The effect of size ratio on the sphere structure factor in colloidal sphere-plate mixtures

The following article appeared in Journal of Chemical Physics 137.20 (2012): 204909 and may be found at http://scitation.aip.org/content/aip/journal/jcp/137/20/10.1063/1.4767722Binary mixtures of colloidal particles of sufficiently different sizes or shapes tend to demix at high concentration. Already at low concentration, excluded volume interactions between the two species give rise to structuring effects. Here, a new theoretical description is proposed of the structure of colloidal sphere-plate mixtures, based on a density expansion of the work needed to insert a pair of spheres and a single sphere in a sea of them, in the presence or not of plates. The theory is first validated using computer simulations. The predictions are then compared to experimental observations using silica spheres and gibbsite platelets. Small-angle neutron scattering was used to determine the change of the structure factor of spheres on addition of platelets, under solvent contrast conditions where the platelets were invisible. Theory and experiment agreed very well for a platelet/sphere diameter ratio Dd 2.2 and reasonably well for Dd 5. The sphere structure factor increases at low scattering vector Q in the presence of platelets; a weak reduction of the sphere structure factor was predicted at larger Q, and for the system with Dd 2.2 was indeed observed experimentally. At fixed particle volume fraction, an increase in diameter ratio leads to a large change in structure factor. Systems with a larger diameter ratio also phase separate at lower concentrationsG. Cinacchi was supported by the EU through a Marie Curie Research Fellowship PIEF-GA-2008-220557 and now by the Ministry of Research of Spain through the Ramón y Cajal contract (Contract. No. RYC-2010-07475). N. Doshi was jointly supported by Imerys and EPSRC DTA. Experiments at ILL were supported by beamtime allocations 9-12- 216 and 9-10-1044. Materials were kindly donated by AZ Electronics (Klebosol) and Lubrizol (Solsperse 41000

Quantum computing with antiferromagnetic spin clusters

We show that a wide range of spin clusters with antiferromagnetic intracluster exchange interaction allows one to define a qubit. For these spin cluster qubits, initialization, quantum gate operation, and readout are possible using the same techniques as for single spins. Quantum gate operation for the spin cluster qubit does not require control over the intracluster exchange interaction. Electric and magnetic fields necessary to effect quantum gates need only be controlled on the length scale of the spin cluster rather than the scale for a single spin. Here, we calculate the energy gap separating the logical qubit states from the next excited state and the matrix elements which determine quantum gate operation times. We discuss spin cluster qubits formed by one- and two-dimensional arrays of s=1/2 spins as well as clusters formed by spins s>1/2. We illustrate the advantages of spin cluster qubits for various suggested implementations of spin qubits and analyze the scaling of decoherence time with spin cluster size.Comment: 15 pages, 7 figures; minor change

Polyelectrolyte Multilayering on a Charged Planar Surface

The adsorption of highly \textit{oppositely} charged flexible polyelectrolytes (PEs) on a charged planar substrate is investigated by means of Monte Carlo (MC) simulations. We study in detail the equilibrium structure of the first few PE layers. The influence of the chain length and of a (extra) non-electrostatic short range attraction between the polycations and the negatively charged substrate is considered. We show that the stability as well as the microstructure of the PE layers are especially sensitive to the strength of this latter interaction. Qualitative agreement is reached with some recent experiments.Comment: 28 pages; 11 (main) Figs - Revtex4 - Higher resolution Figs can be obtained upon request. To appear in Macromolecule

The Self-Administered Witness Interview Tool (SAW-IT): Enhancing witness recall of workplace incidents

Given the often crucial role of witness evidence in Occupational Health and Safety investigation, statements should be obtained as soon as possible after an incident using best practice methods. The present research systematically tested the efficacy of a novel Self-Administered Witness Interview Tool (SAW-IT); an adapted version of the Self-Administered Interview (SAI©) designed to elicit comprehensive information from witnesses to industrial events. The present study also examined whether completing the SAW-IT mitigated the effect of schematic processing on witness recall. Results indicate that the SAW-IT elicited significantly more correct details, as well as more precise information than a traditional incident report form. Neither the traditional report from, nor the SAW-IT mitigated against biasing effects of contextual information about a worker’s safety history, confirming that witnesses should be shielded from extraneous post-event information prior to reporting. Importantly, these results demonstrate that the SAW-IT can enhance the quality of witness reports

Social Learning and Innovation Cycles

We study social learning and innovation in an overlapping generations model, emphasizing the trade-off between marginal innovation (combining existing technologies) and radical innovation (breaking new ground). We characterize both short-term and long-term dynamics of innovation, and the intergenerational accumulation of knowledge. Innovation cycles emerge endogenously, but the number of cycles is finite almost surely, and radical innovation terminates in finite time. We identify a negative relationship between past successes and the magnitude of radical innovation, combining insights from the multi-armed bandit literature with a spatial representation of innovation. Past successes reduce the incremental value of experimentation, and result in less ambitious innovation. In our framework, patents promote radical innovation through two channels: by increasing the expected benefit of radical innovation and by increasing the cost of marginal innovation. Our analysis suggests that sustaining radical innovation in the long-run requires external intervention

Bi-stable tunneling current through a molecular quantum dot

An exact solution is presented for tunneling through a negative-U d-fold degenerate molecular quantum dot weakly coupled to electrical leads. The tunnel current exhibits hysteresis if the level degeneracy of the negative-U dot is larger than two (d>2). Switching occurs in the voltage range V1 < V < V2 as a result of attractive electron correlations in the molecule, which open up a new conducting channel when the voltage is above the threshold bias voltage V2. Once this current has been established, the extra channel remains open as the voltage is reduced down to the lower threshold voltage V1. Possible realizations of the bi-stable molecular quantum dots are fullerenes, especially C60, and mixed-valence compounds.Comment: 5 pages, 1 figure. (v2) Figure updated to compare the current hysteresis for degeneracies d=4 and d>>1 of the level in the dot, minor corrections in the text. To appear in Phys. Rev.

Current hysteresis and memory effect in a molecular quantum dot with strong electron-vibron interaction

Theory of current hysteresis for tunneling through a molecular quantum dot (MQD) with strong electron-vibron interactions and attractive electron-electron correlations is developed. The dot is modeled as a d-fold degenerate energy level weakly coupled to the leads. The effective attractive interaction between polarons in the dot results in a "switching" phenomenon in the current-voltage characteristics when d>2, in agreement with the results for the phenomenological negative-U model. The degenerate MQD with strong electron-vibron coupling has two stable current states in certain interval of the bias voltage below some critical temperature.Comment: 8 pages, 4 figure

Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner