Hamid Reza Seyf

Nanoscale optomechanical actuators for controlling mechanotransduction in living cells.

To control receptor tension optically at the cell surface, we developed an approach involving optomechanical actuator nanoparticles that are controlled with near-infrared light. Illumination leads to particle collapse, delivering piconewton forces to specific cell surface receptors with high spatial and temporal resolution. We demonstrate optomechanical actuation by controlling integrin-based focal adhesion formation, cell protrusion and migration, and T cell receptor activation.

A method for distinguishing between propagons, diffusions, and locons

The majority of intuition on phonon transport has been derived from studies of homogenous crystalline solids, where the atomic composition and structure are periodic. For this specific class of materials, the solutions to the equations of motions for the atoms (in the harmonic limit) result in plane wave modulated velocity fields for the normal modes of vibration. However, it has been known for several decades that whenever a system lacks periodicity, either compositional or structural, the normal modes of vibration can still be determined (in the harmonic limit), but the solutions take on different characteristics and many modes may not be plane wave modulated. Previous work has classified the types of vibrations into three primary categories, namely, propagons, diffusions, and locons. One can use the participation ratio to distinguish locons, from propagons and diffusons, which measures the extent to which a mode is localized. However, distinguishing between propagons and diffusons has remained a challe...

Thermophotovoltaics: a potential pathway to high efficiency concentrated solar power

A high temperature thermophotovoltaic (TPV) system is modeled and its system level performance is assessed in the context of concentrated solar power (CSP) with thermal energy storage (TES). The model includes the treatment of the emitter and the heat transfer fluid that draws thermal energy from the TES, which then allows for the identification and prioritization of the most important TPV cell/module level properties that should be optimized to achieve maximum performance. The upper limiting efficiency for an idealized system is then calculated, which shows that TPV with TES may one day have the potential to become competitive with combined cycle turbines, but could also offer other advantages that would give CSP an advantage over fossil based alternatives. The system concept is enabled by the usage of liquid metal as a high temperature heat transfer and TES fluid. The system concept combines the great economic advantages of TES with the potential for low cost and high performance derived from TPV cells fabricated on reusable substrates, with a high reflectivity back reflector for photon recycling.

Empirical interatomic potentials optimized for phonon properties

Molecular dynamics simulations have been extensively used to study phonons and gain insight, but direct comparisons to experimental data are often difficult, due to a lack of accurate empirical interatomic potentials for different systems. As a result, this issue has become a major barrier to realizing the promise associated with advanced atomistic-level modeling techniques. Here, we present a general method for specifically optimizing empirical interatomic potentials from ab initio inputs for the study of phonon transport properties, thereby resulting in phonon optimized potentials. The method uses a genetic algorithm to directly fit the empirical parameters of the potential to the key properties that determine whether or not the atomic level dynamics and most notably the phonon transport are described properly.Molecular dynamics: Optimized potentials for studying phononsA framework has been developed that can optimize the potentials needed to more accurately study phonons using molecular dynamics. Molecular dynamics simulations are an indispensable tool for studying how atoms interact. Despite their widespread use, however, it is often difficult to determine the potentials needed to accurately describe the various interactions involved for phonons, which are the excitations that underpin physical properties such as thermal conductivity. An international team of researchers led by professor Asegun Henry from the Georgia Institute of Technology presents an approach, based on a genetic algorithm, that can optimise the empirical interatomic potentials for phonons from first principles inputs, that can be used in classical molecular dynamics simulations. And although they demonstrate this method with semiconducting silicon and germanium, it should be extendable to alloys and disordered systems.