Gold nanoparticles decorated with oligo(ethylene glycol) thiols:enhanced Hofmeister effects in colloid−protein mixtures

Oligo(ethylene glycol) (OEG) thiol self-assembled monolayer (SAM) decorated gold nanoparticles (AuNPs) have potential applications in bionanotechnology due to their unique property of preventing the nonspecific absorption of protein on the colloidal surface. For colloid-protein mixtures, a previous study (Zhang et al. J. Phys. Chem. A 2007, 111, 12229) has shown that the OEG SAM-coated AuNPs become unstable upon addition of proteins (BSA) above a critical concentration, c*. This has been explained as a depletion effect in the two-component system. Adding salt (NaCl) can reduce the value of c*; that is, reduce the stability of the mixture. In the present work, we study the influence of the nature of the added salt on the stability of this two-component colloid-protein system. It is shown that the addition of various salts does not change the stability of either protein or colloid in solution in the experimental conditions of this work, except that sodium sulfate can destabilize the colloidal solutions. In the binary mixtures, however, the stability of colloid-protein mixtures shows significant dependence on the nature of the salt: chaotropic salts (NaSCN, NaClO4, NaNO3, MgCl2) stabilize the system with increasing salt concentration, while kosmotropic salts (NaCl, Na2SO4, NH4Cl) lead to the aggregation of colloids with increasing salt concentration. These observations indicate that the Hofmeister effect can be enhanced in two-component systems; that is, the modification of the colloidal interface by ions changes significantly the effective depletive interaction via proteins. Real time SAXS measurements confirm in all cases that the aggregates are in an amorphous state.

Mixtures of probabilistic principal component analysers

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.