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The materials currently used in proton-exchange membrane fuel cells (PEMFCs) require complex control of operating conditions to make them sufficiently durable to permit commercial deployment. One of the major materials challenges to allow simplification of fuel cell operating strategies is the discovery of catalyst supports that are much more stable to oxidative decomposition than currently used carbon blacks. Here we report the synthesis and characterization of Ti(0.7)W(0.3)O2 nanoparticles (approximately 50 nm diameter), a promising doped metal oxide that is a candidate for such a durable catalyst support. The synthesized nanoparticles were platinized, characterized by electrochemical testing, and evaluated for stability under PEMFC and other oxidizing acidic conditions. Ti(0.7)W(0.3)O2 nanoparticles show no evidence of decomposition when heated in a Nafion solution for 3 weeks at 80 °C. In contrast, when heated in sulfuric, nitric, perchloric, or hydrochloric acid, the oxide reacts to form salts such as titanylsulfatehydrate from sulfuric acid. Electrochemical tests show that rates of hydrogen oxidation and oxygen reduction by platinum nanoparticles supported on Ti(0.7)W(0.3)O2 are comparable to those of commercial Pt on carbon black.

We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the latent emotional state of the user. Thus, we focus on predicting the emotion word tags attached by users to their Tumblr posts, treating these as "self-reported emotions. " We demonstrate that our multimodal model combining both text and image features outperforms separate models based solely on either images or text. Our model's results are interpretable, automatically yielding sensible word lists associated with emotions. We explore the structure of emotions implied by our model and compare it to what has been posited in the psychology literature, and validate our model on a set of images that have been used in psychology studies. Finally, our work also provides a useful tool for the growing academic study of images-both photographs and memes-on social networks.

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