Zeolites and amorphous silica-alumina (ASA), which both provide Brønsted acid sites (BASs), are the most extensively used solid acid catalysts in the chemical industry. It is widely believed that BASs consist only of tetra-coordinated aluminum sites (AlIV) with bridging OH groups in zeolites or nearby silanols on ASA surfaces. Here we report the direct observation in ASA of a new type of BAS based on penta-coordinated aluminum species (AlV) by 27Al-{1H} dipolar-mediated correlation two-dimensional NMR experiments at high magnetic field under magic-angle spinning. Both BAS-AlIV and -AlV show a similar acidity to protonate probe molecular ammonia. The quantitative evaluation of 1H and 27Al sites demonstrates that BAS-AlV co-exists with BAS-AlIV rather than replaces it, which opens new avenues for strongly enhancing the acidity of these popular solid acids.
This paper presents a new active learning paradigm which considers not only the uncertainty of the classifier but also the diversity of the corpus. The two measures for uncertainty and diversity were combined using the MMR (Maximal Marginal Relevance) method to give the sampling scores in our active learning strategy. We incorporated MMR-based active machinelearning idea into the biomedical namedentity recognition system. Our experimental results indicated that our strategies for active-learning based sample selection could significantly reduce the human effort.
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