Viscoelasticity of a carbon nanotube-laden air-water interface.

The viscoelasticity of a carbon nanotube (CNT)-laden air-water interface was characterized using two different experimental methods. The first experimental method used a Langmuir-Pockels (LP) trough coupled with a pair of oscillating barriers. The second method is termed the Bicone-Trough (BT) method, where a LP trough was custom-built and fit onto a rheometer equipped with a bicone fixture to standardize the preparation and conditioning of a particle-laden interface especially at high particle coverages. The performance of both methods was evaluated by performing Fast Fourier Transform (FFT) analysis to calculate the signal-to-noise ratios (SNR). Overall, the rheometer-based BT method offered better strain control and considerably higher SNRs compared to the Oscillatory Barriers (OB) method that oscillated barriers with relatively limited positional and speed control. For a CNT surface coverage of 165 mg/m2 and a frequency of 100 mHz, the interfacial shear modulus obtained from the OB method increased from 39 to 57 mN/m as the normal strain amplitude increased from 1 to 3%. No linear viscoelastic regime was experimentally observed for a normal strain as small as 0.5%. In the BT method, a linear regime was observed below a shear strain of 0.1%. The interfacial shear modulus decreased significantly from 96 to 2 mN/m as the shear strain amplitude increased from 0.025 to 10%.

Simulation of semidilute polymer solutions in planar extensional flow via conformationally averaged Brownian noise.

The dynamics and rheology of semidilute polymer solutions in strong flows are of great practical relevance. Processing applications can in principle be designed utilizing the relationship between nonequilibrium polymer conformations and the material properties of the solution. However, the interplay between concentration, flow, hydrodynamic interactions (HIs), and topological interactions which govern semidilute polymer dynamics is challenging to characterize. Brownian dynamics (BD) simulations are particularly valuable as a way to directly visualize how molecular interactions arise in these systems and are quantitatively comparable to single-molecule experiments. However, such simulations are often computationally intractable and are limited by the need to calculate the correlated Brownian noise via decomposition of the diffusion tensor. Previously, we have introduced an iterative conformational averaging (CA) method for BD simulations which bypasses these limitations by preaveraging the HI and Brownian noise in an iterative procedure. In this work, we generalize the CA method to flowing semidilute solutions by introducing a conformation dependent diffusion tensor and a strain dependent approximation to the conformationally averaged Brownian noise. We find that this approach nearly quantitatively reproduces both transient and steady state polymer dynamics and rheology while achieving an order of magnitude computational acceleration. We then utilize the CA method to investigate the concentration and flow rate dependence of polymer dynamics in planar extensional flows. Our results are consistent with previous experimental and simulation studies and provide a detailed view of broad conformational distributions in the semidilute regime. We observe interconversion between stretched and coiled states at steady state, which we conjecture occur due to the effect of concentration on the conformation dependent polymer drag. Additionally, we observe transient flow-induced intermolecular hooks in the startup of flow which lead to diverse and unique stretching pathways.

Conformationally averaged iterative Brownian dynamics simulations of semidilute polymer solutions.

The dynamics of semidilute polymer solutions are important to many polymer solution processing techniques such as fiber spinning and solution printing. The out-of-equilibrium molecular conformations resulting from processing flows directly impact material properties. Brownian dynamics (BD) simulations are a standard technique for studying this connection between polymer conformations in solution and processing flows because they can capture molecular-level polymer dynamics. However, BD simulations of semidilute polymer solutions are computationally limited by the calculation of hydrodynamic interactions (HIs) via an Ewald summed diffusion tensor and stochastic Brownian displacements via the decomposition of the diffusion tensor. Techniques based on the Cholesky decomposition scale with the number of particles N as O(N 3) and approximations in the literature have reduced this scaling to as low as O(N). These methods still require continuous updating of the diffusion tensor and Brownian displacements, resulting in a significant constant per-time step cost. Previously, we introduced a method that avoids this cost for dilute polymer solutions by iterative conformational averaging (CA) of intramolecular HIs. In this work, we extend the CA method to semidilute solutions by introducing a grid-space average of intermolecular HIs and a pairwise approximation to the Brownian displacements based on the truncated expansion ansatz of Geyer and Winter. We evaluate our method by first comparing the computational cost with that of other simulation techniques. We verify our approximations by comparison with expected results for static and dynamic properties at equilibrium and use our method to demonstrate the concentration dependence of HI screening.

An iterative method for hydrodynamic interactions in Brownian dynamics simulations of polymer dynamics.

Brownian Dynamics (BD) simulations are a standard tool for understanding the dynamics of polymers in and out of equilibrium. Quantitative comparison can be made to rheological measurements of dilute polymer solutions, as well as direct visual observations of fluorescently labeled DNA. The primary computational challenge with BD is the expensive calculation of hydrodynamic interactions (HI), which are necessary to capture physically realistic dynamics. The full HI calculation, performed via a Cholesky decomposition every time step, scales with the length of the polymer as O(N3). This limits the calculation to a few hundred simulated particles. A number of approximations in the literature can lower this scaling to O(N2 - N2.25), and explicit solvent methods scale as O(N); however both incur a significant constant per-time step computational cost. Despite this progress, there remains a need for new or alternative methods of calculating hydrodynamic interactions; large polymer chains or semidilute polymer solutions remain computationally expensive. In this paper, we introduce an alternative method for calculating approximate hydrodynamic interactions. Our method relies on an iterative scheme to establish self-consistency between a hydrodynamic matrix that is averaged over simulation and the hydrodynamic matrix used to run the simulation. Comparison to standard BD simulation and polymer theory results demonstrates that this method quantitatively captures both equilibrium and steady-state dynamics after only a few iterations. The use of an averaged hydrodynamic matrix allows the computationally expensive Brownian noise calculation to be performed infrequently, so that it is no longer the bottleneck of the simulation calculations. We also investigate limitations of this conformational averaging approach in ring polymers.

