Communication: Resonance reaction in diffusion-influenced bimolecular reactions

We investigate the influence of a stochastically fluctuating step-barrier potential on bimolecular reaction rates by exact analytical theory and stochastic simulations. We demonstrate that the system exhibits a new "resonant reaction" behavior with rate enhancement if an appropriately defined fluctuation decay length is of the order of the system size. Importantly, we find that in the proximity of resonance, the standard reciprocal additivity law for diffusion and surface reaction rates is violated due to the dynamical coupling of multiple kinetic processes. Together, these findings may have important repercussions on the correct interpretation of various kinetic reaction problems in complex systems, as, e.g., in biomolecular association or catalysis

A general theory of DNA-mediated and other valence-limited interactions

We present a general theory for predicting the interaction potentials between DNA-coated colloids, and more broadly, any particles that interact via valence-limited ligand-receptor binding. Our theory correctly incorporates the configurational and combinatorial entropic factors that play a key role in valence-limited interactions. By rigorously enforcing self-consistency, it achieves near-quantitative accuracy with respect to detailed Monte Carlo calculations. With suitable approximations and in particular geometries, our theory reduces to previous successful treatments, which are now united in a common and extensible framework. We expect our tools to be useful to other researchers investigating ligand-mediated interactions. A complete and well-documented Python implementation is freely available at http://github.com/patvarilly/DNACC .Comment: 18 pages, 10 figure

On the design of precision nanomedicines

Tight control on the selectivity of nanoparticles' interaction with biological systems is paramount for the development of targeted therapies. However, the large number of tunable parameters makes it difficult to identify optimal design "sweet spots" without guiding principles. Here, we combine superselectivity theory with soft matter physics into a unified theoretical framework and we prove its validity using blood brain barrier cells as target. We apply our approach to polymersomes functionalized with targeting ligands to identify the most selective combination of parameters in terms of particle size, brush length and density, as well as tether length, affinity, and ligand number. We show that the combination of multivalent interactions into multiplexed systems enable interaction as a function of the cell phenotype, that is, which receptors are expressed. We thus propose the design of a "bar-coding" targeting approach that can be tailor-made to unique cell populations enabling personalized therapies

Competitive adsorption of multiple proteins to nanoparticles: the Vroman effect revisited

Proteins adsorbed from the blood plasma change nanoparticles inter- actions with the surrounding biological environment. In general, the ad- sorption of multiple proteins has a non-monotonic time dependence, that is, proteins adsorbed at first may slowly be replaced by others. This “Vro- man effect” leads to a highly dynamic protein corona on nanoparticles that profoundly influences the immune response of the body. Thus, the temporal evolution of the corona must be taken into account when consid- ering applications of nanocarriers in, e.g., nanomedicine or drug delivery. Up to now, the Vroman effect is explained solely in terms of diffusion: Smaller proteins which diffuse faster are adsorbed first while larger ones, having a stronger interaction with the surface, are preferred at equilib- rium. Here we use dynamic density functional theory (DDFT) including steric and electrostatic interactions to provide a full model for the tem- poral evolution of the protein corona. In particular, we demonstrate that proper consideration of all interactions leads to Vroman-like adsorption signatures in widely different scenarios. Moreover, consideration of ener- getic terms predicts both competitive as well as co-operative adsorption. In this way, DDFT provides a reacher picture of the evolution of the dynamic protein coron

Catalysis by metallic nanoparticles in solution: thermosensitive microgels as nanoreactors

Metallic nanoparticles have been used as catalysts for various reactions, and the huge literature on the subject is hard to overlook. In many applications, the nanoparticles must be affixed to a colloidal carrier for easy handling during catalysis. These "passive carriers" (e.g. dendrimers) serve for a controlled synthesis of the nanoparticles and prevent coagulation during catalysis. Recently, hybrids from nanoparticles and polymers have been developed that allow us to change the catalytic activity of the nanoparticles by external triggers. In particular, single nanoparticles embedded in a thermosensitive network made from poly(N-isopropylacrylamide) (PNIPAM) have become the most-studied examples of such hybrids: immersed in cold water, the PNIPAM network is hydrophilic and fully swollen. In this state, hydrophilic substrates can diffuse easily through the network, and react at the surface of the nanoparticles. Above the volume transition located at 32°C, the network becomes hydrophobic and shrinks. Now hydrophobic substrates will preferably diffuse through the network and react with other substrates in the reaction catalyzed by the enclosed nanoparticle. Such "active carriers", may thus be viewed as true nanoreactors that open new ways for the use of nanoparticles in catalysis. In this review, we give a survey on recent work done on these hybrids and their application in catalysis. The aim of this review is threefold: we first review hybrid systems composed of nanoparticles and thermosensitive networks and compare these "active carriers" to other colloidal and polymeric carriers (e.g. dendrimers). In a second step we discuss the model reactions used to obtain precise kinetic data on the catalytic activity of nanoparticles in various carriers and environments. These kinetic data allow us to present a fully quantitative comparison of different nanoreactors. In a final section we shall present the salient points of recent efforts in the theoretical modeling of these nanoreactors. By accounting for the presence of a free-energy landscape for the reactants' diffusive approach towards the catalytic nanoparticle, arising from solvent-reactant and polymeric shell-reactant interactions, these models are capable of explaining the emergence of all the important features observed so far in studies of nanoreactors. The present survey also suggests that such models may be used for the design of future carrier systems adapted to a given reaction and solvent

