Transient lensing from a photoemitted electron gas imaged by ultrafast electron microscopy.

Understanding and controlling ultrafast charge carrier dynamics is of fundamental importance in diverse fields of (quantum) science and technology. Here, we create a three-dimensional hot electron gas through two-photon photoemission from a copper surface in vacuum. We employ an ultrafast electron microscope to record movies of the subsequent electron dynamics on the picosecond-nanosecond time scale. After a prompt Coulomb explosion, the subsequent dynamics is characterized by a rapid oblate-to-prolate shape transformation of the electron gas, and periodic and long-lived electron cyclotron oscillations inside the magnetic field of the objective lens. In this regime, the collective behavior of the oscillating electrons causes a transient, mean-field lensing effect and pronounced distortions in the images. We derive an analytical expression for the time-dependent focal length of the electron-gas lens, and perform numerical electron dynamics and probe image simulations to determine the role of Coulomb self-fields and image charges. This work inspires the visualization of cyclotron dynamics inside two-dimensional electron-gas materials and enables the elucidation of electron/plasma dynamics and properties that could benefit the development of high-brightness electron and X-ray sources.

Exploration of metastability and hidden phases in correlated electron crystals visualized by femtosecond optical doping and electron crystallography.

Characterizing and understanding the emergence of multiple macroscopically ordered electronic phases through subtle tuning of temperature, pressure, and chemical doping has been a long-standing central issue for complex materials research. We report the first comprehensive studies of optical doping-induced emergence of stable phases and metastable hidden phases visualized in situ by femtosecond electron crystallography. The electronic phase transitions are triggered by femtosecond infrared pulses, and a temperature-optical density phase diagram is constructed and substantiated with the dynamics of metastable states, highlighting the cooperation and competition through which the macroscopic quantum orders emerge. These results elucidate key pathways of femtosecond electronic switching phenomena and provide an important new avenue to comprehensively investigate optical doping-induced transition states and phase diagrams of complex materials with wide-ranging applications.

Decoupling of structural and electronic phase transitions in VO2.

Using optical, TEM, and ultrafast electron diffraction experiments we find that single crystal VO(2) microbeams gently placed on insulating substrates or metal grids exhibit different behaviors, with structural and metal-insulator transitions occurring at the same temperature for insulating substrates, while for metal substrates a new monoclinic metal phase lies between the insulating monoclinic phase and the metallic rutile phase. The structural and electronic phase transitions in these experiments are strongly first order and we discuss their origins in the context of current understanding of multiorbital splitting, strong correlation effects, and structural distortions that act cooperatively in this system.

Statistical properties and robustness of biological controller-target networks.

Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, in a "many-to-many" control structure. Here we study several of these bipartite (two-layer) networks. We analyze both naturally occurring biological networks (composed of transcription factors controlling genes, microRNAs controlling mRNA transcripts, and protein kinases controlling protein substrates) and a drug-target network composed of kinase inhibitors and of their kinase targets. Certain statistical properties of these biological bipartite structures seem universal across systems and species, suggesting the existence of common control strategies in biology. The number of controllers is ∼8% of targets and the density of links is 2.5%±1.2%. Links per node are predominantly exponentially distributed. We explain the conservation of the mean number of incoming links per target using a mathematical model of control networks, which also indicates that the "many-to-many" structure of biological control has properties of efficient robustness. The drug-target network has many statistical properties similar to the biological networks and we show that drug-target networks with biomimetic features can be obtained. These findings suggest a completely new approach to pharmacological control of biological systems. Molecular tools, such as kinase inhibitors, are now available to test if therapeutic combinations may benefit from being designed with biomimetic properties, such as "many-to-many" targeting, very wide coverage of the target set, and redundancy of incoming links per target.

Percolating bulk heterostructures from neutron reflectometry and small-angle scattering data.

