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Binding interactions between low molecular weight heparin (LMWH) and heparin-binding peptides (HBP) have been applied as a strategy for the assembly of hydrogels that are capable of sequestering growth factors and delivering them in a controlled manner. In this work, the assembly of four-arm star poly(ethylene glycol) (PEG)-LMWH conjugate with PEG-HBP conjugates has been investigated. The interactions between LMWH and the heparin-binding regions of antithrombin III (ATIII) or the heparin interacting protein (HIP) have been characterized via heparin affinity chromatography and surface plasmon resonance (SPR); results indicate that the two peptides have slightly different affinities for heparin and LMWH, and bind LMWH with micromolar affinity. Solutions of the PEG-LMWH and of mixtures of the PEG-LMWH and PEG-HBP were characterized via both bulk rheology and laser tweezer microrheology. Interestingly, solutions of PEG-LMWH (2.5 wt % in PBS) form hydrogels in the absence of PEG-ATIII or PEG-HIP, with storage moduli, determined via bulk rheological measurements, in excess of the loss moduli over frequencies of 0.1-100 Hz. The addition of PEG-ATIII or PEG-HIP increases the moduli in direct proportion to the number of cross-links introduced. Characterization of the hydrogels via microrheology shows the gel microstructure is composed of polymer-rich fibrillar structures surrounded by polymer-depleted buffer. Potential applications of these hydrogels are discussed.

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Social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive Sybils and uses them to perform various malicious activities. Therefore, Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into two categories: Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels. LBP-based methods are not scalable, and they cannot guarantee convergence. In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to label noise. We first propose a framework to unify RW-based and LBP-based methods. Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph. Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils. We compare SybilSCAR with state-of-the-art RW-based methods and LBP-based methods both theoretically and empirically. Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods. Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils, and our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; and 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.

Three phenylenediamine (PD) monomers, o-phenylenediamine (OPD), m-phenylenediamine (MPD), and p-phenylenediamine (PPD), were used to prepare the functionalized graphene (PD/rGO) networks. The results obtained from a series of chemical, thermal, and rheological analyses elucidated the mechanism of the covalent bonding and the existence of cross-linked graphene networks. The measured XRD patterns and molecular dynamic calculations discovered that those PPD and MPD molecules could enlarge graphene interlayer spacing to 1.41 and 1.30 nm, respectively, while OPD molecules were disorderly bonded or nonbonded to the basal planes of graphene layers, resulting in small and variable interlayer distances. The loadings of PD monomers were optimized to achieve superior supercapacitor performance. Electrochemical study showed that PPD/rGO exhibited the largest specific capacitance of 422 F/g with excellent cycling stability and low charge transfer resistance. The large variations in the capacitance values among PD/rGO networks with different PD monomers were explained by the difference in the graphene nanostructures, reversible redox transitions, and charge transfer characteristics. Particularly, density function theory calculations were adopted to compare electronic properties of the PD/rGO composites, including formation energy, electron density distribution, HOMO energy levels, and electron density of states near the Fermi level.

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Eight different three-parallel distributed activation energy model (DAEM) reaction model processes, which were used to describe the pyrolysis of eight lignocellulosic biomass samples very well, were analyzed by means of the Cai−Chen iterative linear integral isoconversional method. The activation energies obtained from the isoconversional method were independent of the heating rate, which indicated that the isoconversional analysis was valid for the pyrolysis of lignocellulosic biomass. The resulting effective activation energies of the pyrolysis of all lignocellulosic biomass samples showed strong dependence upon the extent of conversion: in the low range of conversion, the effective activation energies increase (about 190− 210 kJ mol −1 ) with increasing the extent of conversion; in the medium range of conversion, the effective activation energies exhibit a practically constant value (about 210 kJ mol −1 ); and in the high range of conversion, the effective activation energies increase (about 210−290 kJ mol −1 ) with increasing the extent of conversion.

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