Synthesis and Characterization of Novel Polyimides Based on Pyridine-containing Diamine

A new aromatic diamine monomer containing pyridine unit, 2,6-bis （4-aminophenoxy- 4＇-benzoyl）pyridine（BABP）, was synthesized in three steps, starting from 2,6-pyridinedicarboxyl chloride. A series of novel pyridine-containing polyimides were prepared v/a the polycondensation of BABP with various aromatic dianhydrides through poly（amic acid） precursors, and thermal or chemical imidization of the precursors. The polyimides exhibit desirable properties, e.g., good solubility in N-methyl-2-pyrrolidone and m-cresol, excellent thermal stability and film-forming capability, as well as high inherent viscosity, indicating high molecular weight.     

Controllable growth of chains and grids from polyoxomolybdate building blocks linked by silver(I) dimers

Molecular growth processes utilizing a beta-octamolybdate synthon and {Ag2} dimers are described and the directing influence of "encapsulating" cations and coordinating solvent is also demonstrated. The growth of two 1D chains, (nBu4N)2n[Ag2Mo8O26]n (1) and (nBu4N)2n[Ag2Mo8O26(CH3CN)2]n (2), is achieved when nBu4N+ ions are used, and the diameter of the chains can be expanded by the coordination of CH3CN solvent (2). The formation of a type of gridlike structure in which 1D chains are crossed-over each other in alternatively packed layers is achieved in DMSO as the solvent; DMSO acts as a linking group to give (nBu4N)2n[Ag2Mo8O26(dmso)2]n (3), which, similar to 1 and 2, still incorporates the Bu4N+ ions that exert an "encapsulating" influence. However, in (HDMF)n[Ag3(Mo8O26)(dmf)4]n (4) the relatively bulky Bu4N+ ions are exchanged for protonated DMF cations, thereby allowing the chains to condense to a 2D array. The building block concept is further enforced by the isolation of a "monomeric" unit (Ph4P)2[Ag2Mo8O26(dmso)4] (5), which is isolated when the Ph4P+ ions are so "encapsulating" as to prevent aggregation of the {Ag-Mo8-Ag} building blocks. The nature of the AgAg dimers in each of the compounds 1-4 is examined by DFT calculations and the interplay between these Ag-Ag interactions and the structure types is described.     

Two‐Dimensional Holo‐ and Hemidirected Lead(II) Coordination Polymers: Synthetic,Spectroscopic, Thermal,and Structural Studies of [Pb(μ‐SCN)2(μ‐ebp)1.5]n and {[Pb(μ‐OAc)(μ‐ebp)](ClO4)}n (ebp=4,4′‐[(1E)‐Ethane‐1,2‐diyl]bis[pyridine]; OAc=Acetato)

Two new coordination polymers of Pb^II complexes with bridging 4,4′‐[(1E)‐ethane‐1,2‐diyl]bis[pyridine] (ebp), thiocyanato, and acetato ligands, [Pb(μ‐SCN)₂(μ‐ebp)_1.5]_n ( 1 ) and {[Pb(μ‐OAc)(μ‐ebp)](ClO₄)}_n ( 2 ), were synthesized and characterized by elemental analysis, FT‐IR, ¹H‐ and ¹³C‐NMR, thermal analysis, and single‐crystal X‐ray diffraction. In 1 , the Pb²⁺ ions are doubly bridged by both the ebp and the SCN⁻ ligands into a two‐dimensional polymeric network. The seven‐coordinate geometry around the Pb²⁺ ion in 1 is a distorted monocapped trigonal prism, in which the Pb²⁺ ions have a less‐common holodirected geometry. In 2 , the Pb²⁺ ions are bridged by AcO⁻ ligands forming linear chains, which are also further bridged by the neutral ebp ligands into a two‐dimensional polymeric framework. The Pb²⁺ ions have a five‐coordinate geometry with two N‐atoms from two ebp ligands and three O‐atoms of AcO⁻. Although ClO acts as a counter‐ion, it also makes weak interactions with the Pb²⁺ center. The arrangement of the ligands in 2 exhibits hemidirected geometry, and the coordination gap around the Pb²⁺ ion is possibly occupied by a configurationally active lone pair of electrons.     

