Gold Nanocluster Agglomeration Kinetic Studies: Evidence for Parallel Bimolecular Plus Autocatalytic Agglomeration Pathways as a Mechanism-Based Alternative to an Avrami-Based Analysis

Abstract

The most extensive and highest quality Au 0 n nanocluster agglomeration size vs time TEM data set yet obtained are analyzed by a nanoparticle size vs time equation that is derived herein for parallel bimolecular (B + B → C, rate constant k 3 ) and autocatalytic (B + C → 1.5C, rate constant k 4 ) agglomeration steps of preformed nanoclusters, B. The results show that the size vs time data are well fit by the new size vs time equation. The fits and resultant k 3 and k 4 rate constants yield several interesting insights that are presented and discussed, including the finding that to date k 4 > k 3 , that is, that the autocatalytic agglomeration rate constant is faster than the bimolecular rate constant, at least for the cases examined to date. The results of the effects of added TOABr (tetraoctylammonium bromide) on the 180 °C agglomeration k 3 and k 4 rate constants in unstirred diphenylmethane solvent are also presented and discussed, the TOABr being added originally to compact the nanoclusters double layer thereby helping induce agglomeration. The observed different [TOABr] effects on k 3 vs k 4 also provide prima facie evidence that the two agglomeration steps are fundamentally different and unique. Literature size vs time data, from El-Sayed et al. for Pd nanocluster agglomeration, are also fit as a further test of the new, mechanism-based size vs time equation. The combined results, showing good fits by the k 3 and k 4 steps to the Au 0 n as well as literature Pd, Pt, and Ir nanocluster data, provide good support for the underlying B + B → C and B + C → 1.5C agglomeration steps themselves as well as for the assumptions and math behind the new size vs time equation. The significance of the results in general, as well as for future measurements of k 3 and k 4 rate constants as a preferred way to quantitate nanocluster stability in solution, are also presented and discussed. Most significant, however, is that as a result of the present work one can now use chemical equations and associated, mechanistically rigorously defined concepts of bimolecular (B + B → C; rate constant k 3 ) and autocatalytic (B+ C → 1.5C; rate constant k 4 ) agglomeration to analyze and describe nanoparticle agglomeration rather than the harder to interpret, more obscure n and k parameters from an Avrami-type, semiempirical curve fit.

Kinetics and Mechanisms of Aggregative Nanocrystal Growth

“…52,56,60,133−136 Finke first reported a four-step mechanism combining classical and aggregative growth in 2005. 45,46,49,50,63 Following these initial studies, the contribution of aggregative processes to nanocrystal growth, in certain cases, has been well-established (see below).…”

Kinetics of reaction of gold nanoparticles following partial removal of stabilizers

“…Finke and co-workers have reported on the transition metal nanocluster agglomeration kinetics, by monitoring all three phases of nanocluster formation and ageing (Hornstein and Finke 2004). Additionally, the agglomeration kinetics of dodecanethiol-stabilized gold nanoclusters, in the presence of different concentrations of tetraoctylammonium bromide (TOABr), has been explained based on bimolecular and autocatalytic pathway (Finney et al 2012). Moreover, polymer-like kinetics involving growth of Bi 2 S 3 nanowires has been reported (Cademartiri et al 2012).…”

Effect of volume-fraction dependent agglomeration of nanoparticles on the thermal conductivity of nanocomposites: Applications to epoxy resins, filled by SiO2, AlN and MgO nanoparticles

“…We start by considering a simple kinetic mechanism called "aggregative growth" [57]. Accordingly, the kinetics of agglomeration are governed by the following equations…”