Brownian particles in transient polymer networks

We discuss the thermal motion of colloidal particles in transient polymer networks. For particles that are physically bound to the surrounding chains, light-scattering experiments reveal that the submillisecond dynamics changes from diffusive to Rouse-like upon crossing the network formation threshold. Particles that are not bound do not show such a transition. At longer time scales the mean-square displacement (MSD) exhibits a caging plateau and, ultimately, a slow diffusive motion. The slow diffusion at longer time scales can be related to the macroscopic viscosity of the polymer solutions. Expressions that relate the caging plateau to the macroscopic network elasticity are found to fail for the cases presented here. The typical Rouse scaling of the MSD with the square root of time, as found in experiments at short time scales, is explained by developing a bead-spring model of a large colloidal particle connected to several polymer chains. The resulting analytical expressions for the MSD of the colloidal particle are shown to be consistent with experimental findings

Controlled Nanoparticle Formation by Diffusion Limited Coalescence

Polymeric nanoparticles (NPs) have a great application potential in science and technology. Their functionality strongly depends on their size. We present a theory for the size of NPs formed by precipitation of polymers into a bad solvent in the presence of a stabilizing surfactant. The analytical theory is based upon diffusion-limited coalescence kinetics of the polymers. Two relevant time scales, a mixing and a coalescence time, are identified and their ratio is shown to determine the final NP diameter. The size is found to scale in a universal manner and is predominantly sensitive to the mixing time and the polymer concentration if the surfactant concentration is sufficiently high. The model predictions are in good agreement with experimental data. Hence the theory provides a solid framework for tailoring nanoparticles with a priori determined size.Comment: 4 pages, 3 figure

Relaxation dynamics at different time scales in electrostatic complexes: Time-salt superposition

In this Letter we show that in the rheology of electrostatically assembled soft materials, salt concentration plays a similar role as temperature for polymer melts, and as strain rate for soft solids. We rescale linear and nonlinear rheological data of a set of model electrostatic complexes at different salt concentrations to access a range of time scales that is otherwise inaccessible. This provides new insights into the relaxation mechanisms of electrostatic complexes, which we rationalize in terms of a microscopic mechanism underlying salt-enhanced activated processe

Dynamics of polymer bridge formation and disruption

In this Letter we show, with colloidal probe AFM measurements, that the formation and subsequent disruption of polymer bridges between two solid surfaces is characterized by slow relaxation times. This is due to the retardation of polymer dynamics near a surface. For colloidal particles, that are in constant (Brownian) motion, kinetic aspects are key. To understand these effects, we develop a model of polymer bridging and bridge disruption that agrees quantitatively with our experiment

Self-consistent field predictions for quenched spherical biocompatible triblock copolymer micelles

We have used the Scheutjens-Fleer self-consistent field (SF-SCF) method to predict the self-assembly of triblock copolymers with a solvophilic middle block and sufficiently long solvophobic outer blocks. We model copolymers consisting of polyethylene oxide (PEO) as solvophilic block and poly(lactic-co-glycolic) acid (PLGA) or poly({\ko}-caprolactone) (PCL) as solvophobic block. These copolymers form structurally quenched spherical micelles provided the solvophilic block is long enough. Predictions are calibrated on experimental data for micelles composed of PCL-PEO-PCL and PLGA-PEO-PLGA triblock copolymers prepared via the nanoprecipitation method. We establish effective interaction parameters that enable us to predict various micelle properties such as the hydrodynamic size, the aggregation number and the loading capacity of the micelles for hydrophobic species that are consistent with experimental finding.Comment: accepted for publication in Soft Matte

Sparse Deterministic Approximation of Bayesian Inverse Problems

We present a parametric deterministic formulation of Bayesian inverse problems with input parameter from infinite dimensional, separable Banach spaces. In this formulation, the forward problems are parametric, deterministic elliptic partial differential equations, and the inverse problem is to determine the unknown, parametric deterministic coefficients from noisy observations comprising linear functionals of the solution. We prove a generalized polynomial chaos representation of the posterior density with respect to the prior measure, given noisy observational data. We analyze the sparsity of the posterior density in terms of the summability of the input data's coefficient sequence. To this end, we estimate the fluctuations in the prior. We exhibit sufficient conditions on the prior model in order for approximations of the posterior density to converge at a given algebraic rate, in terms of the number $N$ of unknowns appearing in the parameteric representation of the prior measure. Similar sparsity and approximation results are also exhibited for the solution and covariance of the elliptic partial differential equation under the posterior. These results then form the basis for efficient uncertainty quantification, in the presence of data with noise

Sepsis biomarkers in unselected patients on admission to intensive or high-dependency care

Although many sepsis biomarkers have shown promise in selected patient groups, only C-reactive protein and procalcitonin (PCT) have entered clinical practice. The aim of this study was to evaluate three promising novel sepsis biomarkers in unselected patients at admission to intensive care. We assessed the performance of pancreatic stone protein (PSP), soluble CD25 (sCD25) and heparin binding protein (HBP) in distinguishing patients with sepsis from those with a non-infective systemic inflammatory response and the ability of these markers to indicate severity of illness. METHODS: Plasma levels of the biomarkers, PCT and selected inflammatory cytokines were measured in samples taken from 219 patients during the first six hours of admission to intensive or high dependency care. Patients with a systemic inflammatory response were categorized as having sepsis or a non-infective aetiology, with or without markers of severity, using standard diagnostic criteria. RESULTS: Both PSP and sCD25 performed well as biomarkers of sepsis irrespective of severity of illness. For both markers the area under the receiver operating curve (AUC) was greater than 0.9; PSP 0.927 (0.887 to 0.968) and sCD25 0.902 (0.854 to 0.949). Procalcitonin and IL6 also performed well as markers of sepsis whilst in this intensive care unit (ICU) population, HBP did not: PCT 0.840 (0.778 to 0.901), IL6 0.805 (0.739 to 0.870) and HBP 0.607 (0.519 to 0.694). Levels of both PSP and PCT reflected severity of illness and both markers performed well in differentiating patients with severe sepsis from severely ill patients with a non-infective systemic inflammatory response: AUCs 0.955 (0.909 to 1) and 0.837 (0.732 to 0.941) respectively. Although levels of sCD25 did not correlate with severity, the addition of sCD25 to either PCT or PSP in a multivariate model improved the diagnostic accuracy of either marker alone. CONCLUSIONS: PSP and sCD25 perform well as sepsis biomarkers in patients with suspected sepsis at the time of admission to intensive or high dependency care. These markers warrant further assessment of their prognostic value. Whereas previously published data indicate HBP has clinical utility in the emergency department, it did not perform well in an intensive-care population