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Efficient catalytic ozonation by ruthenium nanoparticles supported on SiO2 or TiO2: Towards the use of a non-woven fiber paper as original support(Chemical Engineering Journal, Elsevier BV, 2015-12-24) [Article]This work focuses on the use of Ru(0) nanoparticles as heterogeneous catalyst for ozone decomposition and radical production. In a first set of experiments, the nanoparticles have been deposited on two inorganic supports (TiO2 or SiO2) by a wet impregnation approach. This study confirmed the high potential of Ru nanoparticles as active species for ozone decomposition at pH 3, since the ozone half-life time decreases by a factor 20-25, compared to the reference experiment carried out without any catalyst. The enhancement of the ozone decomposition kinetics provided an improved radical production and a higher transient radical concentration in a shorten ozone exposure. Consequently, lower oxidant dosage and contact time would be necessary. Thus, very significant atrazine consumption kinetics enhancements were measured. In a second set of experiments, a non-woven fiber paper composed of a TiO2/SiO2/zeolite mixture has been evaluated as an original support for ruthenium nanoparticles. Even if lower ozone decomposition kinetics was observed compared to TiO2 or SiO2, this support would be a promising alternative to inorganic powders to avoid the catalyst recovery step and to design reactors such as tubular reactors. A new numerical procedure is presented for the evaluation of the transient HO° concentration and of the Rct.
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Methods to synthesize NiPt bimetallic nanoparticles by a reversed-phase microemulsion, deposition of NiPt bimetallic nanoparticles on a support, and application of the supported catalyst for CO2 reforming of methane(2015-09-24) [Patent]Embodiments of the present disclosure provide for NiPt nanoparticles, compositions and supports including NiPt nanoparticles, methods of making NiPt nanoparticles, methods of supporting NiPt nanoparticles, methods of using NiPt nanoparticles, and the like.
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SupportNet: a novel incremental learning framework through deep learning and support data(Cold Spring Harbor Laboratory, 2018-05-08) [Preprint]Motivation: In most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems. Results: Here we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data. Availability: Our program is accessible at: \url{https://github.com/lykaust15/SupportNet}.
