Showing papers by "Georgia Institute of Technology published in 2010"

Emerging applications of stimuli-responsive polymer materials

Abstract: Responsive polymer materials can adapt to surrounding environments, regulate transport of ions and molecules, change wettability and adhesion of different species on external stimuli, or convert chemical and biochemical signals into optical, electrical, thermal and mechanical signals, and vice versa. These materials are playing an increasingly important part in a diverse range of applications, such as drug delivery, diagnostics, tissue engineering and 'smart' optical systems, as well as biosensors, microelectromechanical systems, coatings and textiles. We review recent advances and challenges in the developments towards applications of stimuli-responsive polymeric materials that are self-assembled from nanostructured building blocks. We also provide a critical outline of emerging developments.

Shell-isolated nanoparticle-enhanced Raman spectroscopy

Abstract: Surface-enhanced Raman scattering (SERS) is a powerful spectroscopy technique that can provide non-destructive and ultra-sensitive characterization down to single molecular level, comparable to single-molecule fluorescence spectroscopy. However, generally substrates based on metals such as Ag, Au and Cu, either with roughened surfaces or in the form of nanoparticles, are required to realise a substantial SERS effect, and this has severely limited the breadth of practical applications of SERS. A number of approaches have extended the technique to non-traditional substrates, most notably tip-enhanced Raman spectroscopy (TERS) where the probed substance (molecule or material surface) can be on a generic substrate and where a nanoscale gold tip above the substrate acts as the Raman signal amplifier. The drawback is that the total Raman scattering signal from the tip area is rather weak, thus limiting TERS studies to molecules with large Raman cross-sections. Here, we report an approach, which we name shell-isolated nanoparticle-enhanced Raman spectroscopy, in which the Raman signal amplification is provided by gold nanoparticles with an ultrathin silica or alumina shell. A monolayer of such nanoparticles is spread as 'smart dust' over the surface that is to be probed. The ultrathin coating keeps the nanoparticles from agglomerating, separates them from direct contact with the probed material and allows the nanoparticles to conform to different contours of substrates. High-quality Raman spectra were obtained on various molecules adsorbed at Pt and Au single-crystal surfaces and from Si surfaces with hydrogen monolayers. These measurements and our studies on yeast cells and citrus fruits with pesticide residues illustrate that our method significantly expands the flexibility of SERS for useful applications in the materials and life sciences, as well as for the inspection of food safety, drugs, explosives and environment pollutants.

High-performance lithium-ion anodes using a hierarchical bottom-up approach

Abstract: Si-based Li-ion battery anodes have recently received great attention, as they offer specific capacity an order of magnitude beyond that of conventional graphite. The applications of this transformative technology require synthesis routes capable of producing safe and easy-to-handle anode particles with low volume changes and stable performance during battery operation. Herein, we report a large-scale hierarchical bottom-up assembly route for the formation of Si on the nanoscale--containing rigid and robust spheres with irregular channels for rapid access of Li ions into the particle bulk. Large Si volume changes on Li insertion and extraction are accommodated by the particle's internal porosity. Reversible capacities over five times higher than that of the state-of-the-art anodes (1,950 mA h g(-1)) and stable performance are attained. The synthesis process is simple, low-cost, safe and broadly applicable, providing new avenues for the rational engineering of electrode materials with enhanced conductivity and power.

Missing heritability and strategies for finding the underlying causes of complex disease

Abstract: Although recent genome-wide studies have provided valuable insights into the genetic basis of human disease, they have explained relatively little of the heritability of most complex traits, and the variants identified through these studies have small effect sizes. This has led to the important and hotly debated issue of where the 'missing heritability' of complex diseases might be found. Here, seven leading geneticists offer their opinion about where this heritability is likely to lie, what this could tell us about the underlying genetic architecture of common diseases and how this could inform research strategies for uncovering genetic risk factors.

Lattice-Boltzmann Method for Complex Flows

Abstract: With its roots in kinetic theory and the cellular automaton concept, the lattice-Boltzmann (LB) equation can be used to obtain continuum flow quantities from simple and local update rules based on particle interactions. The simplicity of formulation and its versatility explain the rapid expansion of the LB method to applications in complex and multiscale flows. We review many significant developments over the past decade with specific examples. Some of the most active developments include the entropic LB method and the application of the LB method to turbulent flow, multiphase flow, and deformable particle and fiber suspensions. Hybrid methods based on the combination of the Eulerian lattice with a Lagrangian grid system for the simulation of moving deformable boundaries show promise for more efficient applications to a broader class of problems. We also discuss higherorder boundary conditions and the simulation of microchannel flow with finite Knudsen number. Additionally, the remarkable scalability of the LB method for parallel processing is shown with examples. Teraflop simulations with the LB method are routine, and there is no doubt that this method will be one of the first candidates for petaflop computational fluid dynamics in the near future.

