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Our results demonstrate that it is possible, by means of generation of buckles on the silver nanorod (AgNR) array PDMS substrate, to enhance the Raman signal of P. aeruginosa bacteria due to the formation of high density 'hot spots' among the AgNR arrays which provides better entrapment and increases the net effective contact area of bacteria with the metal surface.

We investigate the control of flow direction around a water vapor bubble using the thermoplasmonic effect of a gold nanoisland film (GNF) under laser irradiation with multiple spots. By focusing a laser spot on the GNF immersed in degassed water, a water vapor bubble with a diameter of ~10 μm is generated. Simultaneously, a sub laser spot was focused next to the bubble to yield a temperature gradient in the direction parallel to the GNF surface. Consequently, rapid flow was generated around the bubble, whose flow direction was dependent on the power of the sub laser spot. The observed flow was well-described using a stokeslet; the latter contained components normal and parallel to the GNF surface and was set to 10 μm above the GNF. This technique allows us to apply a significant force on the microfluid at the vicinity of the wall in the direction parallel to the wall surface, where the flow speed is generally suppressed by viscosity. It is expected to be useful for microfluidic pumping and microfluidic thermal management.

We report the fabrication of anisotropic superhydrophobic surface with dual-scale roughness by the deposition of silver nanorods arrays on prestretched poly(dimethylsiloxane) (PDMS) using oblique angle deposition and subsequent release of strain after the deposition, which resulted in the formation of microbuckles/wrinkles. The amplitude and periodicity of the wrinkles were tuned by varying the prestretching mechanical strain (ε) applied to the PDMS film from 0 to 30% prior to Ag nanorods deposition. The peaks and valleys in the surface topography of Ag nanorods arrays covered PDMS films lead to anisotropic wetting by water droplet. The droplet is free to move along the direction parallel to the wrinkles, but the droplet moving perpendicular to the wrinkles confront energy barrier leading to wetting anisotropy. The anisotropic wettability was tuned from 22 to 37° for 10-30% prestretched PDMS film. The dual scale roughness (nanorods on micro wrinkles) was found to be responsible for the superhydrophobicity (contact angle ∼155°) of the sample prepared for 30% prestretched PDMS film in perpendicular direction. The wetting behavior of the Ag nanorods PDMS film surface was reversibly tuned by applying the mechanical strain, which induces the change in the microscale roughness determined by amplitude (A) and periodicity (λ) of the buckles. Most interestingly, the water droplet also displayed the anisotropy in the roll-off angle. The effect of different A and λ on anisotropic wettability of Ag nanorods arrays/PDMS film was also demonstrated by lattice Boltzmann (LB) modeling. These findings may produce a promising way of controlling the direction of liquid flow such as in microfluidic devices and transportation of the microliter water droplets in a preset direction.

We report a facile method to fabricate novel and recyclable Ag nanoparticle decorated TiO2 nanorod array substrates using a glancing angle deposition (GLAD) technique for photocatalysis and surface enhanced Raman scattering (SERS) applications.

Massive open online courses (MOOCs) are redefining the education system and transcending boundaries posed by traditional courses. With the increase in popularity of online courses, there is a corresponding increase in the need to understand and interpret the communications of the course participants. Identifying topics or aspects of conversation and inferring sentiment in online course forum posts can enable instructor interventions to meet the needs of the students, rapidly address course-related issues, and increase student retention. Labeled aspect-sentiment data for MOOCs are expensive to obtain and may not be transferable between courses, suggesting the need for approaches that do not require labeled data. We develop a weakly supervised joint model for aspectsentiment in online courses, modeling the dependencies between various aspects and sentiment using a recently developed scalable class of statistical relational models called hinge-loss Markov random fields. We validate our models on posts sampled from twelve online courses, each containing an average of 10,000 posts, and demonstrate that jointly modeling aspect with sentiment improves the prediction accuracy for both aspect and sentiment.