Deep learning delay coordinate dynamics for chaotic attractors from partial observable data.

A common problem in time-series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold, Takens' theorem proves a time-delayed embedding of the partial state is diffeomorphic to the attractor, although for chaotic and highly nonlinear systems, learning these delay coordinate mappings is challenging. We utilize deep artificial neural networks (ANNs) to learn discrete time maps and continuous time flows of the partial state. Given training data for the full state, we also learn a reconstruction map. Thus, predictions of a time series can be made from the current state and several previous observations with embedding parameters determined from time-series analysis. The state space for time evolution is of comparable dimension to reduced order manifold models. These are advantages over recurrent neural network models, which require a high-dimensional internal state or additional memory terms and hyperparameters. We demonstrate the capacity of deep ANNs to predict chaotic behavior from a scalar observation on a manifold of dimension three via the Lorenz system. We also consider multivariate observations on the Kuramoto-Sivashinsky equation, where the observation dimension required for accurately reproducing dynamics increases with the manifold dimension via the spatial extent of the system.

Ring polymer dynamics and tumbling-stretch transitions in planar mixed flows.

The properties of dilute polymer solutions are governed by the conformational dynamics of individual polymers which can be perturbed in the presence of an applied flow. Much of our understanding of dilute solutions comes from studying how flows manipulate the molecular features of polymer chains out of equilibrium, primarily focusing on linear polymer chains. Recently there has been an emerging interest in the dynamics of nonlinear architectures, particularly ring polymers, which exhibit surprising out-of-equilibrium dynamics in dilute solutions. In particular, it has been observed that hydrodynamics can couple to topology in planar elongational and shear flows, driving molecular expansion in the nonflow direction that is not observed for linear chains. In this paper, we extend our understanding of dilute ring polymer dynamics to mixed flows, which represent flow profiles intermediate between simple shear or planar elongation. We map the conformational behaviors at a number of flow geometries and strengths, demonstrating transitions between coiled, tumbling, and stretched regimes. Indeed, these observations are consistent with how linear chains respond to mixed flows. For both linear and ring polymers, we observe a marked first-order-like transition between tumbling and stretched polymers that we attribute to a dynamic energy barrier between the two states. This manifests as bimodal extension distributions in a narrow range of flow strengths and geometries, with the primary difference between rings and linear chains being the presence of molecular expansion in the vorticity direction.

Factors in cattle affecting embryo transfer pregnancies in recipient animals.

The use of embryo transfer (ET) in cattle is important for profitability and improved genetic gains. The advent of the commercial embryo collection and transfer industry has led to advancements in multiple techniques and practices. Specific variables, however, have historically affected pregnancy rates but an understanding of the magnitude of these effects in the current industry is limited. Transfer location (cranial, middle, or caudal third of the uterine horn ipsilateral to the ovary with a CL), transfer score (range of 1-3 with 1 being excellent and 3 poor, based on difficulty of accessing the site of embryo deposition), and amount of time to complete a transfer, therefore, were recorded. These variables were collected in a setting designed to mimic commercial production practices as well as exaggerated time (due to data collection) to assess effects on pregnancy rates. Fresh and frozen in vivo-derived embryos (n = 256) from Bos taurus cows were transferred to Bos taurus recipients. There tended to be more pregnancies when embryos were deposited in the cranial part of the uterus (P = 0.08) compared to the middle and caudal third of the uterus. With a lesser degree of difficulty in transfers (score 1), there tended to be more pregnancies established (P = 0.07). When lesser time was needed for transferring embryos and collecting data, there were greater pregnancy rates (P = 0.03). Thus, these traditionally accepted variables of influence (site of embryo placement in uterus, difficulty, and time) continue to influence ET pregnancy success.

Effects of dexamethasone and isoflupredone acetate on plasma potassium concentrations and other biochemical measurements in dairy cows in early lactation.

OBJECTIVE: To determine whether administration of isoflupredone acetate (ISO) to healthy cows increases the frequency of severe hypokalemia and whether dexamethasone (DEX) has detectable mineralocorticoid properties. ANIMALS: 33 cows at 20 to 25 days of lactation. PROCEDURES: Cows were randomly allocated to 5 treatment groups and received 2 IM injections (on days 0 and 2) of sterile saline (0.9% NaCl) solution (10 mL each), an injection of ISO (20 mg) or DEX (20 mg) followed by 10 mL of saline solution, or 2 injections of ISO or DEX. Milk production was measured, physical examinations were performed, and blood and urine samples were collected daily on days 0 through 7. RESULTS: Physical examination parameters did not differ among groups; however, 1 cow developed atrial fibrillation on day 4. Both corticosteroids significantly increased plasma glucose concentrations, and ISO significantly decreased plasma potassium concentrations and increased total carbon dioxide concentrations with time. One dose of ISO decreased mean plasma potassium concentration by 25% on day 2, compared with day 0, and severe hypokalemia (serum potassium concentration < 2.3 mEq/L) developed in 1 of 6 cows. Mean plasma potassium concentration was 46% lower on day 3 than on day 0 in cows receiving 2 doses of ISO, and 5 of 7 cows became severely hypokalemic. Mean urinary fractional excretion of potassium significantly increased from that on day 0 in cows receiving 2 doses of ISO. CONCLUSIONS AND CLINICAL RELEVANCE: Both corticosteroids had glucocorticoid activity; however, only ISO had mineralocorticoid activity. Compared with saline solution, administration of 2 doses of ISO significantly increased the frequency of severe hypokalemia.