Layer‐by‐layer (LbL) assembly is a powerful and versatile technique to deposit functional thin films, but often requires a large number of deposition steps to achieve a film thick enough to provide a desired property. By incorporating amine salts into the cationic polyelectrolyte and its associated rinse, LbL clay‐containing nanocomposite films can achieve much greater thickness (>1 μm) with relatively few deposition cycles (≤6 bilayers). Amine salts interact with nanoclays, causing nanoplatelets to deposit in stacks rather than as individual platelets. This technique appears to be universal, exhibiting thick growth with multiple types of nanoclay, including montmorillonite and vermiculite (VMT), and a variety of amine salts (e.g., hexylamine and diethanolamine). The characteristic order found in LbL‐assembled films is maintained despite the incredible thickness. Films assembled in this manner achieve oxygen transmission rates below 0.009 cc m−2 d−1 atm−1 with just 6 bilayers (BLs) of chitosan/VMT deposited. These thick clay‐based thin films also impart exceptional flame resistance. A 2‐BL film renders a 3.2 mm polystyrene plate self‐extinguishing, while an 8‐BL film (3.9 μm thick) prevents ignition entirely. This ability to generate much thicker clay‐based multilayers with amine salts opens up tremendous potential for these nanocoatings in real world applications.

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While the importance of the Food-Energy-Water Nexus (FEW-N) has been widely accepted, a holistic approach to facilitate decision making in FEW-N systems, along with a quantitative index assessing the integrated FEW-N performance is rather lacking. In this work, we propose a FEW-N metric along with a framework to facilitate decision making for FEW-N process systems through a FEW-N integrated approach. The framework and metric are illustrated through a case study on a dairy production and processing plant. The dairy industry is a significant user of water and energy, with water being a top issue for most dairy industries and organizations worldwide. Following the framework, we develop a mixed-integer scheduling model, with alternative pathways, that faithfully replicated the major food, energy, and water aspects of a real cottage-cheese production plant. Using the developed FEW-N metric we were able to optimize the cottage-cheese plant process and observe different trade-offs between the FEW-N elements.