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.

The Metastability of an Electrochemically Controlled Nanoscale Machine on Gold Surfaces

Abstract: The advent of supramolecular chemistry has provided chemists with the wherewithal to construct molecule-level machines 3] in an efficient manner using the protocol of template-direction. Synthetically accessible, linear motor molecules come in the guise of bistable [2]rotaxanes in which the ring component can be induced to move relative to the dumbbell-shaped one by altering the redox characteristics of the molecules. Such precisely controllable nanoscale molecular machines and switches have attracted a lot of attention 3] because of their potential to meet the expectations of a visionary and to act as some of the smallest components for the engineering of nanoelectromechanical systems (NEMs) and the fabrication of nanoelectronic devices. Although the redox-switching properties of numerous bistable [2]rotaxanes have been demonstrated in solution, the lack of coherence of the switches in this phase makes it difficult to harness the potential envisaged by Feynman. It is essential that we establish how to self-assemble these tiny switches in an orderly manner at surfaces and to investigate their switching properties in conjunction with their introduction into solid-state devices that have been shown to function as two-dimensional molecular electronic circuits. The fabrication of such devices required the design and synthesis of bistable [2]rotaxanes that are amphiphilic, so that they can be transferred 14±16] as molecular monolayers using the Langmuir ± Blodgett (LB) technique into a device setting. A molecular switch tunnel junction (MSTJ) has been fabricated by sandwiching such self-organized LB monolayers between a bottom Si electrode and a top Ti/Al electrode in a crossbar device architecture. The switch-on (high conductance) and switch-off (low conductance) states of each junction can be addressed respectively upon applying a 2 V or a 2 V bias. The proposed electromechanical switching mechanism (Figure 1) suggests that the ground state, where the cyclobis(paraquat-p-phenylene) (CBPQT , blue) ring initially encircles the tetrathiafulvalene (TTF, green) unit, represents the switch-off state. When a 2 V bias is applied, the CBPQT ring moves mechanically to the 1,5-dioxynaphthalene (DNP, red) ring system as a result of oxidation of the TTF unit to its radical cation. Although, when the bias is removed, neutrality is restored to the TTF unit, the CBPQT ring continues to reside on the DNP ring system, forming the metastable state. The observation of a switch-on state can be attributed to this slow-decaying metastable state that can be erased by applying a 2 V bias for a fleeting moment during the switching cycle. Since the mechanical motion associated with this decay is an activated process, these devices exhibit a hysteretic current ± voltage response. Herein, we describe how we have utilized a custom-designed variable temperature (VT) electrochemical apparatus to investigate the redox-switching behavior of an Au surface-confined linear motor-molecule, that is, a disulfide-tethered bistable [2]rotaxane SSR ¥ 4PF6, together with the corresponding dumbbell-shaped control compound SSD. In both cases, the appended disulfide function is used to immobilize the redox-active [2]rotaxane and dumbbell control onto gold surfaces as selfassembled monolayers (SAMs). The [2]rotaxane SSR ¥ 4PF6 was obtained (Figure 2) by a template-directed protocol wherein a CBPQT ring was clipped around the TTF unit of the dumbbellshaped precursor SSD. Here, we report i) the results of a semiquantitative electrochemical investigation carried out on the surface-confined SSR and the control (SSD) at room temperature in MeCN, leading to the identification of a [28] W. L. Jorgensen, J. Tirado-Rives, J. Am. Chem. Soc. 1988, 110, 1657. 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Profeta, P. Weiner, J. Am. Chem. Soc. 1984, 106, 765. [32] D. Fincham, D. Heyes, Adv. Chem. Phys. 1985, 63, 493. [33] M. Parrinello, A. Rahman, J. App. Phys. 1981, 52, 7182. [34] T. Darden, D. York, L. Pedersen, J. Chem. Phys. 1993, 98, 10089. [35] C. Zannoni, in The Molecular Physics of Liquid Crystals (Eds. : G. R. Luckhurst, G. W. Gray), Academic Press, London 1979, pp. 51 ± 83. [36] I. Haller, Prog. Solid State Chem. 1975, 10, 103. [37] K. Toyne, in Thermotropic Liquid Crystals (Ed. : G. W. Gray), Wiley, London, 28 ±63. [38] M. Hird, in Physical Properties of Liquid Crystals, Vol. 1: Nematics, (Eds. : D. A. Dunmur, A. Fukuda, G. R. Luckhurst), EMIS, IEE, London 2000, pp. 3 ± 16. [39] A. Ferrarini, P. L. Nordio, J. Chem. Soc. Perkin Trans. 1998, 2, 456. [40] M. E. Tuckerman, B. Berne, G. Martyna, J. Chem. Phys. 1992, 97, 1990.

Theory of the transmission properties of an optical far-field superlens for imaging beyond the diffraction limit

Abstract: A conventional optical superlens for imaging beyond the diffraction limit produces images only in the near-field zone of the superlens. In contrast, an optical far-field superlens (FSL) device has a remarkable transmission property that leads to a one-to-one relationship between the far-field and the near-field angular spectra. This property makes the device suitable for imaging beyond the diffraction limit from far-field measurement. This specific FSL is composed of a properly designed periodically corrugated metallic slab-based superlens. Through the numerical design and parameter study, we show that the transmission property of this FSL is based on a specific strong-broadband wavenumber excitation of surface-plasmon polaritons supported by the nanostructured metallic grating.

A Carpet Cloak Device for Visible Light

Abstract: We report an invisibility carpet cloak device, which is capable of making an object undetectable by visible light. The cloak is designed using quasi conformal mapping and is fabricated in a silicon nitride waveguide on a specially developed nano-porous silicon oxide substrate with a very low refractive index. The spatial index variation is realized by etching holes of various sizes in the nitride layer at deep subwavelength scale creating a local effective medium index. The fabricated device demonstrates wideband invisibility throughout the visible spectrum with low loss. This silicon nitride on low index substrate can also be a general scheme for implementation of transformation optical devices at visible frequency.

Measurement of pion, kaon and proton production in proton–proton collisions at √s = 7 TeV

Abstract: The measurement of primary [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] production at mid-rapidity ([Formula: see text] 0.5) in proton-proton collisions at [Formula: see text][Formula: see text] 7 TeV performed with a large ion collider experiment at the large hadron collider (LHC) is reported. Particle identification is performed using the specific ionisation energy-loss and time-of-flight information, the ring-imaging Cherenkov technique and the kink-topology identification of weak decays of charged kaons. Transverse momentum spectra are measured from 0.1 up to 3 GeV/[Formula: see text] for pions, from 0.2 up to 6 GeV/[Formula: see text] for kaons and from 0.3 up to 6 GeV/[Formula: see text] for protons. The measured spectra and particle ratios are compared with quantum chromodynamics-inspired models, tuned to reproduce also the earlier measurements performed at the LHC. Furthermore, the integrated particle yields and ratios as well as the average transverse momenta are compared with results at lower collision energies.

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.