Ulta-slow relaxation in discontinuous-film based electron glasses

We present field effect measurements on discontinuous 2D thin films which are composed of a sub monolayer of nano-grains of Au, Ni, Ag or Al. Like other electron glasses these systems exhibit slow conductance relaxation and memory effects. However, unlike other systems, the discontinuous films exhibit a dramatic slowing down of the dynamics below a characteristic temperature $T^*$. $T^*$ is typically between 10-50K and is sample dependent. For $T<T^*$ the sample exhibits a few other peculiar features such as repeatable conductance fluctuations in millimeter size samples. We suggest that the enhanced system sluggishness is related to the current carrying network becoming very dilute in discontinuous films so that the system contains many parts which are electrically very weakly connected and the transport is dominated by very few weak links. This enables studying the glassy properties of the sample as it transitions from a macroscopic sample to a mesocopic sample, hence, the results provide new insight on the underlying physics of electron glasses.Comment: 4 pages, 4 figure

Erratum: “Nonlinear optical response of GaN layers on sapphire: The impact of fundamental beam interference” [Appl. Phys. Lett. 76, 810 (2000)]

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70473/2/APPLAB-76-11-1479-1.pd

Chemotaxis When Bacteria Remember: Drift versus Diffusion

{\sl Escherichia coli} ({\sl E. coli}) bacteria govern their trajectories by switching between running and tumbling modes as a function of the nutrient concentration they experienced in the past. At short time one observes a drift of the bacterial population, while at long time one observes accumulation in high-nutrient regions. Recent work has viewed chemotaxis as a compromise between drift toward favorable regions and accumulation in favorable regions. A number of earlier studies assume that a bacterium resets its memory at tumbles -- a fact not borne out by experiment -- and make use of approximate coarse-grained descriptions. Here, we revisit the problem of chemotaxis without resorting to any memory resets. We find that when bacteria respond to the environment in a non-adaptive manner, chemotaxis is generally dominated by diffusion, whereas when bacteria respond in an adaptive manner, chemotaxis is dominated by a bias in the motion. In the adaptive case, favorable drift occurs together with favorable accumulation. We derive our results from detailed simulations and a variety of analytical arguments. In particular, we introduce a new coarse-grained description of chemotaxis as biased diffusion, and we discuss the way it departs from older coarse-grained descriptions.Comment: Revised version, journal reference adde

Behavioral Mechanism during Human Sperm Chemotaxis: Involvement of Hyperactivation

When mammalian spermatozoa become capacitated they acquire, among other activities, chemotactic responsiveness and the ability to exhibit occasional events of hyperactivated motility—a vigorous motility type with large amplitudes of head displacement. Although a number of roles have been proposed for this type of motility, its function is still obscure. Here we provide evidence suggesting that hyperactivation is part of the chemotactic response. By analyzing tracks of spermatozoa swimming in a spatial chemoattractant gradient we demonstrate that, in such a gradient, the level of hyperactivation events is significantly lower than in proper controls. This suggests that upon sensing an increase in the chemoattractant concentration capacitated cells repress their hyperactivation events and thus maintain their course of swimming toward the chemoattractant. Furthermore, in response to a temporal concentration jump achieved by photorelease of the chemoattractant progesterone from its caged form, the responsive cells exhibited a delayed turn, often accompanied by hyperactivation events or an even more intense response in the form of flagellar arrest. This study suggests that the function of hyperactivation is to cause a rather sharp turn during the chemotactic response of capacitated cells so as to assist them to reorient according to the chemoattractant gradient. On the basis of these results a model for the behavior of spermatozoa responding to a spatial chemoattractant gradient is proposed

Photoinduced 3D orientational order in side chain liquid crystalline azopolymers

We apply experimental technique based on the combination of methods dealing with principal refractive indices and absorption coefficients to study the photoinduced 3D orientational order in the films of liquid crystalline (LC) azopolymers. The technique is used to identify 3D orientational configurations of trans azobenzene chromophores and to characterize the degree of ordering in terms of order parameters. We study two types of LC azopolymers which form structures with preferred in-plane and out-of-plane alignment of azochromophores, correspondingly. Using irradiation with the polarized light of two different wavelengths we find that the kinetics of photoinduced anisotropy can be dominated by either photo-reorientation or photoselection mechanisms depending on the wavelength. We formulate the phenomenological model describing the kinetics of photoinduced anisotropy in terms of the isomer concentrations and the order parameter tensor. We present the numerical results for absorption coefficients that are found to be in good agreement with the experimental data. The model is also used to interpret the effect of changing the mechanism with the wavelength of the pumping light.Comment: uses revtex4 28 pages, 10 figure

Machine-learning-assisted insight into spin ice Dy2Ti2O7

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy2Ti2O7. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use an automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy2Ti2O7. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems.Publisher PDFPeer reviewe

Overview of mathematical approaches used to model bacterial chemotaxis I: the single cell

Mathematical modeling of bacterial chemotaxis systems has been influential and insightful in helping to understand experimental observations. We provide here a comprehensive overview of the range of mathematical approaches used for modeling, within a single bacterium, chemotactic processes caused by changes to external gradients in its environment. Specific areas of the bacterial system which have been studied and modeled are discussed in detail, including the modeling of adaptation in response to attractant gradients, the intracellular phosphorylation cascade, membrane receptor clustering, and spatial modeling of intracellular protein signal transduction. The importance of producing robust models that address adaptation, gain, and sensitivity are also discussed. This review highlights that while mathematical modeling has aided in understanding bacterial chemotaxis on the individual cell scale and guiding experimental design, no single model succeeds in robustly describing all of the basic elements of the cell. We conclude by discussing the importance of this and the future of modeling in this area

Testing Human Sperm Chemotaxis: How to Detect Biased Motion in Population Assays

Biased motion of motile cells in a concentration gradient of a chemoattractant is frequently studied on the population level. This approach has been particularly employed in human sperm chemotactic assays, where the fraction of responsive cells is low and detection of biased motion depends on subtle differences. In these assays, statistical measures such as population odds ratios of swimming directions can be employed to infer chemotactic performance. Here, we report on an improved method to assess statistical significance of experimentally determined odds ratios and discuss the strong impact of data correlations that arise from the directional persistence of sperm swimming

Exploiting machine learning in multiscale modelling of materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed