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000844251 1001_ $$0P:(DE-HGF)0$$aGonzález-Burgos, Marina$$b0
000844251 245__ $$aCrowding the Environment of Single-Chain Nanoparticles: A Combined Study by SANS and Simulations
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000844251 520__ $$aWe present an investigation by combining small-angle neutron scattering (SANS) and coarse-grained molecular dynamics (MD) simulations on the conformational properties of single-chain nanoparticles (SCNPs) in crowded macromolecular solutions. By using linear chains as crowders, SANS shows a crossover from almost unperturbed SCNP conformations in dilute conditions toward a continuous collapse of the macromolecule with increasing crowding. This collapse starts when the total concentration of the solution reaches the value of the overlap concentration of the pure SCNP solutions. MD simulations suggest the generalizability of these experimental findings and extend them to the case when the SCNPs themselves are used as crowders—a situation which in real systems leads to unavoidable formation of aggregates, as shown here by SANS and DLS. Exploiting the simulations, we have calculated the contact probability and the distance between monomers as functions of the contour distance between them; the results suggest that crumpled globular conformations are generally adopted by SCNPs in crowded macromolecular solutions. In the case of linear crowders, the SCNPs show, at fixed monomer concentration, a nonmonotonic dependence of their collapse on the length of the crowders.
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000844251 65017 $$0V:(DE-MLZ)GC-1602-2016$$2V:(DE-HGF)$$aPolymers, Soft Nano Particles and Proteins$$x0
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000844251 7001_ $$00000-0002-5137-4649$$aArbe, Arantxa$$b1$$eCorresponding author
000844251 7001_ $$00000-0001-9971-0763$$aMoreno, Angel J.$$b2
000844251 7001_ $$0P:(DE-HGF)0$$aPomposo, José A.$$b3
000844251 7001_ $$0P:(DE-Juel1)130905$$aRadulescu, Aurel$$b4
000844251 7001_ $$0P:(DE-HGF)0$$aColmenero, Juan$$b5
000844251 773__ $$0PERI:(DE-600)1491942-4$$a10.1021/acs.macromol.7b02438$$gVol. 51, no. 4, p. 1573 - 1585$$n4$$p1573 - 1585$$tMacromolecules$$v51$$x1520-5835$$y2018
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