Relative Significance of the Negative Impacts of Hemicelluloses on

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Relative significance of the negative impacts of hemicelluloses on enzymatic cellulose hydrolysis is dependent on lignin content: evidences from substrate structural features and protein adsorption Hongmei Chen, Xuebing Zhao, and Dehua Liu ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.6b01540 • Publication Date (Web): 03 Oct 2016 Downloaded from http://pubs.acs.org on October 7, 2016

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ACS Sustainable Chemistry & Engineering

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Gradient

Elution

Moving

Boundary

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Electrophoresis enables rapid analysis of acids in

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complex biomass-derived streams

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Matthew S. Munson†,‡,≠, Eric M. Karp¶, Claire T. Nimlos¶, Marc Salit†,‡,≠, and Gregg T.

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Beckham¶,*

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† Joint Initiative for Metrology in Biology, 443 Via Ortega, Shriram Center, Stanford University, Stanford CA 94305

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‡ Genome Scale Measurements Group, 443 Via Ortega, Shriram Center, National Institute of Standards and Technology, Stanford, CA 94305

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≠ Department of Bioengineering, Stanford University, 443 Via Ortega, Shriram Center, Stanford, CA 94305

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¶ National Bioenergy Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden CO 80401

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* Corresponding Author Email [email protected]

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Key words

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Biomass conversion, Biorefinery, Lignin, Analytical Chemistry, GEMBE

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ACS Paragon Plus Environment

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Abstract

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Biomass conversion processes such as pretreatment, liquefaction, and pyrolysis often produce

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complex mixtures of intermediates that are a substantial challenge to rapidly and reliably

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analyze. To more comprehensively and efficiently characterize these complex streams, new

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techniques are needed to track species through biomass deconstruction and conversion processes.

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Here, we present the application of an emerging analytical method, Gradient Elution Moving

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Boundary Electrophoresis (GEMBE), to quantify a suite of acids in a complex, biomass-derived

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streams from alkaline pretreatment of corn stover. GEMBE offers distinct advantages over

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common chromatography-spectrometry analytical approaches in terms of analysis time, sample

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preparation requirements, and cost of equipment. As demonstrated here, GEMBE is able to track

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17 distinct compounds (oxalate, formate, succinate, malate, acetate, glycolate, protocatechuate,

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3-hydroxypropanoate, lactate, glycerate, 2-hydroxybutanoate, 4-hydroxybenzoate, vanillate, p-

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coumarate, ferulate, sinapate, and acetovanillone). The lower limit of detection was compound

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dependent and ranged between 0.9 µmol/L and 3.5 µmol /L. Results from GEMBE were similar

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to recent results from an orthogonal method. Overall, GEMBE offers a rapid, robust approach to

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analyze complex biomass-derived samples, and given the ease and convenience of deployment,

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may offer an analytical solution for online tracking of multiple types of biomass streams.

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Introduction

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Biomass conversion processes often rely on a combination of heat, catalysts, and/or mechanical

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work to process lignocellulosic material. As plant cell walls are complex, composite materials

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comprising several biopolymers, among other components, many biomass conversion processes

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produce highly heterogeneous mixtures of compounds that are challenging to characterize.

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Similar challenges exist in conversion processes of other heterogeneous biomass mixtures such

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as in process streams of municipal solid waste and algae. The development of robust analytical

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approaches to monitor solubilized biomass streams is a critical component of the continued

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development of biomass valorization approaches, as measuring conversion of intermediates to

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products is often both a challenge and a key need for process monitoring.

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Alkaline pretreatment liquor (APL) is an exemplary complex, lignin-rich, solubilized mixture

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generated during high pH treatment of biomass. It is composed of high molecular weight lignin,

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low molecular weight aromatic compounds derived from lignin depolymerization, hydroxy acids

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from sugar degradation reactions, alcohols, aldehydes, and other higher molecular weight

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partially depolymerized biopolymers.1 Due to the high degree of chemical heterogeneity, these

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types of solutions are generally metastable and the addition of solvents, acids, or other agents

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during workup facilitates subsequent reactions making it difficult to derive quantitative

59

information from the original sample. In the case of APL, the solution generally has a pH > 102-3

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and decreasing the solution pH < 5 causes much of the solubilized material to precipitate. This

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characteristic rules out many liquid chromatography techniques that do not operate at high pH or

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that use non-aqueous mobile phases. Additionally, the analytical technique of choice must be

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able to separate a wide range of chemicals with varying functionalities. Thus, there are few non-


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destructive methods available that apply to lignin rich solutions, such as APL, that are produced

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in a biorefinery.1,

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various lignin conversion processes that utilize these complex lignin rich materials is crucial for

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the maturation of the technologies, and will require development of robust analytical techniques

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for tracking organic compounds in these complex substrates.

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However, assessing the merits of biological conversion5-8 or any of the

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Electrophoretic techniques, namely capillary electrophoresis (CE), operate at high pH and have

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demonstrated success in measuring inorganic and some organic components in kraft black

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liquors, but run times are often long and the slate of compounds that can be measured via the CE

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methods are somewhat limited.9-14 Gradient Elution Moving Boundary Electrophoresis

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(GEMBE) offers the potential for faster and more robust analysis of these types of complex

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samples.15-17 GEMBE (represented schematically in Figure 1) is injectionless, and relies on a

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counterflow modulated over the course of the separation to achieve selectivity on the basis of

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electrophoretic mobility. Briefly, a separation channel comprised of fused silica capillary bridges

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a pressure controlled separation buffer reservoir and a sample reservoir. Voltage is applied

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between the reservoirs driving electroosmotic flow towards from the buffer reservoir towards the

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sample, and electromigration of charged species in the sample towards the buffer reservoir. A

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pressure driven flow is superimposed on the electroosmotic flow to control the overall flow rate.