We generate percolating fullerene-polymer bulk heterostructures that are consistent with the experimental characterization of a nanostructure, in particular neutron reflectometry and small-angle neutron scattering data from as-cast and annealed poly(3-hexylthiophene) (P3HT) and [6,6]-phenyl C61-butyric acid methyl ester systems. Transport simulations correlate changes in exciton dissociation efficiency and charge collection efficiency with morphological features including characteristic domain size, fullerene concentration profile, degree of fullerene sequestration, and degree of P3HT crystallization.

Systems approaches and algorithms for discovery of combinatorial therapies.

Effective therapy of complex diseases requires control of highly nonlinear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of cellular network activity. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper, we describe the main current and proposed approaches to the design of combinatorial therapies, including the heuristic methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.

Polymer chain swelling induced by dispersed nanoparticles.

The dimensions of individual deuterated polystyrene (d-PS) chains in a well-dispersed mixture of protonated polystyrene and chemically identical nanoparticles was determined by neutron scattering. A 10%-20% increase in the radius of gyration of d-PS was found when the nanoparticles are homogeneously dispersed in the polymer, an effect that occurs only when the radius of gyration of the polymer is larger than the nanoparticle radius. These results are reconciled with the existing literature.

Selective control of the apoptosis signaling network in heterogeneous cell populations.

BACKGROUND: Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Control of apoptosis is essential in a variety of therapeutic environments, including cancer where cancer cell death is a desired outcome and Alzheimer's disease where neuron survival is the desired outcome. However, in both cases these responses must occur with minimal response in other cells exposed to treatment; that is, the response must be selective. METHODOLOGY AND PRINCIPAL FINDINGS: Apoptosis signaling in heterogeneous cells is described by an ensemble of gene networks with identical topology but different link strengths. Selective control depends on the statistics of signaling in the ensemble of networks, and we analyze the effects of superposition, non-linearity and feedback on these statistics. Parallel pathways promote normal statistics while series pathways promote skew distributions, which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signaling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an exhaustive search method and the second is a linear programming based approach. Though control of a single gene in the signaling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control in heterogeneous apoptosis networks is studied and is used to identify general control strategies. CONCLUSIONS AND SIGNIFICANCE: We have explored two methods for the study of selectivity in cell populations. The first is an exhaustive search method limited to three node perturbations. The second is an effective linear model, based on interpolation of single node sensitivity, in which the selective combinations can be found by linear programming optimization. We found that selectivity is promoted by acting on the least sensitive nodes in the case of weak populations, while selective control of robust populations is optimized through perturbations of more sensitive nodes. High throughput experiments with heterogeneous cell lines could be designed in an analogous manner, with the further possibility of incorporating the selectivity optimization process into a closed-loop control system.

Self-assembled multilayers of nanocomponents.

We show it is possible to assemble nanoparticle-polymer layers in a controllable manner dictated by the difference in nano-object morphology and dielectric properties. A thin (10-100 nm) layer of the two components is spin coated onto a solid substrate and the system thermally aged to activate a cross-linking process between polymer molecules. The nanoparticles segregate to the solid substrate prior to complete cross-linking if entropic forces are dominant or to the air interface if dielectric (surface energy) forces are properly tuned. Subsequent layers are then spin coated onto the layer below, and the process is repeated to create layered structures with nanometer accuracy useful for tandem solar cells, sensors, optical coatings, etc. Unlike other self-assembly techniques the layer thicknesses are dictated by the spin coating conditions and relative concentration of the two components.

General strategies for nanoparticle dispersion.

Traditionally the dispersion of particles in polymeric materials has proven difficult and frequently results in phase separation and agglomeration. We show that thermodynamically stable dispersion of nanoparticles into a polymeric liquid is enhanced for systems where the radius of gyration of the linear polymer is greater than the radius of the nanoparticle. Dispersed nanoparticles swell the linear polymer chains, resulting in a polymer radius of gyration that grows with the nanoparticle volume fraction. It is proposed that this entropically unfavorable process is offset by an enthalpy gain due to an increase in molecular contacts at dispersed nanoparticle surfaces as compared with the surfaces of phase-separated nanoparticles. Even when the dispersed state is thermodynamically stable, it may be inaccessible unless the correct processing strategy is adopted, which is particularly important for the case of fullerene dispersion into linear polymers.