Communications larger and farther apart, then constant. This easily explains the effect of the tartaric acid content on the platinum particle size in the impreg--impreg Pt-SiO, series. Moreover, the ap-Recently, polypyrrole composites containing nano-size particles of metal oxides (y-Fe,O,, a-Fe,O,, SnO,, WO,, and TiO,) have been prepared and considered for electromagnetic interference shielding['] and microwave absorbing applications. These composites were prepared by polymerizing pyrrole (chemically or electrochemically) in an aqueous

Thanks to advances in chemical synthesis that enable control over the size, structure, properties, and functionalization, magnetic nanoparticles (NPs) present unique opportunities in areas as diverse as data storage, cancer treatment, and biomedical imaging. While superparamagnetism dominates the properties of magnetic NPs, a quantitative understanding of superparamagnetic blocking in NP assemblies remains elusive. We address this challenge here via comprehensive magnetic characterization and analysis of soft ferromagnetic NP ensembles based on Ni. NPs were synthesized by the injection of a Ni−oleylamine (OAm) complex into 200 °C trioctylphosphine (TOP), with size control achieved via the TOP:OAm ratio, reaction time, and differential centrifugation. X-ray diffraction, electron microscopy, and various spectroscopies reveal polycrystalline/twinned face-centered-cubic Ni NPs with mean diameters from 4 to 22 nm, dispersities down to 10%, and TOP and OAm ligands. Superparamagnetic blocking temperatures are carefully determined, quantitatively accounting for the substantial yet frequently ignored effects of dispersity, resulting in mean blocking temperatures spanning 5 K to >300 K. Even accounting for an ∼1 nm-thick magnetically dead/canted shell (deduced from magnetization) and the temperature dependence of the Ni magnetocrystalline anisotropy, these mean blocking temperatures cannot be quantitatively reproduced. Remarkably, this discrepancy is substantially resolved by accounting for shape anisotropy effects that result from even modest average deviations from spherical shapes. A quantitative understanding of the size-dependent blocking temperature of soft ferromagnetic metallic NP assemblies is thus achieved, with no adjustable fitting parameters, by quantitatively accounting for the size distribution, effective ferromagnetic volume, temperature-dependent magnetocrystalline anisotropy, and random shape anisotropy. While frequently ignored, the characterization of such factors is thus vital, paving the way to quantitative understanding of superparamagnetism in other magnetic NP systems.

Achieving thermodynamic faithfulness and transferability across state points is an outstanding challenge in the bottom-up coarse graining of molecular models, with many efforts focusing on augmenting the form of coarse-grained interaction potentials to improve transferability. Here, we revisit the critical role of the simulation ensemble and the possibility that even simple models can be made more predictive through a smarter choice of ensemble. We highlight the efficacy of coarse graining from ensembles where variables conjugate to the thermodynamic quantities of interest are forced to respond to applied perturbations. For example, to learn activity coefficients, it is natural to coarse grain from ensembles with spatially varying external potentials applied to one species to force local composition variations and fluctuations. We apply this strategy to coarse grain both an atomistic model of water and methanol and a binary mixture of spheres interacting via Gaussian repulsions and demonstrate near-quantitative capture of activity coefficients across the whole composition range. Furthermore, the approach is able to do so without explicitly measuring and targeting activity coefficients during the coarse graining process; activity coefficients are only computed after-the-fact to assess accuracy. We hypothesize that ensembles with applied thermodynamic potentials are more “thermodynamically informative.” We quantify this notion of informativeness using the Fisher information metric, which enables the systematic design of optimal bias potentials that promote the learning of thermodynamically faithful models. The Fisher information is related to variances of structural variables, highlighting the physical basis underlying the Fisher information’s utility in improving coarse-grained models.

Polymer formulations possessing mesostructures or phase coexistence are challenging to simulate using atomistic particle-explicit approaches due to the disparate time and length scales, while the predictive capability of field-based simulations is hampered by the need to specify interactions at a coarser scale (e.g., χ-parameters). To overcome the weaknesses of both, we introduce a bottom-up coarse-graining methodology that leverages all-atom molecular dynamics to molecularly inform coarser field-theoretic models. Specifically, we use relative-entropy coarse-graining to parametrize particle models that are directly and analytically transformable into statistical field theories. We demonstrate the predictive capability of this approach by reproducing experimental aqueous poly­(ethylene oxide) (PEO) cloud-point curves with no parameters fit to experimental data. This synergistic approach to multiscale polymer simulations opens the door to de novo exploration of phase behavior across a wide variety of polymer solutions and melt formulations.

Understanding the phase behavior of polyelectrolyte coacervation is crucial for many applications, including consumer formulations, wet adhesives, processed food, and drug delivery. However, in most cases, modeling coacervation is not easily accessed by molecular simulation methods due to the long-range nature of electrostatic forces and the typically high molecular weights of the species involved. We present a modeling strategy to study complex coacervation leveraging the strengths of both particle simulations and polymer field theory. Field theory is uniquely suited to capture larger-length scales that are inaccessible to particle simulations, but its predictive capability is limited by the need to specify emergent parameters. Using model coacervate-forming systems consisting of poly(acrylic acid) and poly(allylamine hydrochloride), we show an original way to use small-scale, all-atom simulations to parameterize field-theoretic models via the relative entropy coarse-graining approach. The dependence of coacervation on the salt concentration, molecular weight, and charge stoichiometry is predicted without fitting to experimental data and is consistent with experimental trends including asymmetric phase behavior from non-stoichiometric mixtures of polyelectrolytes. This demonstrates a unique simulation approach to study phase behavior in coacervate-forming systems, which is particularly useful when chemical specificity is of interest.