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At the beginning of the separation, the convective velocity at the channel entrance exceeds the

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electrophoretic velocity for all analytes of interest. The applied pressure is then gradually

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reduced resulting in sequential elution of analytes into the capillary in the order of

85

electrophoretic mobility. The eluted boundaries are detectable as steps using a variety of


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detectors, in the case of this work, capacitively coupled contactless conductivity detection

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(C4D).18-21

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GEMBE can be operated using a wide range of conditions (pH, sample conductivity, etc.)22-24

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with relatively inexpensive instrumentation25-27 and can utilize a diversity of detection modes.17,

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25, 27-28

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achieve demanding separations29, can handle samples containing particulates24, 30, and can be

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used for online monitoring of reactions within a sample.25 The use of a “universal detector” is

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enabled by orthogonal analysis methods that allow for peak identification, such as the GCxGC-

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MS method described in our concurrent report.4

It has also been demonstrated that GEMBE can operate in multidimensional modes to

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Once a select set of compounds are identified using a method such as the GCxGC-TOF/MS

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method described by Karp et al.4, GEMBE can be applied to more rapidly analyze samples for

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the identified compounds. In this work, we demonstrate the utility of GEMBE for the

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quantification of the major lignin and sugar degradation products present in corn stover APL

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over a variety of pretreatment conditions. Our results are then quantitatively compared to

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samples representing identical process conditions analyzed by an orthogonal GCxGC-TOF/MS

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method. GEMBE offers distinct advantages in terms of analysis time, sample preparation

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requirements, and cost of equipment over the GCxGC-TOF/MS method.

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Results and Discussion

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Identification of Analytes of Interest


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The use of a C4D as a detection mode in GEMBE requires a priori knowledge of what analytes

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are likely to be present in the samples of interest. This method development knowledge

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accelerates the identification of separation conditions, and enables the assignment of compounds

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to corresponding electropherogram steps. For APL, a list of target analytes was constructed from

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a combination of literature review and orthogonal methods that utilize MS for compound

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identification.4 The compounds of interest are summarized in Table S1. APL is rich in both

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monoaromatic compounds from the breakdown of lignin, and also organic acids from the

115

degradation of carbohydrates.1, 4

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Identification of Separation Conditions

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Optimization of separation conditions is strongly influenced by the physical properties of the

119

analytes of interest. The primary mechanism for resolving species in GEMBE is electrophoretic

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mobility, which is determined by the ratio of the analytes electrical charge and hydrodynamic

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drag. The pH of the separation buffer is therefore a critical parameter in resolving the analytes.

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The effective charge for each species was estimated for pH values from 7 to 11. We used the

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estimated charge-to-mass ratio as a prediction of the relative mobility of each analyte as a

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function of pH, normalized to acetate, which was constant across the pH range examined (Figure

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S1). While these calculations suggest a pH of 10 would be optimal, we implemented our method

126

using a background electrolyte (BGE) that had previously been used for GEMBE17 having a pH

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of 8.3 (250 mM Tris, 250 mM Boric Acid). The consequence of this is that the several of the

128

suspected analytes are not anticipated to be detectable. These analytes are noted in Table S1.

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Other key operating parameters were optimized empirically. Reducing capillary inner diameter

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can increase the peak capacity of the separation at the cost of limit of detection. A capillary ID of

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10 µm was determined to give the best performance for these samples. After selecting a capillary

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ID, the maximum field that can be applied without generating significant Joule heating was

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determined to be 500 V/cm (or 2.5 kV across the 5cm capillary). The applied pressure was

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reduced at a rate of -62.5 Pa/s starting at a value of +30 kPa and ending at -26 kPa. The starting

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pressure for the separation is selected by determining the entry pressure for the fastest analyte of

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interest. To account for experimental fluctuations, the starting pressure was set 1 kPa higher than

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the calculated entry pressure. The ending pressure was determined by finding the applied

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pressure that results in bulk reversal of flow. The separation time scales linearly, while the

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resolution increases with the square root of the pressure ramp rate. We established an arbitrary

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maximum separation time of 1,000 sec, including necessary rinse times to calculate the slowest

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acceptable ramp rate. A characteristic electropherogram is presented in Figure 2A. Each stepwise

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change in conductivity represents the elution front of an analyte (or unresolved group of

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analytes). Interpretation of the electropherograms is aided by computing the time derivative of

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the detector signal (Figure 2B) resulting in a conventional electropherogram. This report contains

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a detailed discussion of the use of GEMBE for the analysis of corn stover APL and comparison

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of the method to our GCxGC-MS method.4 The separation conditions identified are also

148

amenable to analysis of APL derived from other feedstocks (e.g. switchgrass, pine, etc.), and for

149

tracking bioconversion of these APL substrates. Representative electropherograms for samples

150

derived from materials other than corn stover are shown in the SI (Figure S2).

151 152

Sample Properties – Dynamic Range, Conductivity, and Particulates


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The components of APL span a large dynamic range in concentration. This makes it challenging

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to determine the optimal dilution factor for the raw samples in BGE that will allow low

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abundance species to be detected without the high abundance species saturating the detector, or

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electrostatically overloading the separation zones. This challenge was addressed by analyzing

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each sample at two dilution levels. Samples were diluted 100x and 1,000x in BGE. High

158

concentration analytes were quantified from 1,000X dilution, while low concentration analytes

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were determined from the 100X dilution.

160 161

The properties of APL samples vary as a function of pretreatment conditions. The conductivity

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of the sample is of particular importance for GEMBE. The variation in conductivity of APL

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arises primarily as a function of NaOH loading, which is trivially anticipated. The pretreatment

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temperature also impacts the conductivity of the APL, however, as higher temperatures result in

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the production of increasing levels of charged breakdown products. APL samples diluted in BGE

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that have a different conductivity than the BGE will result in field amplified sample stacking at

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the capillary entrance,27, 31 which results in an increase in step height when the sample is less

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conductive than the BGE and a reduction in step height when the sample is more conductive than

169

the BGE. This effect limits the ability to compare samples with different conductivities and

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construct calibrations that apply across all samples. One strategy to mask the variation in

171

conductivity between samples is to increase the conductivity of the BGE so that the conductivity

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of the diluted APL sample is dominated by the BGE conductivity. Raising the conductivity of the

173

BGE has deleterious effects on the separation, as it raises the limit of detection (LOD) and

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reduces resolution by decreasing the maximum applied voltage. Given the high conductivity of

175

the APL samples (between 7 and 120 mS cm-1) examined in this report (Table S2), this approach


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is not feasible. Alternatively, we employed an internal standard to track the extent of sample

177

stacking observed in each separation. Sodium bromide was added to each sample at a

178

concentration of 30 µM. The bromide ion was chosen as the internal standard because it is absent

179

in the APL samples and is both resolved from and higher in electrophoretic mobility than all the

180

other species present including chloride. All calculated step heights are then normalized by the

181

ratio of the bromide step in the sample and a sample containing only sodium bromide. The

182

elution time of the bromide zone was used to align all electropherograms.

