Aromaticity on the fly: cyclic transition state stabilization at finite temperature.

We study the transition state of pericyclic reactions at elevated temperature with unbiased ab initio molecular dynamics. We find that the transition state of the intramolecular rearrangements for barbaralane and bullvalene remains aromatic at high temperature despite the significant thermal atomic motions. Structural, magnetic, and electronic properties of the dynamical transition state show the concertedness and aromatic character. Free-energy calculations also support the validity of the transition state theory for the present rearrangement reactions. The calculations demonstrate that cyclic delocalization represents a strong force to synchronize the thermal atomic motions even at high temperatures.

Topologically invariant reaction coordinates for simulating multistate chemical reactions.

Evaluating free energy profiles of chemical reactions in complex environments such as solvents and enzymes requires extensive sampling, which is usually performed by potential of mean force (PMF) techniques. The reliability of the sampling depends not only on the applied PMF method but also the reaction coordinate space within the dynamics is biased. In contrast to simple geometrical collective variables that depend only on the positions of the atomic coordinates of the reactants, the E(gap) reaction coordinate (the energy difference obtained by evaluating a suitable force field using reactant and product state topologies) has the unique property that it is able to take environmental effects into account leading to better convergence, a more faithful description of the transition state ensemble and therefore more accurate free energy profiles. However, E(gap) requires predefined topologies and is therefore inapplicable for multistate reactions, in which the barrier between the chemically equivalent topologies is comparable to the reaction activation barrier, because undesired "side reactions" occur. In this article, we introduce a new energy-based collective variable by generalizing the E(gap) reaction coordinate such that it becomes invariant to equivalent topologies and show that it yields more well behaved free energy profiles than simpler geometrical reaction coordinates.

Auxetic nuclei in embryonic stem cells exiting pluripotency.

Embryonic stem cells (ESCs) self-renew in a state of naïve pluripotency in which they are competent to generate all somatic cells. It has been hypothesized that, before irreversibly committing, ESCs pass through at least one metastable transition state. This transition would represent a gateway for differentiation and reprogramming of somatic cells. Here, we show that during the transition, the nuclei of ESCs are auxetic: they exhibit a cross-sectional expansion when stretched and a cross-sectional contraction when compressed, and their stiffness increases under compression. We also show that the auxetic phenotype of transition ESC nuclei is driven at least in part by global chromatin decondensation. Through the regulation of molecular turnover in the differentiating nucleus by external forces, auxeticity could be a key element in mechanotransduction. Our findings highlight the importance of nuclear structure in the regulation of differentiation and reprogramming.

On the transition from displacement to mixing ventilation with a localized heat source

We investigate the steady state natural ventilation of a room heated at the base and consisting of two vents at different levels. We explore how the air flow rate and internal temperature relative to the exterior vary as a function of the vent areas, position of the vents and heat load in order to establish appropriate ventilation strategies for a room. When the room is heated by a distributed source, the room becomes well mixed and the steady state ventilation rate depends on the heating rate, the area of the vents and the distance between the lower and upper level vents. However, when the room is heated by a localised source the room becomes stratified. If the effective ventilation area is sufficiently large, then the interface separating the two layers lies above the inlet vent and the lower layer is comprised of ambient fluid. In this case the upper layer is warmer than in the well mixed case and the ventilation rate is smaller. However, if the effective area for ventilation is sufficiently small, then the interface separating the two layers lies below the inlet vent and the lower layer is comprised of warm fluid which originates as the cold incoming fluid mixes during descent from the vent through the upper layer. In this case both the ventilation rate and the upper layer temperature are the same as in the case of a distributed heat load. As the vertical separation between lower and upper level vents decreases, then the temperature difference between the layers falls to zero and the room becomes approximately well mixed. These findings suggest how the appropriate ventilation strategy for a room can be varied depending on the exterior temperature, with mixing ventilation more suitable for winter conditions and displacement ventilation for warmer external temperatures.

Bypass transition to sustained thermoacoustic oscillations in a linearly stable rijke tube

In this paper we examine triggering in a simple linearly-stable thermoacoustic system using techniques from flow instability and optimal control. Firstly, for a noiseless system, we find the initial states that have highest energy growth over given times and from given energies. Secondly, by varying the initial energy, we find the lowest energy that just triggers to a stable periodic solution. We show that the corresponding initial state grows first towards an unstable periodic solution and, from there, to the stable periodic solution. This exploits linear transient growth, which arises due to nonnormality in the governing equations and is directly analogous to bypass transition to turbulence. Thirdly, we introduce noise that has similar spectral characteristics to this initial state. We show that, when triggering from low noise levels, the system grows to high amplitude self-sustained oscillations by first growing towards the unstable periodic solution of the noiseless system. This helps to explain the experimental observation that linearly-stable systems can trigger to self-sustained oscillations even with low background noise. © 2010 by University of Cambridge. Published by the American Institute of Aeronautics and Astronautics, Inc.

A detailed chemistry simulation of the SI-HCCI transition

A Stochastic Reactor Model (SRM) has been used to simulate the transition from Spark Ignition (SI) mode to Homogeneous Charge Compression Ignition (HCCI) mode in a four cylinder in-line four-stroke naturally aspirated direct injection SI engine with cam profile switching. The SRM is coupled with GT-Power, a one-dimensional engine simulation tool used for modelling engine breathing during the open valve portion of the engine cycle, enabling multi-cycle simulations. The model is initially calibrated in both modes using steady state data from SI and HCCI operation. The mode change is achieved by switching the cam profiles and phasing, resulting in a Negative Valve Overlap (NVO), opening the throttle, advancing the spark timing and reducing the fuel mass as well as utilising a pilot injection. Experimental data is presented along with the simulation results. The model is used to investigate key control parameters and their effects on parameters that are difficult to measure experimentally. The effect of the spark in the first HCCI cycles is found to have a major impact on the stability of the transition. Copyright © 2010 SAE International.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.