a b s t r a c tAs the attraction of creating biofuels and bio-based chemicals from lignocellulosic biomass has increased, researchers have been challenged with developing a better understanding of lignin structure, quantity and potential uses. Lignin has frequently been considered a waste-product from the deconstruction of plant cell walls, in attempts to isolate polysaccharides that can be hydrolyzed and fermented into fuel or other valuable commodities. In order to develop useful applications for lignin, accurate analytical instrumentation and methodologies are required to qualitatively and quantitatively assess, for example, what the structure of lignin looks like or how much lignin comprises a specific feedstock's cellular composition. During the past decade, various diverse strategies have been employed to elucidate the structure and composition of lignin. These techniques include using two-dimensional nuclear magnetic resonance to resolve overlapping spectral data, measuring biomass with vibrational spectroscopy to enable modeling of lignin content or monomeric ratios, methods to probe and quantify the linkages between lignin and polysaccharides, or refinements of established methods to provide higher throughput analyses, less use of consumables, etc. This review seeks to provide a comprehensive overview of many of the advancements achieved in evaluating key lignin attributes. Emphasis is placed on research endeavored in the last decade.

Lignocellulosic biomass has been proposed as an option for reducing global dependence on nonrenewable energy sources, such as oil. Selection and development of biomass feedstocks that efficiently yield the maximum fuel or biomaterial requires the availability of reliable methods for compositional and structural characterization of plant material. Many standard methods for biomass analysis are laborious and slow, and employ a variety of harsh reagents requiring some degree of remediation. The use of simpler and more rapid spectroscopic methods has proved invaluable in analyzing biomass. In the twenty-first century, researchers have employed techniques such as Raman, mid-infrared, and near-infrared spectroscopy for a wide range of applications in endeavors to further understand biofuel feedstocks. While many methods remain time consuming and expensive, a growing interest in high-throughput spectroscopic techniques has provided faster and larger scale feedstock screening for desirable traits. This review seeks to provide an overview of both high-throughput techniques and those requiring longer analysis times but still providing abundant qualitative and quantitative data. While applications of these instrumental methods have been researched for decades, more recent developments will be discussed here.

SummaryThe productivity of plants as biofuel or biomaterial crops is established by both the yield of plant biomass per unit area of land and the efficiency of conversion of the biomass to biofuel. Higher yielding biofuel crops with increased conversion efficiencies allow production on a smaller land footprint minimizing competition with agriculture for food production and biodiversity conservation. Plants have traditionally been domesticated for food, fibre and feed applications. However, utilization for biofuels may require the breeding of novel phenotypes, or new species entirely. Genomics approaches support genetic selection strategies to deliver significant genetic improvement of plants as sources of biomass for biofuel manufacture. Genetic modification of plants provides a further range of options for improving the composition of biomass and for plant modifications to assist the fabrication of biofuels. The relative carbohydrate and lignin content influences the deconstruction of plant cell walls to biofuels. Key options for facilitating the deconstruction leading to higher monomeric sugar release from plants include increasing cellulose content, reducing cellulose crystallinity, and/or altering the amount or composition of noncellulosic polysaccharides or lignin. Modification of chemical linkages within and between these biomass components may improve the ease of deconstruction. Expression of enzymes in the plant may provide a cost-effective option for biochemical conversion to biofuel.

BackgroundIn order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS).ResultsThe PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks.ConclusionEucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Pulmonary toxicity studies on carbon nanotubes focus primarily on as-produced materials and rarely are guided by a life cycle perspective or integration with exposure assessment. Understanding toxicity beyond the as-produced, or pure native material, is critical, due to modifications needed to overcome barriers to commercialization of applications. In the first series of studies, the toxicity of as-produced carbon nanotubes and their polymer-coated counterparts was evaluated in reference to exposure assessment, material characterization, and stability of the polymer coating in biological fluids. The second series of studies examined the toxicity of aerosols generated from sanding polymer-coated carbon-nanotube-embedded or neat composites. Postproduction modification by polymer coating did not enhance pulmonary injury, inflammation, and pathology or in vitro genotoxicity of as-produced carbon nanotubes, and for a particular coating, toxicity was significantly attenuated. The aerosols generated from sanding composites embedded with polymer-coated carbon nanotubes contained no evidence of free nanotubes. The percent weight incorporation of polymer-coated carbon nanotubes, 0.15% or 3% by mass, and composite matrix utilized altered the particle size distribution and, in certain circumstances, influenced acute in vivo toxicity. Our study provides perspective that, while the number of workers and consumers increases along the life cycle, toxicity and/or potential for exposure to the as-produced material may greatly diminish.

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The creation of fuels, chemicals, and materials from plants can aid in replacing products fabricated from non-renewable energy sources. Before using biomass in downstream applications, it must be characterized to assess chemical traits, such as cellulose, lignin, or lignin monomer content, or the sugars released following an acid or enzymatic hydrolysis. The measurement of these traits allows researchers to gage the recalcitrance of the plants and develop efficient deconstruction strategies to maximize yields. Standard methods for assessing biomass phenotypes often have experimental protocols that limit their use for screening sizeable numbers of plant species. Raman spectroscopy, a non-destructive, non-invasive vibrational spectroscopy technique, is capable of providing qualitative, structural information and quantitative measurements. Applications of Raman spectroscopy have aided in alleviating the constraints of standard methods by coupling spectral data with multivariate analysis to construct models capable of predicting analytes. Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line. Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information. This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

The thermal maturity of shale is often measured by vitrinite reflectance (VRo). VRo measurements for the Devonian-Mississippian black shale source rocks evaluated herein predicted thermal immaturity in areas where associated reservoir rocks are oil-producing. This limitation of the VRo method led to the current evaluation of Raman spectroscopy as a suitable alternative for developing correlations between thermal maturity and Raman spectra. In this study, Raman spectra of Devonian-Mississippian black shale source rocks were regressed against measured VRo or sample-depth. Attempts were made to develop quantitative correlations of thermal maturity. Using sample-depth as a proxy for thermal maturity is not without limitations as thermal maturity as a function of depth depends on thermal gradient, which can vary through time, subsidence rate, uplift, lack of uplift, and faulting. Correlations between Raman data and vitrinite reflectance or sample-depth were quantified by peak-fitting the spectra. Various peak-fitting procedures were evaluated to determine the effects of the number of peaks and maximum peak widths on correlations between spectral metrics and thermal maturity. Correlations between D-frequency, G-band full width at half maximum (FWHM), and band separation between the G-and D-peaks and thermal maturity provided some degree of linearity throughout most peak-fitting assessments; however, these correlations and those calculated from the G-frequency, D/G FWHM ratio, and D/G peak area ratio also revealed a strong dependence on peak-fitting processes. This dependency on spectral analysis techniques raises questions about the validity of peak-fitting, particularly given the amount of subjective analyst involvement necessary to reconstruct spectra. This research shows how user interpretation and extrapolation affected the comparability of different samples, the accuracy of generated trends, and therefore, the potential of the Raman spectral method to become an industry benchmark as a thermal maturity probe. A Raman method devoid of extensive operator interaction and data manipulation is quintessential for creating a standard method.