183 184

APL samples also contain significant particulates of varying size. GEMBE can be adversely

185

impacted by sample particulates, and strategies have been developed to mitigate these effects.24

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With the conditions employed in this report, biomass particulates were not observed to migrate

187

in the separation channel. We did however observe particulates in the BGE. We hypothesize that

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these were likely borate precipitates. For samples where the passage of a particle would affect

189

determination of the step height associated with an analyte in a given sample, that step was

190

excluded from the data set. The frequency of this occurrence was less than 0.1% of all

191

sample/analyte combinations characterized in this study.

192 193

Assignment of Analytes to Corresponding Electropherogram Steps

194

Each analyte listed Table S1 was prepared separately at 130 µM in BGE with 30 µM NaBr and

195

analyzed, allowing us to determine the elution order and order of magnitude of the detector

196

response. Several of the listed compounds could not be detected. The resultant

197

electropherograms can be superimposed (Figure S3) to determine which analytes, if present in

198

the sample would be resolved. The mapping of compound to step numbers as noted in Figure 2B


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is detailed in Table 1. Of the analytes listed, 15 were able to be adequately resolved from each

200

other, 9 were not detectable, and 10 were detected as part of co-eluting steps. Several other

201

compounds (noted in Table S1) were analyzed in an effort to identify unassigned steps in the

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electropherogram resulting in the identification of steps corresponding to chloride, sulfate,

203

carbonate, and phosphate ions. Detectable analytes were pooled at known concentration ratios

204

and spiked into representative APL samples for final identification electropherogram steps. A

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single representative compound was chosen to represent all the components of co-eluting steps.

206

Several steps in the electropherogram remain unidentified. The presence of unidentified steps

207

underscores the value of companion methods4 that allow for determination of chemical identity

208

to guide peak identification.

209 210

Standards of 2-hydroxybutanoic acid and salicylic acid coelute (step 13), vanillic acid and

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gluconic acid coelute (step 15), and p-coumaric acid and 2-hydroxyisocaproic acid coelute (step

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16). Our orthogonal characterization of comparable APL samples did not detect salicylic acid,

213

and detected gluconic acid and 2-hydroxyisocaproic acid at trace levels.4 We therefore consider

214

step 13 to be representative of the 2-hydroxybutanoic acid concentration, step 15 to be

215

representative of the concentration of vanillic acid, and step 16 to be representative of the

216

concentration of p-coumaric acid. Standards for ferulic acid, syringic acid, vanillin, and catechol

217

co-elute (step 17). Prior results show that vanillin is not present in similar samples, and syringic

218

acid is present at trace levels.4 Catechol was not quantifiable using the GCxGC-MS method, but

219

based on literature reports is likely to be present.1 The height of Step 17 is therefore assumed to

220

be proportional to the response-weighted sum of the concentrations of catechol and ferulic acid.

221

It is not possible to determine the distribution of components in the step from a single


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measurement of height. Catechol is also a key metabolic intermediate in biological conversion of

223

APL.32 Future adaptation of this technique to monitoring metabolite consumption and production

224

during biological conversion will benefit from resolution of catechol from ferulic and syringic

225

acid.

226 227

We also observe several system steps with this BGE. The system effects have an adverse impact

228

on quantification for several of the analytes of interest. The most significant system step arises

229

from the presence of carbonate in the BGE and samples. Carbonate forms a large system step at

230

an elution time of approximately 620 sec. We anticipate that the size and variability of the

231

carbonate step interferes with the quantification of muconate. Calibration of muconate was not

232

attempted in this report, as it was not observed to be at detectable concentrations in the APL

233

samples considered. Formic acid, succinic acid, and malic acid (steps 2, 3, and 4 respectively)

234

coelute with system steps. The contribution of the system steps to the observed signal for each of

235

these analytes was determined by analyzing a blank sample (containing only 30 µM NaBr in

236

BGE) each day, and subtracting the value of step height in the blank from the step height

237

determined in the sample.

238 239

The elution order generally followed the predicted mobility (Figure S1). The notable deviations

240

from predicted mobility are protocatechuic acid and catechol. Both compounds have higher

241

electrophoretic mobility that was anticipated by their charge to mass ratios, particularly catechol,

242

which should be neutrally charged at pH 8.3. As boric acid is known to form covalent anionic

243

complexes with polyhydroxy compounds,33-34 the apparent acceleration of catechol and

244

protocatechuic acid is likely a result of the formation such complexes. The steps corresponding


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to these two compounds are significantly broadened in comparison to the characteristic step

246

width of other analytes, suggesting that under the conditions employed the relevant species are in

247

a dynamic equilibrium. Given these observations, and the number of target analytes that contain

248

multiple hydroxide groups, it is likely that the boric acid in the BGE is contributing to the

249

resolution of the method.

250 251

Calibration

252

System calibration was conducted using a mixed pool design. Six subpools consisting of

253

equimolar mixtures of three compounds were prepared. These pools were mixed at known ratios

254

to produce six calibration pools using a complete latin square design (Table S3). The calibration

255

pools were treated as though they were samples, i.e., diluted 100x and 1,000x prior to analysis

256

with GEMBE. The concentration range for each analyte in the diluted samples is from 6 µM to

257

300 µM. Calibration samples were analyzed in triplicate. Sulfate and phosphate were included in

258

the pool design in order to balance the number of analytes in each subpool. These analytes were

259

not of sufficient interest in our applications to warrant analysis. Calibration curves for 16 of the

260

calibrants are shown in Figure S4. The limit of detection (LOD) and limit of quantitation (LOQ)

261

are defined in this paper as 3 and 10 times the ratio of estimated standard error of the intercept

262

and the estimated slope of the regression line, respectively. The LOD and LOQ for each

263

compound are reported in Table 2, in units of µmol L-1, and mg mL-1.

