Imbibition dynamics of nano-particulate ink-jet drops on micro-porous media

Ink-jet printing of nano-metallic colloidal fluids on to porous media such as coated papers has become a viable method to produce conductive tracks for low-cost, disposable printed electronic devices. However, the formation of well-defined and functional tracks on an absorbing surface is controlled by the drop imbibition dynamics in addition to the well-studied post-impact drop spreading behavior. This study represents the first investigation of the real-time imbibition of ink-jet deposited nano-Cu colloid drops on to coated paper substrates. In addition, the same ink was deposited on to a non-porous polymer surface as a control substrate. By using high-speed video imaging to capture the deposition of ink-jet drops, the time-scales of drop spreading and imbibition were quantified and compared with model predictions. The influences of the coating pore size on the bulk absorption rate and nano-Cu particle distribution have also been studied.

Optimization of nanotube electrode geometry in a liquid crystal media from wavefront aberrations

This paper presents experimental optimization of number and geometry of nanotube electrodes in a liquid crystal media from wavefront aberrations for realizing nanophotonic devices. The refractive-index gradient profiles from different nanotube geometries-arrays of one, three, four, and five-were studied along with wavefront aberrations using Zernike polynomials. The optimizations help the device to make application in the areas of voltage reconfigurable microlens arrays, high-resolution displays, wavefront sensors, holograms, and phase modulators. © 2012 Optical Society of America.

Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features. © 2012 IEEE.

Sorting nanoparticles by centrifugal fields in clean media

The on-demand availability of nanomaterials with selected size and well-defined chemical/physical properties is of fundamental importance for their widespread application. We report two clean, rapid, and non-destructive approaches for nanoparticle (NP) size selection in centrifugal fields. The first exploits rate zonal separation in a high viscosity gradient. The second exploits selective sedimentation of NPs with different sizes. These methods are here applied to metallic nanoparticles (MNPs) with different compositions and surface chemistry, dispersed either in water or organic solvents. The approach is general and can also be exploited for the separation of NPs of any material. We selectively sort both Au and AgNPs with sizes in the 10-30 nm range, achieving chemical-free MNPs with low polydispersivity. We do not use solutes, thus avoiding contamination, and only require low centrifugal fields, easily achievable in benchtop systems. © 2013 American Chemical Society.