The aggregative growth and oriented attachment of nanocrystals and nanoparticles are reviewed, and they are contrasted to classical LaMer nucleation and growth, and to Ostwald ripening. Kinetic and mechanistic models are presented, and experiments directly observing aggregative growth and oriented attachment are summarized. Aggregative growth is described as a nonclassical nucleation and growth process. The concept of a nucleation function is introduced, and approximated with a Gaussian form. The height (Γ max ) and width (Δt n ) of the nucleation function are systematically varied by conditions that influence the colloidal stability of the small, primary nanocrystals participating in aggregative growth. The nucleation parameters Γ max and Δt n correlate with the final nanocrystal mean size and size distribution, affording a potential means of achieving nucleation control in nanocrystal synthesis.

The thermal coarsening (180 °C) of decanethiolate-capped Au nanocrystals is studied at various tetraoctylammonium bromide concentrations. The coarsening kinetics are determined by measuring nanocrystal size distributions (CSDs) as a function of time. The results are shown to be consistent with aggregative nucleation and growth. For each kinetic trial, the time dependence of the aggregative nucleation rate is extracted from the early time CSDs and fitted by a Gaussian profile. The height of the profile is the maximum nucleation rate, Γmax, and the 2σ width is the time window for nucleation, Δt n. These nucleation parameters are shown to control the final mean size and size distribution of the coarsened nanocrystals. The coarsening kinetics are influenced by tetraoctylammonium bromide concentration because the nanocrystals are partially electrostatically stabilized.

The kinetics and mechanism of Bi-nanocrystal growth from the precursor Bi[N(SiMe3)2]3 are determined at various Na[N(SiMe3)2] additive concentrations. The results establish that aggregative nucleation and growth processes dominate Bi-nanocrystal formation. The time dependence of the aggregative nucleation rate−the nucleation function−is determined over the range of Na[N(SiMe3)2] concentrations studied. The time width of aggregative nucleation (Δt n) is shown to remain reasonably narrow, and to correlate with the final Bi-nanocrystal size distribution. The maximum aggregative nucleation rate (Γmax) is shown to vary systematically with Na[N(SiMe3)2] concentration, producing a systematic variation in the final nanocrystal mean size. The Na[N(SiMe3)2] additive functions as both a nucleation-control agent and an Ostwald-ripening agent.

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.