264

We were not able to directly calibrate 3-hydroxypropionic acid because standards of sufficient

265

purity are not available. We applied the calibration curve for lactic acid to approximate

266

quantification of 3-hydroxypropionic acid. The two compounds are structural isomers, and

267

although the pKa’s of the carboxylic acid protons are different (3.8 for lactate and 4.2 for 3-


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hydroxypropionate), both should have identical valence (-1) and molecular mass at pH 8.3, and

269

thereby have nearly the same electrophoretic mobility. The signal generated using C4D is

270

proportional to the concentration of an analyte and its mobility relative to the mobility of both

271

buffer species,21 and as such, lactate and 3-hydroxypropionate should produce nearly identical

272

detector responses. The fact that the two compounds are resolvable, indicates that their mobilities

273

in this system are not identical, as such, we consider the results reported for 3-hydroxypropionic

274

acid to be approximations.

275 276

Analysis of APL samples

277

We demonstrated the utility of GEMBE by analyzing a series of corn stover-derived APL

278

samples representing a range of NaOH loadings at two processing temperatures. These samples

279

were process replicates of the samples analyzed with an orthogonal GCxGC-MS method

280

described elsewhere.4 Each sample was analyzed in triplicate. The 12 samples were randomized

281

across eight experimental days. Five of the samples were repeated within a single day. This

282

allows us to compare the interday and intraday repeatability of the method in aggregate, and for

283

each analyte. Considering only analytes that were determined to be present above the LOQ, the

284

aggregate relative standard deviation for these samples was 8.5%, and the relative standard

285

deviation for the samples repeated within a day was 4.6%.

286 287

Lignin degradation products: We were able to quantify four monoaromatic compounds present

288

in APL using GEMBE: p-coumaric acid, ferulic acid, vanillic acid, and 4-hydroxybenzoic acid

289

(Figure 3). For all these compounds except 4-hydroxybenzoic acid, the GEMBE and GCxGC-

290

MS results compare favorably with respect to the total amount present and the overall trend


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associated with increasing NaOH loading. The results from the GC method detect 4-

292

hydroxybenzoic acid as a trace component, while GEMBE detects this compound at a

293

characteristic level of approximately 1 mg/mL, which is comparable (on a mass basis) to the

294

amount of vanillic acid detected by both methods. The GEMBE method presented here is also

295

capable of quantifying acetovanillone and protocatechuic acid, as demonstrated by the

296

construction of calibration curves. Both compounds were not present at concentrations high

297

enough to be quantifiable with GEMBE in the APL samples. Protocatechuic acid is anticipated

298

to be at trace levels by our GC method and is an important compound to track in bioconversion

299

samples. Acetovanillone is routinely detected by the GCxGC-TOF/MS method, but is between

300

the LOD and LOQ of the GEMBE method due to its partial charge at pH 8.3.

301 302

Sugar degradation products: The GEMBE method is capable of quantifying five of the six

303

sugar degradation products that were reported using GC analysis4: lactic acid, glyceric acid,

304

glycolic acid, 3-hydroxypropanoic acid, and 2-hydroxybutanoic acid, but unable to detect

305

glycerol. The impact of process temperature and NaOH loading on the concentration of these

306

analytes was determined (Figure 4). Failure to detect glycerol, although it is present at mmol L-1

307

levels in APL4 is due to the lack of charge on the analyte under the conditions utilized here. The

308

pH required for glycerol to have a comparable charge-to-mass ratio as the slowest eluting analyte

309

detected with the current method (acetovanillone) is approximately 13.3. The GEMBE method

310

was able to quantify five additional compounds that were unable to be quantified using the

311

GCxGC-TOF/MS method: acetic acid, formic acid, oxalic acid, succinic acid, and malic acid.

312

The impact of process temperature and NaOH loading on the concentration of these analytes was

313

determined (Figure 5). Acetic acid could not be quantified with GCxGC-TOF/MS due to its


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volatility during the dry-down phase, and formic acid was lost in the solvent. The remaining

315

three compounds -- succinic acid, malic acid, and oxalic acid – were all detected at trace (defined

316

as less than 0.1 mg/mL) levels by the GCxGC-TOF/MS method, but were quantified by GEMBE

317

at higher concentrations, within an order of magnitude of 0.1 mg/mL.

318 319

Method Comparison: The trends in concentration of sugar-derived compounds are comparable

320

to the trends demonstrated using GCxGC-TOF/MS analysis. Both methods show that higher

321

pretreatment temperatures increase the amounts of glycolic acid, 2-hydroxybutanoic acid, and

322

lactic acid present in the APL. Past work has shown that increasing pretreatment temperatures

323

solubilize more sugars2 thereby increasing the amount of sugar degradation products present in

324

APL. GEMBE analysis of APL samples shows that 3-hydroxypropionic acid (Figure 4), succinic

325

acid, and formic acid (Figure 5) also demonstrate this behavior. Succinic acid and formic acid

326

were not tracked with GCxGC-TOF/MS. The trend observed for 3-hydroxypropionic acid in the

327

GCxGC-TOF/MS method, however, is qualitatively different: the concentration profile plateaus

328

with increasing NaOH loading and is unaffected by increases in pretreatment temperature. It is

329

possible that the discrepancy in the two methods is a result due to a difference LOQ for this

330

relatively low abundance species. Concentrations profiles demonstrating this behavior are

331

suggestive of analytes that do not from originate sugar degradation reactions, but rather are likely

332

from another biomass component that is fully solubilized and degraded. Analytes demonstrating

333

this behavior with GEMBE characterization are acetic acid, glyceric acid, oxalic acid, and malic

334

acid.

