Periodic mesoporous organosilicas (PMOs) with controlled structures have been synthesized by using cetyltrimethylammonium bromide (CTAB) and sodium perfluorooctanoate (PFONa) as co-templates, 1,2-bis (triethoxysilyl)ethane (BTEE) as an organosilica precursor. By increasing the weight ratio of PFONa/CTAB, a structure transformation from a cubic (Pm-3n) to a two-dimensional hexagonal (p6m) mesostructure and then to multilamellar vesicles can be observed. The cubic and hexagonal samples have similar particle size (200-750 nm), pore size (2.6 and 2.8 nm, respectively), total pore volume (approximately 0.7 cm3/g), and surface area (approximately 900 m2/g), providing ideal candidates to study the peptide enrichment performance influenced simply by pore symmetries. Matrix-assisted laser desorption ionization time-of-flight mass spectroscopy (MALDI-TOF MS) analysis indicates that PMO with a cubic (Pm-3n) structure is more effective in small molecular weight peptides enrichment compared with PMO with a hexagonal structure, showing the importance of mesostructural control for targeted applications. The phenomena can be attributed to the cage-type structure of the Pm-3n symmetry, which possesses cages with a relatively larger pore size and connectivity with a relatively smaller size. It is suggested that the pore entrances with small size are responsible for entrapping small molecular weight peptides. Our study may shed light on the designed synthesis of functional porous materials with controlled structures and enhanced performance in peptides enrichment.

Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

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