Semiconductor Nanocrystals: Structure, Properties, and Band Gap Engineering

Abstract: Semiconductor nanocrystals are tiny light-emitting particles on the nanometer scale. Researchers have studied these particles intensely and have developed them for broad applications in solar energy conversion, optoelectronic devices, molecular and cellular imaging, and ultrasensitive detection. A major feature of semiconductor nanocrystals is the quantum confinement effect, which leads to spatial enclosure of the electronic charge carriers within the nanocrystal. Because of this effect, researchers can use the size and shape of these “artificial atoms” to widely and precisely tune the energy of discrete electronic energy states and optical transitions. As a result, researchers can tune the light emission from these particles throughout the ultraviolet, visible, near-infrared, and mid-infrared spectral ranges. These particles also span the transition between small molecules and bulk crystals, instilling novel optical properties such as carrier multiplication, single-particle blinking, and spectral diffusi...

Self-powered nanowire devices.

Abstract: The lateral and vertical integration of ZnO piezoelectric nanowires allows for voltage and power outputs sufficient to power nanowire-based sensors.

The ns-3 Network Simulator

Abstract: As networks of computing devices grow larger and more complex, the need for highly accurate and scalable network simulation technologies becomes critical. Despite the emergence of large-scale testbeds for network research, simulation still plays a vital role in terms of scalability (both in size and in experimental speed), reproducibility, rapid prototyping, and education. With simulation based studies, the approach can be studied in detail at varying scales, with varying data applications, varying field conditions, and will result in reproducible and analyzable results.

Ab initio Gene Identification in Metagenomic Sequences

Abstract: We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes.

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

Abstract: Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling strategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its band limit in hertz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling rate is exponentially lower than the Nyquist rate of W hertz. In contrast to Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.

On ideal lattices and learning with errors over rings

Abstract: The “learning with errors” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount of noise, from truly uniform ones. The problem has been shown to be as hard as worst-case lattice problems, and in recent years it has served as the foundation for a plethora of cryptographic applications. Unfortunately, these applications are rather inefficient due to an inherent quadratic overhead in the use of LWE. A main open question was whether LWE and its applications could be made truly efficient by exploiting extra algebraic structure, as was done for lattice-based hash functions (and related primitives). We resolve this question in the affirmative by introducing an algebraic variant of LWE called ring-LWE, and proving that it too enjoys very strong hardness guarantees. Specifically, we show that the ring-LWE distribution is pseudorandom, assuming that worst-case problems on ideal lattices are hard for polynomial-time quantum algorithms. Applications include the first truly practical lattice-based public-key cryptosystem with an efficient security reduction; moreover, many of the other applications of LWE can be made much more efficient through the use of ring-LWE. Finally, the algebraic structure of ring-LWE might lead to new cryptographic applications previously not known to be based on LWE.

n-Type organic semiconductors in organic electronics.

Abstract: Organic semiconductors have been the subject of intensive academic and commercial interest over the past two decades, and successful commercial devices incorporating them are slowly beginning to enter the market. Much of the focus has been on the development of hole transporting, or p-type, semiconductors that have seen a dramatic rise in performance over the last decade. Much less attention has been devoted to electron transporting, or so called n-type, materials, and in this paper we focus upon recent developments in several classes of n-type materials and the design guidelines used to develop them.

Toward Efficient Binders for Li-Ion Battery Si-Based Anodes: Polyacrylic Acid

Abstract: Si-based Li-ion battery anodes offer specific capacity an order of magnitude beyond that of conventional graphite. However, the formation of stable Si anodes is a challenge because of significant volume changes occuring during their electrochemical alloying and dealloying with Li. Binder selection and optimization may allow significant improvements in the stability of Si-based anodes. Most studies of Si anodes have involved the use of carboxymethylcellulose (CMC) and poly(vinylidene fluoride) (PVDF) binders. Herein, we show for the first time that pure poly(acrylic acid) (PAA), possessing certain mechanical properties comparable to those of CMC but containing a higher concentration of carboxylic functional groups, may offer superior performance as a binder for Si anodes. We further show the positive impact of carbon coating on the stability of the anode. The carbon-coated Si nanopowder anodes, tested between 0.01 and 1 V vs Li/Li+ and containing as little as 15 wt % of PAA, showed excellent stability duri...

Functional and evolutionary insights from the genomes of three parasitoid Nasonia species.