335


15


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Page 16 of 37

336

For lignin degradation products, the trend associated with increasing temperature noted using the

337

GCxGC-TOF/MS method is not recapitulated in this report. In our prior study, it was observed

338

that for lower NaOH loading, more p-coumaric acid and ferulic acid were extracted at a process

339

temperature of 130°C. The GEMBE method shows comparable levels of these analytes for both

340

process temperatures across the entire range of treatment severity. The most likely source of this

341

difference is batch-to-batch variability, as the GCxGC-TOF/MS and GEMBE methods were

342

characterized using samples from different process batches. We cannot rule out the possibility

343

that source of the discrepancy was the co-elution of p-coumaric acid with 2-hydroxyisocaproic

344

acid and ferulic acid with catechol and syringic acid. If the extraction of 2-hydroxyisocaproic

345

acid, catechol, or syringic acid increases with temperature, the confounded measurement of the

346

two species by GEMBE could explain the elimination of the difference in extraction at the two

347

treatment temperatures observed by the GCxGC-TOF/MS analysis.

348 349

There were 8 analytes quantified using both methods. All of the analytes quantified show a high

350

degree of correlation between the measurements except 3-hydroxypropionic acid (Figure 6A-C).

351

The presence and nature of bias between the methods is shown with Bland-Altman35 plots

352

(Figure 6D-F). For four of the analytes (ferulic acid, vanillic acid, p-coumaric acid, and 2-

353

hydroxybutanoic acid), including all the lignin derived aromatic compounds, there appears to be

354

some constant bias, with GEMBE giving values on the order of 1 mg/mL higher than the

355

GCxGC-MS method (Figure 6D). The magnitude of this bias is larger for the samples processed

356

at 160°C. For the compounds that are correlated but not comparable, the nature of the bias

357

appears to be proportional to the signal (Figure 6E). The proportionality between the methods

358

indicates that relative changes between samples determined using one method would be


16

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359

recapitulated with the other. The samples analyzed were not duplicate samples, but rather

360

process replicates, and the analyses were conducted following different storage protocols prior to

361

analysis. Examination of the role these differences play in the observed bias between the

362

methods is the subject of future work.

363 364

Conclusions

365

In this report, we have demonstrated GEMBE can be used to quantify 17 compounds in APL

366

with a characteristic LOD on the order of 2 µM, and LOQ on the order of 6 µM. We compare the

367

results from our GEMBE characterization of a set of corn stover APL samples to results

368

generated by an orthogonal GCxGC-MS method.4 The analytical performance of the two

369

methods in terms of accuracy and LOD is comparable, but GEMBE offers distinct advantages in

370

terms of analysis time, sample preparation requirements, and cost of equipment. Opportunities to

371

further improve the resolution of the GEMBE method without substantially increasing the

372

analysis time include addition of selective agents and adjustment of BGE pH. Data analysis for

373

GEMBE requires custom written pipelines, which represents a significant effort that is not

374

required for the GCxGC-TOF/MS method. The sensitivity of the non-linear least squares curve

375

fitting to the initial guess for peak position, and peak asymmetry that results from sample

376

stacking play a role in the uncertainty of the measurement. Improvement of these pipelines

377

represents a significant avenue of improvement for future work. GEMBE is further limited in its

378

ability to detect neutral analytes e.g. glycerol. Our ability to detect catechol using our current

379

method suggests that BGE chemistries can be employed that generate complexes of neutral

380

analytes with a charged tag allowing their detection.

381


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Page 18 of 37

382

Taken together, the GCxGC-TOF/MS method and GEMBE are powerful companion methods

383

allowing unambiguous identification of target compounds and rapid quantification, respectively.

384

Preliminary characterization of other samples types indicates that GEMBE should be extendable

385

to the other sample types that the GC-based method has been applied to, such as pyrolysis oil,

386

and for tracking bioconversion. Given past success with GEMBE for real-time tracking of

387

chemical reactions, and the fact that dilution is the only preparative step required prior to

388

analysis, it is possible to envision GEMBE-instrumented bioreactors for automated real-time

389

monitoring with a time resolution on the order of the separation time (i.e., 20 minutes).

390 391

Materials and Methods

392

Production of APL: Alkaline pretreatment of corn stover and switchgrass were performed as

393

previously reported.2-3 Briefly, dried stover was loaded into sealed stainless steel vessels at mass

394

fraction of 10% in water. Sodium hydroxide was added at a concentration between 35 mg/g dry

395

stover and 660 mg/g dry stover, matching previous conditions.2 Vessel temperature was elevated

396

to 130°C or 160°C for 30 minutes at temperature. APL samples were aliquoted and stored at 4°C

397

prior to analysis. Repeated analysis of the same sample showed no changes in composition on a

398

time scale of months.

399 400

Preparation of Materials: Standard solutions of potential analytes were prepared at a nominal

401

concentration of 270 mmol/L in either pyridine or reagent grade deionized water (Sigma-

402

Aldrich) as indicated in Table S1. Standard solutions were stored in glass amber vials with PTFE

403

back silicon septa at room temperature. Background electrolyte (BGE) consisted of 250 mmol/L

404

tris(hydroxymethyl)aminomethane (Sigma-Aldrich) and 250 mmol/L boric acid (Sigma-Aldrich)


18

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405

and filtered with a 0.2 µm disposable vacuum filtration unit (Thermo). Sodium bromide (Sigma-

406

Aldrich) stock solution was prepared at a concentration of 0.5 mol/L in deionized water. Sample

407

dilution buffer was prepared by diluting the sodium bromide stock solution in BGE to a

408

concentration of 30 µmol/L. APL samples were diluted 100x in sample dilution buffer, these

409

samples were subsequently diluted 10x in sample dilution buffer for a net dilution of 1,000x.

410

Both dilution levels were analyzed for each sample. All analysis was completed with a single

411

batch of BGE. Since signal generation using capacitively coupled contactless conductivity

412

detection is a result of displacement of BGE ions, small batch-to-batch variations in buffer

413

composition can impact the slope and dynamic range of the calibration curve for each analyte.

