Controlling Self-Assembly and Topology at Micro-Nano Length Scales Using a Contact-Free Mixed Nanocolloid Droplet Architecture.

Spatially varying the ordering of colloids of multiple sizes at micro-nano scales finds application in different industrial processes including manufacturing of photonic crystals. In this work, we showcase a unique physics-based architecture through which we have been able to control the morphology of the precipitates evolving out of the drying of a contact-free droplet at micro to nano length scales. We show that by varying the relative concentration of the larger sized colloids, one can modulate evaporation, subsequent particle transport, and particle ordering at the droplet interface, thereby controlling the rates of certain instabilities like buckling. In this way, we have produced evaporation-induced self-assembly structures (devoid of any substrate effect) with striking topological and surface features. Furthermore, we proved that these instabilities can be further tuned using a measured amount of external heating through the alteration of the evaporation rates. Notwithstanding, we also quantified that the ordering of the mixed colloids varies, in a spatial sense, across the droplet surface, exhibiting unique patterns, porosity, and lattice arrangements, all at the nanoscale. The results assure that the fine-tuning of the macroscale parameters like heating rate and particle loading can be used to fine-tune the micro-nanoscale features in a droplet-based high-throughput bottom-up framework.

Reconstruction of elongated bubbles fusing the information from multiple optical probes through a Bayesian inference technique.

In this communication, a novel optical technique has been proposed for the reconstruction of the shape of a Taylor bubble using measurements from multiple arrays of optical sensors. The deviation of an optical beam passing through the bubble depends on the contour of bubble surface. A theoretical model of the deviation of a beam during the traverse of a Taylor bubble through it has been developed. Using this model and the time history of the deviation captured by the sensor array, the bubble shape has been reconstructed. The reconstruction has been performed using an inverse algorithm based on Bayesian inference technique and Markov chain Monte Carlo sampling algorithm. The reconstructed nose shape has been compared with the true shape, extracted through image processing of high speed images. Finally, an error analysis has been performed to pinpoint the sources of the errors.