Abstract: We report here genome sequences and comparative analyses of three closely related parasitoid wasps: Nasonia vitripennis, N. giraulti, and N. longicornis. Parasitoids are important regulators of arthropod populations, including major agricultural pests and disease vectors, and Nasonia is an emerging genetic model, particularly for evolutionary and developmental genetics. Key findings include the identification of a functional DNA methylation tool kit; hymenopteran-specific genes including diverse venoms; lateral gene transfers among Pox viruses, Wolbachia, and Nasonia; and the rapid evolution of genes involved in nuclear-mitochondrial interactions that are implicated in speciation. Newly developed genome resources advance Nasonia for genetic research, accelerate mapping and cloning of quantitative trait loci, and will ultimately provide tools and knowledge for further increasing the utility of parasitoids as pest insect-control agents.

Efficient hierarchical graph-based video segmentation

Abstract: We present an efficient and scalable technique for spatiotemporal segmentation of long video sequences using a hierarchical graph-based algorithm. We begin by over-segmenting a volumetric video graph into space-time regions grouped by appearance. We then construct a “region graph” over the obtained segmentation and iteratively repeat this process over multiple levels to create a tree of spatio-temporal segmentations. This hierarchical approach generates high quality segmentations, which are temporally coherent with stable region boundaries, and allows subsequent applications to choose from varying levels of granularity. We further improve segmentation quality by using dense optical flow to guide temporal connections in the initial graph. We also propose two novel approaches to improve the scalability of our technique: (a) a parallel out-of-core algorithm that can process volumes much larger than an in-core algorithm, and (b) a clip-based processing algorithm that divides the video into overlapping clips in time, and segments them successively while enforcing consistency. We demonstrate hierarchical segmentations on video shots as long as 40 seconds, and even support a streaming mode for arbitrarily long videos, albeit without the ability to process them hierarchically.

Dissolving polymer microneedle patches for influenza vaccination

Abstract: Influenza prophylaxis would benefit from a vaccination method enabling simplified logistics and improved immunogenicity without the dangers posed by hypodermic needles. Here we introduce dissolving microneedle patches for influenza vaccination using a simple patch-based system that targets delivery to skin's antigen-presenting cells. Microneedles were fabricated using a biocompatible polymer encapsulating inactivated influenza virus vaccine for insertion and dissolution in the skin within minutes. Microneedle vaccination generated robust antibody and cellular immune responses in mice that provided complete protection against lethal challenge. Compared to conventional intramuscular injection, microneedle vaccination resulted in more efficient lung virus clearance and enhanced cellular recall responses after challenge. These results suggest that dissolving microneedle patches can provide a new technology for simpler and safer vaccination with improved immunogenicity that could facilitate increased vaccination coverage.

Uncovering social spammers: social honeypots + machine learning

Abstract: Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.). Based on these profile features, we develop machine learning based classifiers for identifying previously unknown spammers with high precision and a low rate of false positives.

Flexible High-Output Nanogenerator Based on Lateral ZnO Nanowire Array

Abstract: We report here a simple and effective approach, named scalable sweeping-printing-method, for fabricating flexible high-output nanogenerator (HONG) that can effectively harvesting mechanical energy for driving a small commercial electronic component. The technique consists of two main steps. In the first step, the vertically aligned ZnO nanowires (NWs) are transferred to a receiving substrate to form horizontally aligned arrays. Then, parallel stripe type of electrodes are deposited to connect all of the NWs together. Using a single layer of HONG structure, an open-circuit voltage of up to 2.03 V and a peak output power density of approximately 11 mW/cm(3) have been achieved. The generated electric energy was effectively stored by utilizing capacitors, and it was successfully used to light up a commercial light-emitting diode (LED), which is a landmark progress toward building self-powered devices by harvesting energy from the environment. This research opens up the path for practical applications of nanowire-based piezoelectric nanogeneragtors for self-powered nanosystems.