414 415

Measurement of sample conductivity: The conductivities of diluted samples, BGE, and sample

416

dilution buffer were measured at room temperature using a conductivity meter (LAQUAtwin,

417

Horiba) following the manufacturer’s instructions. Briefly, the sensor was prepared by

418

application of a wetting solution for ten minutes followed by rinsing with deionized water, and

419

calibration using a manufacturer provided standard solution with a nominal conductivity of 1.4

420

mS/cm. The sensor was rinsed with deionized water until the meter read 1 µS/cm or less. The

421

sensor was then rinsed with an aliquot of sample, prior to measurement.

422 423

GEMBE Apparatus: The apparatus for performing GEMBE separations has been described

424

previously22,

425

(McMaster-Carr) to accommodate approximately 200 µL. The BGE reservoir was machined

426

from polyetherimide (LabSmith) to contain 2 mL. The separation channel consisted of a 5 cm

427

long fused silica capillary (Polymicro Technologies) with nominal outer and inner diameters of

30

(Figure 1). The sample reservoir was machined from acetal copolymer


19


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Page 20 of 37

428

360 µm and 10 µm, respectively. The separation channel was inserted approximately 1 mm into

429

the lumen of the sample reservoir through a 360 µm drilled hole, passed through a C4D detector

430

(TraceDec, Innovative Sensor Technologies) and approximately 4 mm into the BGE reservoir

431

through a miniature compression fitting (LabSmith). High voltage (PS350, Stanford Research

432

Systems) was applied using platinum wires inserted into the reservoirs. The polarity of the

433

voltage applied to the buffer reservoir was positive relative to the grounded sample reservoir, so

434

that the direction of electroosmotic flow would be from the BGE reservoir towards the sample.

435

A precision pressure controller (Series 600, Mensor), with air as the supply gas, was used to

436

control the headspace pressure inside the sealed run buffer reservoir. Instrument control and data

437

acquisition was automated using LabView software (National Instruments). The same capillary

438

was used in all experiments without repositioning. Changing or repositioning the capillary can

439

slightly alter the calibration due to shifts in the detection volume relative to the sample reservoir.

440 441

GEMBE Separation Procedures. Samples were removed and/or added to the sample reservoir

442

using a pipette. A 200 µL sample volume was used in all experiments. To prevent carryover

443

between samples, prior to sample loading, the sample reservoir was rinsed three times with 200

444

µL of BGE and once with an aliquot of interest. During sample loading the applied pressure was

445

set to 32 kPa to ensure a flow of run buffer through the capillary and prevent air bubbles. After

446

sample loading, high voltage was applied (2500 V) and the pressure was maintained at 32 kPa

447

for approximately 10 s. The applied pressure was then ramped downward at a rate of 63 Pa/s for

448

900 s. To rapidly rinse the separated sample from the capillary, the pressure was then increased

449

again to 68 kPa, and held for approximately 20 s, after which the high voltage was turned off.

450

The pressure was then set to 32 kPa for 30 seconds prior to evacuating the sample from the


20

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451

reservoir. The detector settings were frequency, 2x high; voltage, 0 dB; gain, 200%; offset, 0;

452

filter, slow; and data acquisition rate, 19.8 Hz.

453 454

Calibration: Mixtures were prepared from standard stocks as follows. Six equimolar sub-pools

455

were composed by mixing equal volumes of three standard solutions. Each sub pool consisted of

456

one aqueous standard and two with pyridine as solvent. The assignment into sub-pools was based

457

on elution order in order to maximize the elution time difference between members of a sub-

458

pool; e.g., sub-pool 1 consisted the first (sulfuric acid), seventh (acetic acid), and thirteenth

459

(sulfuric acid) species. Sub-pools were then mixed at fixed ratios using a latin square design (ESI

460

Table SIII), resulting in six calibration pools each with 3 components nominal concentrations of

461

at 30 mmol/L, 24 mmol/L, 18 mmol/L, 12 mmol/L, 6 mmol/L, and 0 mmol/L. The pools were

462

structured such that adjacent analytes were at disparate or comparable levels to enable

463

assessment of crosstalk between analytes. Calibration pools were diluted 100x in sample dilution

464

buffer, these samples were subsequently diluted 10x in sample dilution buffer for a net dilution

465

of 1,000x prior to analysis, resulting in a calibration range for each analyte of 6 µM to 300 µM at

466

10 levels and duplicate null measurements. Calibration curves were collected in full on three

467

different days, along with appropriate blank samples to normalize for stacking. The order of

468

analysis of the calibrants was randomized within a day.

469 470

Data Processing: Data processing pipelines were custom coded in R. Raw electropherograms

471

were converted to peaks for data visualization using a 161-point Savitsky-Golay derivative.

472

Individual steps were modeled as the sum of an error function and a quadratic baseline. Steps

473

were fit individually when there was adequate resolution. Bromide, oxalic acid, formic acid,


21


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Page 22 of 37

474

protocatechuic acid, and acetovanillone were fit as single species. Other analytes were combined

475

into a single zone and fit simultaneously as the sum of the appropriate number of error functions

476

as follows: succinic acid/system peak/malic acid, acetic acid/glycolic acid, 3-hydroxypropanoic

477

acid/lactic acid/glyceric acid, 2-hydroxybutanoic acid/4-hydroxybenzoic acid, vanillic acid/p-

478

coumaric acid, and ferulic acid/impurity/sinapic acid. Steps corresponding to chloride, sulfate,

479

and muconate/carbonate/phosphate were not fit as they were of limited interest. Step heights for

480

each analyte zone were determined for either the 100x or 1,000x dilution sample. Oxalic acid,

481

formic acid, and acetic acid/glycolic acid zones were fit using the 1,000x dilutions. Remaining

482

analytes were determined from the 100x dilution samples. Due to the sensitivity of the parameter

483

algorithm to the initial values, the data processing pipeline supplied graphical summaries of all

484

curve fits. Initial positions and data windows were adjusted as necessary for individual

485

electropherograms until stable representative fits were obtained for all species of interest.