Carbohydrate-binding module O-mannosylation alters binding selectivity to cellulose and lignin

Improved understanding of the effect of protein glycosylation is expected to provide the foundation for the design of protein glycoengineering strategies. In this study, we examine the impact of O-glycosylation on the binding selectivity of a model Family 1 carbohydrate-binding module (CBM), which has been shown to be one of the primary sub-domains responsible for non-productive lignin binding in multi-modular cellulases. Specifically, we examine the relationship between glycan structure and the binding specificity of the CBM to cellulose and lignin substrates. We find that the glycosylation pattern of the CBM exhibits a strong influence on the binding affinity and the selectivity between both cellulose and lignin. In addition, the large set of binding data collected allows us to examine the relationship between binding affinity and the correlation in motion between pairs of glycosylation sites. Our results suggest that glycoforms displaying highly correlated motion in their glycosylation sites tend to bind cellulose with high affinity and lignin with low affinity. Taken together, this work helps lay the groundwork for future exploitation of glycoengineering as a tool to improve the performance of industrial enzymes.

Improved understanding of the effect of protein glycosylation is expected to provide the foundation for the design of protein glycoengineering strategies.

The cell walls of terrestrial plants primarily comprise the polysaccharides cellulose, hemicellulose, and pectin, as well as the heterogeneous aromatic polymer, lignin. In nature, carbohydrates derived from plant polysaccharides provide a massive carbon and energy source for biomass-degrading fungi, bacteria, and archaea, which together are the primary organisms that recycle plant matter and are a critical component of the global carbon cycle. Across the various environments in which these microbes break down lignocellulose, a few known enzymatic and chemical systems have evolved to deconstruct polysaccharides to soluble sugars.^1–6 These natural systems are, in several cases, being evaluated for industrial use to produce sugars for further conversion into renewable biofuels and chemicals.From an industrial perspective, overcoming biomass recalcitrance to cost-effectively produce soluble intermediates, including sugars for further upgrading remains the main challenge in biomass conversion. Lignin, the evolution of which in planta provided a significant advantage for terrestrial plants to mitigate microbial attack, is now widely recognized as a primary cause of biomass recalcitrance.⁷ Chemical and/or biological processing scenarios of lignocellulose have been evaluated⁸ and several approaches have been scaled to industrial biorefineries to date. Many biomass conversion technologies overcome recalcitrance by partially or wholly removing lignin from biomass using thermochemical pretreatment or fractionation. This approach enables easier polysaccharide access for carbohydrate-active enzymes and/or microbes. There are however, several biomass deconstruction approaches that employ enzymes or microbes with whole, unpretreated biomass.^9,10 In most realistic biomass conversion scenarios wherein enzymes or microbes are used to depolymerize polysaccharides, native or residual lignin remains.^11,12 It is important to note that lignin can bind and sequester carbohydrate-active enzymes, which in turn can affect conversion performance.¹³Therefore, efforts aimed at improving cellulose binding selectivity relative to lignin have emerged as major thrusts in cellulase studies.^14–25 Multiple reports in the past a few years have made exciting new contributions to our collective understanding of how fungal glycoside hydrolases, which are among the most well-characterized cellulolytic enzymes given their importance to cellulosic biofuels production, bind to lignin from various pretreatments.^15,17 Taken together, these studies have demonstrated that the Family 1 carbohydrate-binding modules (CBMs) often found in fungal cellulases are the most relevant sub-domains for non-productive binding to lignin,^15,17,20,26 likely due to the hydrophobic face of these CBMs that is known to be also responsible for cellulose binding (Fig. 1).²⁷Open in a separate windowFig. 1Model of glycosylated CBM binding the surface of a cellulose crystal. Glycans are shown in green with oxygen atoms in red, tyrosines known to be critical to binding shown in purple, and disulfide bonds Cys8–Cys25 and Cys19–Cys35 in yellow.Furthermore, several studies have been published recently using protein engineering of Family 1 CBMs to improve CBM binding selectivity to cellulose with respect to lignin. Of particular note, Strobel et al. screened a large library of point mutations in both the Family 1 CBM and the linker connecting the catalytic domain (CD) and CBM.^21,22 These studies demonstrated that several mutations in the CBM and one in the linker led to improved cellulose binding selectivity compared to lignin. The emerging picture is that the CBM-cellulose interaction, which occurs mainly as a result of stacking between the flat, hydrophobic CBM face (which is decorated with aromatic residues) and the hydrophobic crystal face of cellulose I, is also likely the main driving force in the CBM-lignin interaction given the strong potential for aromatic–aromatic and hydrophobic interactions.Alongside amino acid changes, modification of O-glycosylation has recently emerged as a potential tool in engineering fungal CBMs, which Harrison et al. demonstrated to be O-glycosylated.^28–31 In particular, we have revealed that the O-mannosylation of a Family 1 CBM of Trichoderma reesei cellobiohydrolase I (TrCel7A) can lead to significant enhancements in the binding affinity towards bacterial microcrystalline cellulose (BMCC).^30,32,33 This observation, together with the fact that glycans have the potential to form both hydrophilic and hydrophobic interactions with other molecules, led us to hypothesize that glycosylation may have a unique role in the binding selectivity of Family 1 CBMs to cellulose relative to lignin and as such, glycoengineering may be exploited to improve the industrial performance of these enzymes. To test this hypothesis, in the present study, we systematically probed the effects of glycosylation on CBM binding affinity for a variety of lignocellulose-derived cellulose and lignin substrates and investigated routes to computationally predict the binding properties of different glycosylated CBMs.     