Catalyzed Bimolecular Reactions in Responsive Nanoreactors

We describe a general theory for surface-catalyzed bimolecular reactions in responsive nanoreactors, catalytically active nanoparticles coated by a stimuli-responsive 'gating' shell, whose permeability controls the activity of the process. We address two archetypal scenarios encountered in this system: The first, where two species diffusing from a bulk solution react at the catalyst's surface; the second where only one of the reactants diffuses from the bulk while the other one is produced at the nanoparticle surface, e.g., by light conversion. We find that in both scenarios the total catalytic rate has the same mathematical structure, once diffusion rates are properly redefined. Moreover, the diffusional fluxes of the different reactants are strongly coupled, providing a richer behavior than that arising in unimolecular reactions. We also show that in stark contrast to bulk reactions, the identification of a limiting reactant is not simply determined by the relative bulk concentrations but controlled by the nanoreactor shell permeability. Finally, we describe an application of our theory by analyzing experimental data on the reaction between hexacyanoferrate (III) and borohydride ions in responsive hydrogel-based core-shell nanoreactors.Comment: 9 pages, 4 figure

Novel approaches to acoustic immunosensing of extracellular vesicles

Extracellular vesicles (EVs) constitute a promising source of biomarkers for disease diagnostics and can be obtained via liquid biopsies from various bodily fluids. While much progress has been made in recent years, challenges remain on the sensitivity, specificity and clinical implementation of current analytical workflows

Nanoscale friction of biomimetic hair surfaces

We investigate the nanoscale friction between biomimetic hair surfaces using chemical colloidal probe atomic force microscopy experiments and nonequilibrium molecular dynamics simulations. In the experiments, friction is measured between water-lubricated silica surfaces functionalised with monolayers formed from either octadecyl or sulfonate groups, which are representative of the surfaces of virgin and ultimately bleached hair, respectively. In the simulations, friction is monitored between coarse-grained model hair surfaces with different levels of chemical damage, where a specified amount of grafted octadecyl groups are randomly replaced with sulfonate groups. The sliding velocity dependence of friction in the simulations can be described using an extended stress-augmented thermally activation model. As the damage level increases in the simulations, the friction coefficient generally increases, but its sliding velocity-dependence decreases. At low sliding velocities, which are closer to those encountered experimentally and physiologically, we observe a monotonic increase of the friction coefficient with damage ratio, which is consistent with our new experiments using biomimetic surfaces and previous ones using real hair. This observation demonstrates that modified surface chemistry, rather than roughness changes or subsurface damage, control the increase in nanoscale friction of bleached or chemically damaged hair. We expect the methods and biomimetic surfaces proposed here to be useful to screen the tribological performance of hair care formulations both experimentally and computationally

Combinatorial entropy behaviour leads to range selective binding in ligand-receptor interactions

From viruses to nanoparticles, constructs functionalized with multiple ligands display peculiar binding properties that only arise from multivalent effects. Using statistical mechanical modelling, we describe here how multivalency can be exploited to achieve what we dub range selectivity, that is, binding only to targets bearing a number of receptors within a specified range. We use our model to characterise the region in parameter space where one can expect range selective targeting to occur, and provide experimental support for this phenomenon. Overall, range selectivity represents a potential path to increase the targeting selectivity of multivalent constructs

Combinatorial entropy behaviour leads to range selective binding in ligand-receptor interactions

From viruses to nanoparticles, constructs functionalized with multiple ligands display peculiar binding properties that only arise from multivalent effects. Using statistical mechanical modelling, we describe here how multivalency can be exploited to achieve what we dub range selectivity, that is, binding only to targets bearing a number of receptors within a specified range. We use our model to characterise the region in parameter space where one can expect range selective targeting to occur, and provide experimental support for this phenomenon. Overall, range selectivity represents a potential path to increase the targeting selectivity of multivalent constructs