486 487

The height of the bromide step was fit for every sample to compensate for differences in stacking

488

arising from differences in sample conductivity. For each sample the “stacking ratio” was

489

defined as the ratio of the height of the bromide step in the sample to the height of the bromide

490

step in a control sample consisting only of sample dilution buffer (corrected for the small

491

differences in bromide concentration arising from dilution of the sample). The stacking ratio was

492

used to scale the heights determined for each step in the electropherogram. Calibration curves

493

(described above) were used to determine the concentration of each analyte in the diluted

494

sample, which was then scaled by the dilution factor to recover the concentration in the raw

495

sample.

496


22

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497

Supporting Information.

498

Measurement error and chemical identification information is included as Supporting

499

Information. This material is available free of charge via the Internet at http://pubs.acs.org.

500

Corresponding Author

501

Gregg T. Beckham

502

*Email [email protected]

503

Funding Sources

504

EMK, CTN, and GTB thank the U.S. Department of Energy Bioenergy Technologies Office

505

(DOE-BETO) for funding via Contract No. DE-AC36-08GO28308 with the National Renewable

506

Energy Laboratory.

507

Acknowledgements

508

MSM and MS would like to acknowledge Drew Endy and Christina Smolke for graciously

509

hosting this work in their laboratories, Stephanie Galanie for logistical support, Sarah Munro,

510

Ariel Hecht, and Jerod Parsons for useful support in the construction of data analysis pipelines,

511

and Scott Pine for assistance in designing calibration pools. The U.S. Government retains and the

512

publisher, by accepting the article for publication, acknowledges that the U.S. Government

513

retains a nonexclusive, paid up, irrevocable, worldwide license to publish or reproduce the

514

published form of this work, or allow others to do so, for U.S. Government purposes. Certain

515

commercial equipment, instruments, or materials are identified in this report to specify

516

adequately the experimental procedure. Such identification does not imply recommendation or

517

endorsement by the National Institute of Standards and Technology, nor does it imply that the

518

materials or equipment identified are necessarily the best available for the purpose.


23


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Page 24 of 37

519

References

520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563

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17. Shackman, J. G.; Munson, M. S.; Ross, D., Gradient Elution Moving Boundary Electrophoresis for High-Throughput Multiplexed Microfluidic Devices. Anal. Chem. 2007, 79 (2), 565-571. 18. da Silva, J. A. F.; do Lago, C. L., An Oscillometric Detector for Capillary Electrophoresis. Anal. Chem. 1998, 70 (20), 4339-4343. 19. Guijt, R. M.; Evenhuis, C. J.; Macka, M.; Haddad, P. R., Conductivity Detection for Conventional and Miniaturised Capillary Electrophoresis Systems. Electrophoresis 2004, 25 (2324), 4032-4057. 20. Zemann, A. J.; Schnell, E.; Volgger, D.; Bonn, G. K., Contactless Conductivity Detection for Capillary Electrophoresis. Anal. Chem. 1998, 70 (3), 563-567. 21. Brito-Neto, J. G. A.; da Silva, J. A. F.; Blanes, L.; do Lago, C. L., Understanding Capacitively Coupled Contactless Conductivity Detection in Capillary and Microchip Electrophoresis. Part 1. Fundamentals. Electroanalysis 2005, 17 (13), 1198-1206. 22. Flanigan, P. M.; Ross, D.; Shackman, J. G., Determination of Inorganic Ions in Mineral Water by Gradient Elution Moving Boundary Electrophoresis. Electrophoresis 2010, 31 (20), 3466-3474. 23. Kralj, J. G.; Munson, M. S.; Ross, D., Total Protein Quantitation Using the Bicinchoninic Acid Assay and Gradient Elution Moving Boundary Electrophoresis. Electrophoresis 2014, 35 (12-13), 1887-1892. 24. Strychalski, E. A.; Henry, A. C.; Ross, D., Expanding the Capabilities of Microfluidic Gradient Elution Moving Boundary Electrophoresis for Complex Samples. Anal. Chem. 2011, 83 (16), 6316-6322. 25. Ross, D.; Kralj, J. G., Simple Device for Multiplexed Electrophoretic Separations Using Gradient Elution Moving Boundary Electrophoresis with Channel Current Detection. Anal. Chem. 2008, 80 (24), 9467-9474. 26. Ross, D.; Romantseva, E. F., Gradient Elution Moving Boundary Electrophoresis with Channel Current Detection. Anal. Chem. 2009, 81 (17), 7326-7335. 27. Sikorsky, A. A.; Fourkas, J. T.; Ross, D., Gradient Elution Moving Boundary Electrophoresis with Field-Amplified Continuous Sample Injection. Analytical Chemistry 2014, 86 (7), 3625-3632. 28. Ross, D.; Munson, M. S., Gradient Elution Moving Boundary Electrophoresis with Channel Current Detection in Relatively Long Capillaries. Electrophoresis 2014, 35 (5), 770776. 29. Ross, D.; Shackman, J. G.; Kralj, J. G.; Atencia, J., 2d Separations on a 1d Chip: Gradient Elution Moving Boundary Electrophoresis-Chiral Capillary Zone Electrophoresis. Lab Chip 2010, 10 (22), 3139-3148. 30. Strychalski, E. A.; Henry, A. C.; Ross, D., Microfluidic Analysis of Complex Samples with Minimal Sample Preparation Using Gradient Elution Moving Boundary Electrophoresis. Anal. Chem. 2009, 81 (24), 10201-10207. 31. Munson, M. S.; Danger, G.; Shackman, J. G.; Ross, D., Temperature Gradient Focusing with Field-Amplified Continuous Sample Injection for Dual-Stage Analyte Enrichment and Separation. Anal. Chem. 2007, 79 (16), 6201-6207. 32. Fuchs, G.; Boll, M.; Heider, J., Microbial Degradation of Aromatic Compounds - from One Strategy to Four. Nat. Rev. Microbiol. 2011, 9 (11), 803-816. 33. Boeseken, J., The Use of Boric Acid for the Determination of the Configuration of Carbohydrates. Adv. Carb. Chem. 1949, 4, 189-210.


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34. Zittle, C. A., Reaction of Borate with Substances of Biological Interest. Advances in Enzymology and Related Subjects of Biochemistry 1951, 12, 493-527. 35. Altman, D. G.; Bland, J. M., Measurement in Medicine - the Analysis of Method Comparison Studies. Statistician 1983, 32 (3), 307-317.