Sedentary Behavior: Emerging Evidence for a New Health Risk

Abstract: Compared with our parents or grandparents, we are spending increasing amounts of time in environments that not only limit physical activity but require prolonged sitting—at work, at home, and in our cars and communities.1 Work sites, schools, homes, and public spaces have been (and continue to be) re-engineered in ways that minimize human movement and muscular activity. These changes have a dual effect on human behavior: people move less and sit more. From an evolutionary perspective, humans were designed to move—to locomote and engage in all manner of manual labor throughout the day. This was essential to our survival as a species. The recent shift from a physically demanding life to one with few physical challenges has been sudden, occurring during a tiny fraction of human existence. Societal indicators of reductions in human energy expenditure and increases in sedentary behavior during the past several decades are particularly striking. In 1970, 2 in 10 working Americans were in jobs requiring only light activity (predominantly sitting at a desk), whereas 3 in 10 were in jobs requiring high-energy output (eg, construction, manufacturing, farming).2 By 2000, more than 4 in 10 adults were in light-activity jobs, whereas 2 in 10 were in high-activity jobs.2 Moreover, during the past 20 years, total screen time (ie, using computers, watching television, playing video games) has increased dramatically. In 2003, nearly 6 in 10 working adults used a computer on the job and more than 9 in 10 children used computers in school (kindergarten through grade 12).3 Between 1989 and 2009, the number of households with a computer and Internet access increased from 15% to 69%.3 Other significant contributors to daily sitting time—watching television and driving personal vehicles—are at all-time highs, with estimates of nearly 4 hours and 1 hour, respectively.4,5 Scientists studying the ill effects of this decrease in physical activity have revealed a complex, multifaceted relationship among physical work, energy expenditure, and health.6,7 Clinical and basic research has focused on the benefits of incorporating regular bouts of exercise into modern life to adjust to some extent for the loss of the physically active life led by our ancestors.6 Current public health recommendations propose engaging in at least 150 minutes per week of moderate-to-vigorous activity to help prevent and manage multiple chronic conditions, notably cardiovascular disease, type 2 diabetes, obesity, and some cancers.7 The evidence base that supports this exercise recommendation is substantial. (Here, we use the terms exercise and moderate-to-vigorous activity interchangeably, recognizing that, by some definitions, exercise refers specifically to intentional activity carried out for health/fitness purposes.7) Those who do not exercise have by convention been termed sedentary. However, this is no longer an adequate perspective. Recent epidemiologic evidence suggests that the metabolic and long-term health consequences of habitual sedentary behavior (too much sitting) are distinct from those associated with a lack of moderate-to-vigorous activity (too little exercise).8,9 This shift in perspective is being clarified through innovations in technology used to characterize movement patterns in populations.

Nanoscale Tunable Reduction of Graphene Oxide for Graphene Electronics

Abstract: The reduced form of graphene oxide (GO) is an attractive alternative to graphene for producing large-scale flexible conductors and for creating devices that require an electronic gap. We report on a means to tune the topographical and electrical properties of reduced GO (rGO) with nanoscopic resolution by local thermal reduction of GO with a heated atomic force microscope tip. The rGO regions are up to four orders of magnitude more conductive than pristine GO. No sign of tip wear or sample tearing was observed. Variably conductive nanoribbons with dimensions down to 12 nanometers could be produced in oxidized epitaxial graphene films in a single step that is clean, rapid, and reliable.

Piezoelectric BaTiO₃ thin film nanogenerator on plastic substrates.

Abstract: The piezoelectric generation of perovskite BaTiO3 thin films on a flexible substrate has been applied to convert mechanical energy to electrical energy for the first time. Ferroelectric BaTiO3 thin films were deposited by radio frequency magnetron sputtering on a Pt/Ti/SiO2/(100) Si substrate and poled under an electric field of 100 kV/cm. The metal-insulator (BaTiO3)-metal-structured ribbons were successfully transferred onto a flexible substrate and connected by interdigitated electrodes. When periodically deformed by a bending stage, a flexible BaTiO3 nanogenerator can generate an output voltage of up to 1.0 V. The fabricated nanogenerator produced an output current density of 0.19 μA/cm(2) and a power density of ∼7 mW/cm(3). The results show that a nanogenerator can be used to power flexible displays by means of mechanical agitations for future touchable display technologies.

An integrated perspective of the continuum between earthquakes and slow-slip phenomena

Abstract: Slow slip, a mechanism by which faults can relieve stress, was thought to be distinct from earthquakes. However, a global review of slow-slip phenomena suggests that instead there is a continuum between the two types of event.

Energy-efficient link adaptation in frequency-selective channels

Abstract: Energy efficiency is becoming increasingly important for small form factor mobile devices, as battery technology has not kept up with the growing requirements stemming from ubiquitous multimedia applications. This paper addresses link adaptive transmission for maximizing energy efficiency, as measured by the "throughput per Joule" metric. In contrast to the existing water-filling power allocation schemes that maximize throughput subject to a fixed overall transmit power constraint, our scheme maximizes energy efficiency by adapting both overall transmit power and its allocation, according to the channel states and the circuit power consumed. We demonstrate the existence of a unique globally optimal link adaptation solution and develop iterative algorithms to obtain it. We further consider the special case of flat-fading channels to develop an upper bound on energy efficiency and to characterize its variation with bandwidth, channel gain and circuit power. Our results for OFDM systems demonstrate improved energy savings with energy optimal link adaptation as well as illustrate the fundamental tradeoff between energy-efficient and spectrum-efficient transmission.