Regulation of Nontraditional Intrinsic Luminescence (NTIL) in Hyperbranched Polysiloxanes by Adjusting Alkane Chain Lengths: Mechanism, Film Fabrication, and Chemical Sensing

Biocompatible polymers with nontraditional intrinsic luminescence (NTIL) possess the advantages of environmental friendliness and facile structural regulation. To regulate the emission wavelength of polymers with NTIL, the alkane chain lengths of hyperbranched polysiloxane (HBPSi) are adjusted. Optical investigation shows that the emission wavelength of HBPSi is closely related to the alkane chain lengths; namely, short alkane chains will generate relatively long-wavelength emission. Electronic communication among functional groups is responsible for the emission. In a concentrated solution, HBPSi molecules aggregate together due to the strong hydrogen bond and amphiphilicity, and the functional groups in the aggregate are so close that their electron clouds are overlapped and generate spatial electronic delocalizations. HBPSi with shorter alkane chains will generate larger electronic delocalizations and emit longer-wavelength emissions. Moreover, these polymers show excellent applications in the fabrication of fluorescent films and chemical sensing. This work could provide a strategy for regulating the emission wavelengths of unconventional fluorescent polymers

Structure Regulation and Application of Bagasse-Based Porous Carbon Material Based on H₂O₂‑Assisted Hydrothermal Treatment

Biomass-based porous carbon has attracted much attention of researchers due to its wide range of raw materials, low cost, and other advantages. It has been also widely used in energy storage devices such as lithium-ion batteries. The carbonization process and final morphology of biomass are indirectly affected by the surface modification and structure optimization of the original biomass. The focus of this study is to prepare porous carbon, which can be used as an electrode material of a lithium-ion battery by surface modification and structure regulation of biomass pretreatment. In this study, the method of H2O2-assisted hydrothermal treatment was used to prepare a kind of bagasse based porous carbon materials that can be used in lithium-ion batteries. The reaction process of H2O2 and bagasse under hydrothermal conditions was explored and described. The results showed that H2O2 promoted the hydrolysis and oxidation of lignocellulose in bagasse and the small molecules obtained from lignocellulose hydrolysis under hydrothermal conditions would be repolymerized to form carbon spheres. The prepared carbon material was applied to the lithium-ion battery. Under a current density of 0.1 A g–1, the specific capacity of 891 mAh g–1 in the first cycle was displayed, and a specific capacity of 453 mAh g–1 was maintained after 150 cycles. At the same time, it showed a good rate performance. After 10 cycles at current densities of 0.1, 0.2, 0.5, 1, 2, and 5 A g–1, the specific capacities of 545, 421, 307, 245, 195, and 133 mAh g–1 were obtained. All the raw materials and products used in this experiment are harmless to the environment, which has certain guiding significance for the structural regulation and green synthesis of lignocellulosic biomass-derived carbon materials

Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier

<div><p>The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at <a href="http://datamining.xmu.edu.cn/software/hpfp" target="_blank">http://datamining.xmu.edu.cn/software/hpfp</a>.</p> </div

Influential factors for success rate of 1^st and 2^nd hierarchical layers.

<p>Influential factors for success rate of 1^st and 2^nd hierarchical layers.</p

Performance on different classifiers on protein fold recognition (one sequence in each family).

<p>Performance on different classifiers on protein fold recognition (one sequence in each family).</p

Extraction process of the 188-dimensional (188D) feature vectors (FV).

<p>Sequences are input and processed by analyzing amino acid composition, distribution and protein physicochemical properties, FV1–FV188 are output as feature vectors.</p

Preliminary results^* of PCA analysis.

*<p>Eigenvalue > 3.</p

Algorithm 1. Circulating Combination of EFSS.

<p>Algorithm 1. Circulating Combination of EFSS.</p

Comparison of success rate among several studies.

<p>Our work outperforms all previous works with an accuracy of 74.21%.</p

The architecture of our ensemble classifier.

<p>The training dataset is classified by all base classifiers. After K-Means clustering and circulating combination the best ensemble result is achieved.</p