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615

Figure 1: Schematic representation of GEMBE. A separation capillary bridges reservoirs containing sample and background electrolyte (BGE). The BGE reservoir is pressure controlled, in order to tune bulk flow, which is directed from the BGE reservoir towards the sample. As the applied pressure is reduced, electrophoresis of sample ions is able to overcome convection at the capillary inlet, and ions elute into the capillary in order of decreasing mobility. 616 617


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Page 28 of 37

Figure 2: Representative electropherogram from separation of corn stover APL using GEMBE (A). The heights of the steps in the raw detector signal are proportional to the concentration of species eluting at that step. To facilitate visual inspection and identification of step positions, the derivative of the detector signal is calculated (B,C). Labeled steps have been identified by spiking standards of compounds identified by orthogonal analyses of similar samples, and are detailed in Table 1.4 Unknown peaks are labeled U and system peaks are labeled S. 618


28

Page 29 of 37

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Table 1: Correspondence between analyte and step number

Step Number

Compound

1

oxalic acid

2

formic acid

3

succinic acid

4

malic acid

5

muconic acid

6

acetic acid

7

glycolic acid

8

protocatechuic acid

9

propionic acid

10

3-hydroxypropionic acid

11

lactic acid

12

glyceric acid 2-hydroxybutanoic acid

13 salicylic acid 14

4-hydroxybenzoic acid vanillic acid

15 gluconic acid vanillic acid 16 2-hydroxyisocaproic acid ferulic acid syringic acid 17 catechol vanillin 18

sinapic acid

19

acetovanillone

619 620 621


29


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Table 2: Limit of detection and limit of quantification for analytes LOD

LOQ

Analyte

μmol/L

mg/mL

μmol/L

mg/mL

oxalic acid

1.05

0.09

3.51

0.31

formic acid

2.49

0.11

8.29

0.37

succinic acid

1.26

0.15

4.19

0.49

malic acid

2.12

0.28

7.06

0.93

acetic acid

1.61

0.09

5.36

0.32

glycolic acid

2.27

0.17

7.56

0.57

protocatechuic acid

1.26

0.19

4.20

0.65

3-hydroxypropionic acid

2.00

0.18

6.66

0.60

lactic acid

2.00

0.18

6.66

0.60

glyceric acid

1.95

0.21

6.49

0.69

2-hydroxybutanoic acid

2.27

0.20

7.55

0.68

4-hydroxybenzoic acid

1.12

0.15

3.73

0.51

vanillic acid

3.57

0.65

6.11

1.11

p-coumaric acid

1.87

0.30

6.22

1.02

ferulic acid

0.86

0.17

2.88

0.56

sinapic acid

2.45

0.55

8.16

1.82

acetovanillone

2.34

0.39

7.81

1.30

622 623


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Figure 3: The four most abundant lignin degradation products p-coumaric acid (A), ferulic acid (B), and vanillic acid (C), and 4-hydoxybenzoic acid (D) are quantified as a function of


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pretreatment severity. Corn stover pretreatments were performed for 30 minutes at temperatures of 130°C and 160°C over NaOH loadings ranging from 35 to 660 mg/g dry stover. A replicate of this figure with mmol/L units is shown in Figure S5. Error bars are the standard deviation of three replicate measurements conducted on different days.

Figure 4: Hydroxyacid products resulting primarily from sugar degradation reactions and extractives in the alkaline media during pretreatment are quantified and tracked as a function of pretreatment severity that were also tracked using the GCxGC-TOF/MS-based method.4 General trends are seen in that increasing pretreatment temperature creates more glycolic acid (A), 2hydroxybutanioc acid (B), 3-hydroxypropionic acid (C), and lactic acid (D) in the APL sample. Glyceric acid (E) production was not effected by temperature, but does increase with hydroxide loading. A replicate of this Figure with mmol/L units is shown in Figure S6. Error bars are the


32

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standard deviation of three replicate measurements conducted on different days. 624

Figure 5: Hydroxyacid products resulting primarily from sugar and extractives degradation reactions in the alkaline media during pretreatment, are quantified and tracked as a function of pretreatment severity. These compounds could not be tracked using the GCxGC-TOF/MS-based method.4 General trends are seen in that increasing pretreatment temperature creates more succinic acid (A) and formic acid (B) in the APL sample. Oxalic acid (C) production was not effected by temperature, but does increase with hydroxide loading. Acetic acid (D) and malic acid (E) production were not observed to be a strong function of either temperature or hydroxide loading. A replicate of this Figure with mmol/L units is shown in Figure S7. Error bars are the


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Page 34 of 37

standard deviation of three replicate measurements conducted on different days. 625 626


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Figure 6: Comparison of measured concentrations for analytes quantified using both the GCxGC-TOF/MS and GEMBE methods. Results from the two methods are correlated for glyceric acid, glycolic acid, and lactic acid (A) comparable for p-coumaric acid, ferulic acid, vanillic acid, and 2-hydroxybutanoic acid (B) but not well correlated for 3-hydroxypropionic acid (C), and. The nature of the bias between the two methods is demonstrated using BlandAltman plots. Ferulic acid, p-coumaric acid, vanillic acid, and 2-hydroxybutanoic acid show constant bias that is temperature dependent (D) while lactic acid, glycolic acid, and glyceric acid each demonstrate proportional bias (E). Differences are calculated by subtracting GEMBE values from GCxGC-TOF/MS values. Error bars are the standard deviation of three replicate


35


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measurements conducted on different days. 627


36

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628

For Table of Contents Use Only

629

Title: Gradient Elution Moving Boundary Electrophoresis enables rapid analysis of acids in

630

complex biomass-derived streams

631

Authors: Matthew S. Munson, Eric M. Karp, Claire T. Nimlos, Marc Salit, and Gregg T.

632

Beckham

633

Synopsis

634

GEMBE is able to rapidly and efficiently quantify multiple compounds in complex biomass

635

streams with little to no expensive equipment.

636 637

TOC Figure.

638 639 640 641


37

Relative Significance of the Negative Impacts of Hemicelluloses on

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