Fuzzy Learning of Talbot Effect Guides Optimal Mask Design for Proximity Field Nanopatterning Lithography

Processing methods used in photonics and nanotechnology possess many limitations restricting their application areas such as high cost, inability to produce fine details, problems with scalability, and long processing time. Proximity field nanopatterning is a lithography method which surpasses these limitations. By using interference patterns produced by a two-dimensional phase mask, the technique is able to generate a submicron detailed exposure on a millimeter-size slab of light sensitive photopolymer, which is then developed like a photographic plate to reveal three-dimensional interference patterns from the phase mask. While it is possible to use simulations to obtain the interference patterns produced by a phase mask, realizing the mask dimensions necessary for producing a desired interference pattern is analytically challenging due to the intricacies of light interactions involved in producing the final interference pattern. An alternative method is to iteratively optimize the phase mask until the interference patterns obtained converge to the desired pattern. However, depending on the optimization technique used, one either risks a significant probability of failure or requires a prohibitive number of iterations. We argue that an optimization technique that is to take advantage of the physics of the problem using machine learning methods (here fuzzy learning) can lead to competent mask design. This technique is described in this letter.     

Influence of inhibitor addition on the corrosion protection performance of sol–gel coatings on AA2024

Sol–gel films are actively investigated during the last decade as possible candidates for environmentally friendly pre-treatments. However, the important drawback in this case is the lack of active corrosion protection and self-healing ability.     

CFD Prediction of Flow and Homogenization in a Stirred Vessel: Part I Vessel with One and Two Impellers

A simulation of flow field and tracer homogenization was performed using the commercial CFD software FLUENT 6.1. The aim is to investigate the potential of CFD software to predict concentration distribution of added tracer in cylindrical vessels. The calculated results – dimensionless velocity profiles, power and pumping numbers, dimensionless concentration curves, and mixing times – were compared with experiments in stirred vessels. In Part I, the study was performed for vessels agitated by one or two impellers on a centric shaft. Two different impellers were used – a 6‐bladed 45° pitched blade turbine and a standard Rushton turbine. The standard k‐? turbulence model and multiple reference frames method were used for the simulations. The influence of the grid type was also investigated; three types of grid – a structured, unstructured and a special user‐defined grid – were studied.