Orbiting and Lidars for Earth and Planetary Applications.

At NASA Goddard Space Flight Center, we have been developing spaceborne lidar instruments for space sciences. We have successfully flown several missions in the past based on mature diode pumped solid-state laser transmitters. In recent years, we have been developing advanced laser technologies for applications such as laser spectroscopy, laser communications, and interferometry.

Projecting non-diffracting waves with intermediate-plane holography.

We introduce intermediate-plane holography, which substantially improves the ability of holographic trapping systems to project propagation-invariant modes of light using phase-only diffractive optical elements. Translating the mode-forming hologram to an intermediate plane in the optical train can reduce the need to encode amplitude variations in the field, and therefore complements well-established techniques for encoding complex-valued transfer functions into phase-only holograms. Compared to standard holographic trapping implementations, intermediate-plane holograms greatly improve diffraction efficiency and mode purity of propagation-invariant modes, and so increase their useful non-diffracting range.

Photokinetic analysis of the forces and torques exerted by optical tweezers carrying angular momentum.

The theory of photokinetic effects expresses the forces and torques exerted by a beam of light in terms of experimentally accessible amplitude and phase profiles. We use this formalism to develop an intuitive explanation for the performance of optical tweezers operating in the Rayleigh regime, including effects arising from the influence of light's angular momentum. First-order dipole contributions reveal how a focused beam can trap small objects, and what features limit the trap's stability.

Machine-learning approach to holographic particle characterization.

Holograms of colloidal dispersions encode comprehensive information about individual particles' three-dimensional positions, sizes and optical properties. Extracting that information typically is computationally intensive, and thus slow. Here, we demonstrate that machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers.

Modeling the structure and composition of nanoparticles by extended X-ray absorption fine-structure spectroscopy.

Many metal clusters in the 1-nm size range are catalytically active, and their enhanced reactivity is often attributed to their size, structure, morphology, and details of alloying. Synchrotron sources provide a wide range of opportunities for studying catalysis. Among them, extended X-ray absorption fine-structure (EXAFS) spectroscopy is the premier method for investigating structure and composition of nanocatalysts.