A new study of the enzymatic hydrolysis of carboxymethyl cellulose with a bulk acoustic wave sensor

A new bulk acoustic wave (BAW) cellulase sensing technique, which is based on the enzymatic hydrolysis process of sodium carboxymethylcellulose (CMC) by cellulase, was established. The frequency shift curves of BAW sensor indicated that the viscosity of the tested solutions decreased during the hydrolysis process. The hydrolysis rate of CMC by cellulase was calculated from the frequency shift curves. The hydrolysis rate of CMC under different pH conditions at 30°C showed that cellulase had high hydrolysis ability approximately at pH 5.0. Kinetic parameters (the Michaelis constant K_m and the maximum rate V_max) of the process were estimated by using a linear method of Lineweaver–Burk plot. K_m is 1.95±0.25 mg ml⁻¹ and V_max is −(4.25±0.58)×10⁻³ g^1/2 cm^−3/2 cP^1/2 min⁻¹. Also the activation energy (E_a) of the enzymatic hydrolysis, with a value of 51.99±1.26 kJ mol⁻¹, was estimated in this work.     

A Perturbation Approach to Vector Optimization Problems: Lagrange and Fenchel–Lagrange Duality

Journal of Optimization Theory and Applications - In this paper, we study a general minimization vector problem which is expressed in terms of a perturbation mapping defined on a product of locally...     

Upper bounds of Schubert polynomials

Science China Mathematics - Let w be a permutation of {1, 2, …, n}, and let D(w) be the Rothe diagram of w. The Schubert polynomial ${\mathfrak{S}_w}\left(x \right)$ can be realized as the...     

Existence and Uniqueness of Solutions to Time-delays Stochastic Fractional Differential Equations with Non-Lipschitz Coefficients

In this paper, we consider the existence and uniqueness of solutions to time-varying delays stochastic fractional differential equations (SFDEs) with non-Lipschitz coefficients. By using fractional calculus and stochastic analysis, we can obtain the existence result of solutions for stochastic fractional differential equations.