Fe2O3–C composite nanofiber electrodes is drastically improved. The good electrochemical performance is associated with the homogenous dispersed Fe2O3 nanocrystals on the carbon nanofiber support. Such a structure prevents the aggregation of active materials, maintains the structure integrity and thus enhances the electronic conductivity during lithium insertion and extraction.

Production of K∗(892)0 and ϕ (1020) in p–Pb collisions at √sNN = 5.02 TeV

Abstract: The production of K$^{*}$(892)$^{0}$ and $\phi$(1020) mesons has been measured in p-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV. K$^{*0}$ and $\phi$ are reconstructed via their decay into charged hadrons with the ALICE detector in the rapidity range $-0.5 < y <0$. The transverse momentum spectra, measured as a function of the multiplicity, have p$_{\mathrm{T}}$ range from 0 to 15 GeV/$c$ for K$^{*0}$ and from 0.3 to 21 GeV/$c$ for $\phi$. Integrated yields, mean transverse momenta and particle ratios are reported and compared with results in pp collisions at $\sqrt{s}$ = 7 TeV and Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 2.76 TeV. In Pb-Pb and p-Pb collisions, K$^{*0}$ and $\phi$ probe the hadronic phase of the system and contribute to the study of particle formation mechanisms by comparison with other identified hadrons. For this purpose, the mean transverse momenta and the differential proton-to-$\phi$ ratio are discussed as a function of the multiplicity of the event. The short-lived K$^{*0}$ is measured to investigate re-scattering effects, believed to be related to the size of the system and to the lifetime of the hadronic phase.

Adiabatic elimination-based coupling control in densely packed subwavelength waveguides.

Abstract: The ability to control light propagation in photonic integrated circuits is at the foundation of modern light-based communication. However, the inherent crosstalk in densely packed waveguides and the lack of robust control of the coupling are a major roadblock toward ultra-high density photonic integrated circuits. As a result, the diffraction limit is often considered as the lower bound for ultra-dense silicon photonics circuits. Here we experimentally demonstrate an active control of the coupling between two closely packed waveguides via the interaction with a decoupled waveguide. This control scheme is analogous to the adiabatic elimination, a well-known procedure in atomic physics. This approach offers an attractive solution for ultra-dense integrated nanophotonics for light-based communications and integrated quantum computing.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Deep Residual Learning for Image Recognition

Abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.