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Register a new userQuantifying the Reversible Association of Thermosensitive Nanoparticles
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Under many conditions, biomolecules and nanoparticles associate by means of attractive bonds, due to hydrophobic attraction. Extracting the microscopic association or dissociation rates from experimental data is complicated by the dissociation events and by the sensitivity of the binding force to temperature (T). Here we introduce a theoretical model that combined with light-scattering experiments allows us to quantify these rates and the reversible binding energy as a function of T. We apply this method to the reversible aggregation of thermoresponsive polystyrene/poly(N-isopropylacrylamide) core-shell nanoparticles, as a model system for biomolecules. We find that the binding energy changes sharply with T, and relate this remarkable switchable behavior to the hydrophobic-hydrophilic transition of the thermosensitive nanoparticles.
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We report on the structural properties of mixed aggregates made from rare-earth inorganic nanoparticles (radius 20 Angstroms) and polyelectrolyte-neutral block copolymers in aqueous solutions. Using scattering experiments and Monte Carlo simulations, we show that these mixed aggregates have a hierarchical core-shell microstructure. The core is made of densely packed nanoparticles and it is surrounded by a corona of neutral chains. This microstructure results from a process of controlled association and confers to the hybrid aggregates a remarkable colloidal stability.
We investigated the reversible ferromagnetic (FM) behavior of pure and Co doped CeO2 nanopowders. The as-sintered samples displayed an increasing paramagnetic contribution upon Co doping. Room temperature FM is obtained simply by performing thermal treatments in vacuum at temperatures as low as 500^{circ}C and it can be switched off by performing thermal treatments in oxidizing conditions. The FM contribution is enhanced as we increase the time of the thermal treatment in vacuum. Those systematic experiments establish a direct relation between ferromagnetism and oxygen vacancies and open a path for developing materials with tailored properties.
The dependence of the surface plasmons resonance (SPR) frequency on the size of gold nanoparticles (GNPs) is experimentally studied. The measured data for the SPR frequency by UV-Vis spectroscopy and GNPs diameter by Dynamic Light Scattering (DLS), Transmission Electron Microscopy (TEM) and Atomic Force Microscopy (AFM) are collected in the course of classical citrate GNPs synthesis. The relationship between the GNPs size and the blue shift of the light absorption is presented. They are fitted by an equation with a single free parameter, the dielectric permittivity of the surrounding media. Thus, the refractive index of the surrounding media is determined, which characterizes the GNPs surface shell.
Biochemical reactions are fundamentally noisy at a molecular scale. This limits the precision of reaction networks, but also allows fluctuation measurements which may reveal the structure and dynamics of the underlying biochemical network. Here, we study non-equilibrium reaction cycles, such as the mechanochemical cycle of molecular motors, the phosphorylation cycle of circadian clock proteins, or the transition state cycle of enzymes. Fluctuations in such cycles may be measured using either of two classical definitions of the randomness parameter, which we show to be equivalent in general microscopically reversible cycles. We define a stochastic period for reversible cycles and present analytical solutions for its moments. Furthermore, we associate the two forms of the randomness parameter with the thermodynamic uncertainty relation, which sets limits on the timing precision of the cycle in terms of thermodynamic quantities. Our results should prove useful also for the study of temporal fluctuations in more general networks.
While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hindered the understanding of non-equilibrium phenomena in diverse settings, from glasses to driven systems to biology. The length of a losslessly compressed data file is a direct measure of its information content: The more ordered the data is, the lower its information content and the shorter the length of its encoding can be made. Here, we describe how data compression enables the quantification of order in non-equilibrium and equilibrium many-body systems, both discrete and continuous, even when the underlying form of order is unknown. We consider absorbing state models on and off-lattice, as well as a system of active Brownian particles undergoing motility-induced phase separation. The technique reliably identifies non-equilibrium phase transitions, determines their character, quantitatively predicts certain critical exponents without prior knowledge of the order parameters, and reveals previously unknown ordering phenomena. This technique should provide a quantitative measure of organization in condensed matter and other systems exhibiting collective phase transitions in and out of equilibrium.
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