Selective One-Dimensional \u3csup\u3e13\u3c/sup\u3eC-\u3csup\u3e13\u3c/sup\u3eC Spin-Diffusion Solid-State Nuclear Magnetic Resonance Methods to Probe Spatial Arrangements in Biopolymers including Plant Cell Walls, Peptides, and Spider Silk

© 2020 American Chemical Society. All rights reserved. Two-dimensional (2D) and 3D through-space 13C-13C homonuclear spin-diffusion techniques are powerful solid-state nuclear magnetic resonance (NMR) tools for extracting structural information from 13C-enriched biomolecules, but necessarily long acquisition times restrict their applications. In this work, we explore the broad utility and underutilized power of a chemical shift-selective one-dimensional (1D) version of a 2D 13C-13C spin-diffusion solid-state NMR technique. The method, which is called 1D dipolar-assisted rotational resonance (DARR) difference, is applied to a variety of biomaterials including lignocellulosic plant cell walls, microcrystalline peptide fMLF, and black widow dragline spider silk. 1D 13C-13C spin-diffusion methods described here apply in select cases in which the 1D 13C solid-state NMR spectrum displays chemical shift-resolved moieties. This is analogous to the selective 1D nuclear Overhauser effect spectroscopy (NOESY) experiment utilized in liquid-state NMR as a faster (1D instead of 2D) and often less ambiguous (direct sampling of the time domain data, coupled with increased signal averaging) alternative to 2D NOESY. Selective 1D 13C-13C spin-diffusion methods are more time-efficient than their 2D counterparts such as proton-driven spin diffusion (PDSD) and dipolar-assisted rotational resonance. The additional time gained enables measurements of 13C-13C spin-diffusion buildup curves and extraction of spin-diffusion time constants TSD, yielding detailed structural information. Specifically, selective 1D DARR difference buildup curves applied to 13C-enriched hybrid poplar woody stems confirm strong spatial interaction between lignin and acetylated xylan polymers within poplar plant secondary cell walls, and an interpolymer distance of ∼0.45-0.5 nm was estimated. Additionally, Tyr/Gly long-range correlations were observed on isotopically enriched black widow spider dragline silks

Water quality is a poor predictor of recreational hotspots in England

Maintaining and improving water quality is key to the protection and restoration of aquatic ecosystems, which provide important benefits to society. In Europe, the Water Framework Directive (WFD) defines water quality based on a set of biological, hydro-morphological and chemical targets, and aims to reach good quality conditions in all river bodies by the year 2027. While recently it has been argued that achieving these goals will deliver and enhance ecosystem services, in particular recreational services, there is little empirical evidence demonstrating so. Here we test the hypothesis that good water quality is associated with increased utilization of recreational services, combining four surveys covering walking, boating, fishing and swimming visits, together with water quality data for all water bodies in eight River Basin Districts (RBDs) in England. We compared the percentage of visits in areas of good water quality to a set of null models accounting for population density, income, age distribution, travel distance, public access, and substitutability. We expect such association to be positive, at least for fishing (which relies on fish stocks) and swimming (with direct contact to water). We also test if these services have stronger association with water quality relative to boating and walking alongside rivers, canals or lakeshores. In only two of eight RBDs (Northumbria and Anglian) were both criteria met (positive association, strongest for fishing and swimming) when comparing to at least one of the null models. This conclusion is robust to variations in dataset size. Our study suggests that achieving the WFD water quality goals may not enhance recreational ecosystem services, and calls for further empirical research on the connection between water quality and ecosystem services

High Throughput Screening Technologies in Biomass Characterization

Biomass analysis is a slow and tedious process and not solely due to the long generation time for most plant species. Screening large numbers of plant variants for various geno-, pheno-, and chemo-types, whether naturally occurring or engineered in the lab, has multiple challenges. Plant cell walls are complex, heterogeneous networks that are difficult to deconstruct and analyze. Macroheterogeneity from tissue types, age, and environmental factors makes representative sampling a challenge and natural variability generates a significant range in data. Using high throughput (HTP) methodologies allows for large sample sets and replicates to be examined, narrowing in on more precise data for various analyses. This review provides a comprehensive survey of high throughput screening as applied to biomass characterization, from compositional analysis of cell walls by NIR, NMR, mass spectrometry, and wet chemistry to functional screening of changes in recalcitrance via HTP thermochemical pretreatment coupled to enzyme hydrolysis and microscale fermentation. The advancements and development of most high-throughput methods have been achieved through utilization of state-of-the art equipment and robotics, rapid detection methods, as well as reduction in sample size and preparation procedures. The computational analysis of the large amount of data generated using high throughput analytical techniques has recently become more sophisticated, faster and economically viable, enabling a more comprehensive understanding of biomass genomics, structure, composition, and properties. Therefore, methodology for analyzing large datasets generated by the various analytical techniques is also covered

Recovery of low molecular weight compounds from alkaline pretreatment liquor via membrane separations

Lignin is an abundant renewable resource that is a promising substrate for upgrading to fuels and chemicals. However, lignin-rich biorefinery streams are often physically and chemically complex, and could benefit substantially from fractionation. In this work, a membrane process was developed to fractionate low molecular weight (LMW) lignin-related compounds (molecular weight (MW) < 1000 Da) from a lignin-rich, alkaline pretreated liquor (APL) prepared from pretreatment of corn stover with NaOH. The developed membrane process exhibits up to 98.5% rejection of high molecular weight (HMW) (MW > 1000 Da) species and generates a permeate stream with >80% recovery of LMW lignin-related compounds including aromatic species such as p-coumarate and ferulate, resulting in a 6-fold enrichment in LMW organic compounds relative to the crude APL. Experimental batch data were used to develop a detailed process model of an industrial scale, continuous membrane filtration system. The open-source model has several independent process inputs, such as the concentration of target compounds, feed flow rate, volume recovery, and membrane selectivity. This process model was used to show that the system has a low estimated energy demand (0.75 kW h m−3 permeate) and was used to identify primary cost drivers, including the membrane material cost. These results offer a key step towards a scalable, low energy, and cost-effective lignin MW fractionation method with implications for both improving product isolation from lignin and improving carbon yields across the biorefinery