In materials science, the control over the spatial arrangement of colloids in soft matter hosts implies control over a wide variety of materials properties, ranging from the system’s rheology, to its optics, to its catalytic activity. To direct particle assembly, colloids are often manipulated using external fields to steer them into well-defined structures at given locations. We have been developing alternative strategies based on fields that arise when a colloid is placed within soft matter to form an inclusion that generates a potential field in its host. Such potential fields allow particles to interact with each other. If the soft matter host is deformed in some way, the potential allows the particles to interact with the global system distortion. The concept is quite general, and applied within any medium in which distortions cost energy. We have explored these ideas in three media: curved fluid interfaces, where particles interact with the host interface via capillarity; confined nematic liquid crystals, where particles interact with the host director field via elastic interactions, and deformed lipid bilayers, where particles interact o tense membranes. These example systems have important analogies and pronounced differences which we seek to understand and exploit.

 

 

Epithelial cells are mostly quiescent when they are mature and uninjured, but they undergo collective migration during morphogenesis, cancer metastasis, and wound repair. We have recently reported (Nature Materials, Park et al, 2015) that, during differentiation, airway epithelial cells in air-liquid interface culture undergo a transition from a fluid-like, mobile “unjammed” state toward a solid-like, immobile “jammed” state. This transition toward the jammed state is substantially delayed in cells from asthmatic donors, compared with cells from normal donors. Furthermore, mature, jammed cells undergo a transition toward the unjammed state when they are subjected to compressive stress that mimics bronchoconstriction, a process that occurs during asthma exacerbations. These jamming and unjamming transitions are accompanied by unique changes in cell shape that are associated with intercellular forces.

 

The dynamic clustering of globular particles in suspensions exhibiting competing short-range attraction and long-range repulsion such as in protein solutions has gained a lot of interest over the past years. We investigate theoretically the influence of clustering on the dynamics of globular particle dispersions [1]. To this end, we systematically explore various pair potential models by a combination of state-of-the-art analytic methods in conjunction with computer simulations where the solvent-mediated hydrodynamic interactions are likewise included. Our theoretical results show that the cluster peak (intermediate-range-order peak) is present also in the hydrodynamic function characterizing the short-time dynamics, in accord with experimental data [2]. Enhanced short-range attraction leads to a smaller self-diffusion coefficient and a larger dispersion viscosity. The behavior of the (generalized) sedimentation coefficient is more intricate, e.g. showing non-monotonic interaction strength dependence.

 [1] J. Riest and G. Nägele, Short-time dynamics in dispersions with competing short-range attraction and long-range repulsion, Soft Matter 11, 9273 (2015).

[2] Collaboration with D. Godfrin (MIT), Y. Liu (NIST) and N. Wagner (UDEL), work in progress.

 

The progress in neutrino physics over the past fifteen years has been tremendous: we have learned that neutrinos have mass and change flavor. This discovery won the 2015 Nobel Prize. I will pick out one of the threads of the story-- the measurement of flavor oscillation in neutrinos produced by cosmic ray showers in the atmosphere, and further measurements by long-baseline beam experiments. In this talk, I will present the latest results from the Super-Kamiokande and T2K (Tokai to Kamioka) long-baseline experiments, and will discuss how the next generation of high-intensity beam experiments will address some of the remaining puzzles.

Outermost occupied electron shells of chemical elements resemble monopoles, dipoles, quadrupoles, and octupoles corresponding to filled s-, p-, d-, and f-atomic orbitals. Theoretically, elements with hexadecapolar outer shells could also exist, but none of the known stable elements have filled g-orbitals. On the other hand, the research paradigm of “colloidal atoms” displays complexity of physical behavior of colloidal particles exceeding that of their atomic counterparts, allowing for switching between colloidal elastic dipole and quadrupole configurations using weak external stimuli. This lecture will describe colloidal elastic hexadecapoles formed by polymer microspheres dispersed in a liquid crystal, a nematic fluid of orientationally ordered molecular rods. The solid microspheres locally perturb the uniform molecular alignment of the nematic host, inducing hexadecapolar and other elastic multipoles that drive highly anisotropic colloidal interactions. We uncover physical underpinnings behind the spontaneous formation of colloidal elastic hexadecapoles and describe the ensuing particle bonding inaccessible to colloids studied previously. The lecture will conclude with discussion of practical applications that can be enabled by combining unique properties of metal and semiconductor nanoparticles with facile switching of self-assembled ordered superstructures that they exhibit in nematic hosts.

 TBA

 TBA

Louisa Barama’s PhD defense on November 18^th in room ES&T L1255 at 3:00 pm (Zoom Meeting : https://gatech.zoom.us/j/97517526199).

Advanced Methods for Real-time Identification and Determination of seismic events

Natural disasters pose an indistinguishable threat to populations all around the world, affecting ~200 hundred million every year, with earthquakes being the most deadly. Global seismic monitoring allows for robust real-time analysis to provide useful information about an event to assist in earthquake emergency response. Additionally it is an essential tool for monitoring anthropogenic seismic sources like nuclear weapons tests, the use of which can have disastrous effects on human life, ecological environments and public health, ramifications that can last for generations.  The focus of this thesis is on characterizing and identifying unique seismic events in near-real-time using the waveforms of initial seismic phase arrivals from teleseismic stations, their derivative products like radiated earthquake energy and rupture duration, and machine learning (ML). This thesis is a compilation of several works addressing novel methods for seismic event identification of: global tsunamigenic earthquakes, uncharacteristically high-energy tsunami earthquakes, deep earthquakes, and underground nuclear explosions (UNE).  First, I present the current Real-Time Earthquake Energy and Rupture Duration Determinations (RTerg) products and methodology applied to a case study of fast-rupturing tsunami earthquakes in the Solomon Islands, testing the robustness of the RTerg derivative waveform products and Tsunami Earthquake (TsE) discriminant threshold used for real-time analysis. Second, I show how peaks in RTerg energy flux curves from teleseismic stations and their differences in broadband and high frequency bandwidths can be associated with depth phase arrivals (P, pP, sP) to identify deep earthquakes, highlighting the potential for real-time depth determinations using first derivative waveform products without additional processing of waveforms. Next, I introduce nuclear explosion monitoring from a global network of stations, starting with the compilation of the first openly available and comprehensive UNE seismic waveform and event catalog termed GTUNE (Georgia Tech Underground Nuclear Explosions). GTUNE seismic records are sourced from declassified nuclear tests, previously published datasets and openly available waveforms and were assembled into a user‐friendly format compatible with most python‐based ML packages. The next contribution to this thesis is the development of a global UNE classifier using labeled P-wave seismograms from GTUNE. I trained a Convolutional Neural Network (CNN) to identify three classes: earthquake P-wave, nuclear P-wave, and noise. I found that the model can accurately characterize most events, finding over 90% of the signals in the validation set, even with limited training data. Lastly, I combine the thesis works described thus far and applied similar ML methodology to classify/predict deep earthquakes, using both a CNN and a Deep Neural Net (DNN), trained on both physical features of the energy flux time series (prominence and peak density) as well as the original waveforms.  Results show better single station predictions using the original waveforms.  By contrast, for full network determinations, the energy flux products perform the best, despite the smaller training dataset.  We anticipate that ML models like our UNE and deep earthquake classifiers can have broad application for other “small data” seismic signals including volcanic and non-volcanic tremor, anomalous earthquakes, tsunami earthquakes, ice-quakes or landslide-quakes.

Committee:  Dr. Andrew V Newman (advisor), Dr. Zhigang Peng, Dr. Jesse Williams, Dr. Winnie Chu, Dr. Samer Naif