Department of

Chemical Engineering

Designing molecular technology for the 21st century with biology and chemistry


 


Professor Themis Matsoukas | Research

Colloid Chemistry of Sol-Gel Nanoparticles

Nanoparticles from various materials can be synthesized in a number of ways. Maintaining a stable suspension of such small particles is, however, a difficult task. Equally difficult is controlling the degree of aggregation in a precise and reversible manner. We are collaborating with with Advanced Cooling Technologies to develop colloidal dispersions with enhanced thermal properties.

Related publications

  • S. Lotfizadeh, T. Desai, and T. Matsoukas. The thermal conductivity of clustered nanocolloids. APL Materials, 2(6):066102, 2014.
  • S. Lotfizadeh and T. Matsoukas. Colloidal thermal fluids. Encyclopedia of Surface and Colloid Science, (accepted), 2014.

Plasma Colloids

Gas-phase processing of materials has several advantages but some important limitations as well. Particles in the gas phase are almost invariably obtained in agglomerated form. This is compounded the lack of tools such as pH agents, ionic strength, solvation forces, or steric interactions to control the growth process. Low-pressure plasmas offer the promise of breaking away from such limitations. Particles in these systems become electrostatically charged and this offers an opportunity to effect interactions at the microscopic level. In collaboration with Advanced Cooling Technologies we are developing a plasma process to synthesize organic nanoparticle and to modify the surfaces of metallic and oxide particles with functional or passivating coatings.

Related publications

Population Balances & Stochastic Modeling of Populations

The growth and disintegration of dispersed systems and the evolution of their size distribution is a problem that arises in several processes. In the presence of coagulation or fragmentation the population balance that governs the evolution of the size distribution is an integro-differential equation. Several difficulties associated with the solution of such equations are circumvented through the use of Monte Carlo (MC) methods. Monte Carlo utilizes probabilistic tools to sample a finite subset of a system in order to infer its properties. We have recently developed a new MC algorithm which we have shown to be both accurate and efficient. Most significantly, the simulation time is not limited by the finite number of particles and the error propagation has been shown to be much slower than conventional techniques.

Related publications

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