Gradient Elution Moving Boundary Electrophoresis Enables Rapid

Research Article pubs.acs.org/journal/ascecg

Gradient Elution Moving Boundary Electrophoresis Enables Rapid Analysis of Acids in Complex Biomass-Derived Streams Matthew S. Munson,†,‡,§ Eric M. Karp,⊥ Claire T. Nimlos,⊥ Marc Salit,†,‡,§ and Gregg T. Beckham*,⊥ †

Joint Initiative for Metrology in Biology, Stanford University, 443 Via Ortega, Shriram Center, Stanford, California 94305, United States ‡ Genome Scale Measurements Group, National Institute of Standards and Technology, 443 Via Ortega, Shriram Center, Stanford, California 94305, United States § Department of Bioengineering, Stanford University, 443 Via Ortega, Shriram Center, Stanford, California 94305, United States ⊥ National Bioenergy Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States S Supporting Information *

ABSTRACT: Biomass conversion processes such as pretreatment, liquefaction, and pyrolysis often produce complex mixtures of intermediates that are a substantial challenge to analyze rapidly and reliably. To characterize these streams more comprehensively and efficiently, new techniques are needed to track species through biomass deconstruction and conversion processes. Here, we present the application of an emerging analytical method, gradient elution moving boundary electrophoresis (GEMBE), to quantify a suite of acids in a complex, biomass-derived streams from alkaline pretreatment of corn stover. GEMBE offers distinct advantages over common chromatography-spectrometry analytical approaches in terms of analysis time, sample preparation requirements, and cost of equipment. As demonstrated here, GEMBE is able to track 17 distinct compounds (oxalate, formate, succinate, malate, acetate, glycolate, protocatechuate, 3-hydroxypropanoate, lactate, glycerate, 2-hydroxybutanoate, 4-hydroxybenzoate, vanillate, pcoumarate, ferulate, sinapate, and acetovanillone). The lower limit of detection was compound dependent and ranged between 0.9 and 3.5 μmol/L. Results from GEMBE were similar to recent results from an orthogonal method based on GC×GC-TOF/ MS. Overall, GEMBE offers a rapid, robust approach to analyze complex biomass-derived samples, and given the ease and convenience of deployment, may offer an analytical solution for online tracking of multiple types of biomass streams. KEYWORDS: Alkaline pretreatment, Analytical chemistry, Biomass conversion, Biorefinery, Lignin, GEMBE

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INTRODUCTION Biomass conversion processes often rely on a combination of heat, catalysts, and/or mechanical work to process lignocellulosic material. As plant cell walls are complex, composite materials comprising several biopolymers, among other components, many biomass conversion processes produce highly heterogeneous mixtures of compounds that are challenging to characterize. Similar challenges exist in conversion processes of other heterogeneous biomass mixtures such as in process streams of municipal solid waste and algae. The development of robust analytical approaches to monitor solubilized biomass streams is a critical component of the continued development of biomass valorization approaches, as measuring yields of intermediates and products is often both a challenge and a key need for process monitoring. Alkaline pretreatment liquor (APL) is an exemplary complex, lignin rich, solubilized mixture generated during high pH treatment of biomass. It is composed of high molecular weight lignin, low molecular weight aromatic compounds derived from lignin depolymerization, hydroxy acids from sugar degradation © XXXX American Chemical Society

reactions, alcohols, aldehydes, and other higher molecular weight partially depolymerized biopolymers.1 Because of the high degree of chemical heterogeneity, these types of solutions are generally metastable and the addition of solvents, acids, or other agents during workup facilitates subsequent reactions, making it difficult to derive quantitative information from the original sample. In the case of APL, the solution generally has a pH > 102,3 and decreasing the solution pH < 5 causes much of the solubilized material to precipitate. This characteristic rules out many liquid chromatography techniques that do not operate at high pH or that use nonaqueous mobile phases. Additionally, the analytical technique of choice must be able to separate a wide range of chemicals with varying functionalities. Thus, there are few nondestructive methods available that apply to lignin rich solutions, such as APL, that are produced in a biorefinery.1,4 However, assessing the merits of biological Received: August 29, 2016 Revised: September 23, 2016

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DOI: 10.1021/acssuschemeng.6b02076 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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ACS Sustainable Chemistry & Engineering conversion5−8 or any of the various lignin conversion processes that utilize these complex lignin rich materials is crucial for the maturation of the technologies, and will require development of robust analytical techniques for tracking organic compounds in these complex substrates. Electrophoretic techniques, namely capillary electrophoresis (CE), operate at high pH and have demonstrated success in measuring inorganic and some organic components in kraft black liquors, but run times are often long and the slate of compounds that can be measured via the CE methods are somewhat limited.9−14 Gradient elution moving boundary electrophoresis (GEMBE) offers the potential for faster and more robust analysis of these types of complex samples.15−17 GEMBE (represented schematically in Figure 1) is injection-

the identified compounds. In this work, we demonstrate the utility of GEMBE for the quantification of the major lignin and sugar degradation products present in corn stover APL over a variety of pretreatment conditions. Our results are then quantitatively compared to samples representing identical process conditions analyzed by GC×GC-TOF/MS. GEMBE offers distinct advantages in terms of analysis time, sample preparation requirements, and cost of equipment over GC×GC-TOF/MS.

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RESULTS AND DISCUSSION Identification of Analytes of Interest. The use of a C4D as a detection mode in GEMBE requires a priori knowledge of what analytes are likely to be present in the samples of interest. This method development knowledge accelerates the identification of separation conditions, and enables the assignment of compounds to corresponding electropherogram steps. For APL, a list of target analytes was constructed from a combination of literature review and orthogonal methods that utilize MS for compound identification.4 The compounds of interest are summarized in Table S1. APL is rich in both monoaromatic compounds from the breakdown of lignin and organic acids from the degradation of carbohydrates.1,4 Identification of Separation Conditions. Optimization of separation conditions is strongly influenced by the physical properties of the analytes of interest. The primary mechanism for resolving species in GEMBE is electrophoretic mobility, which is determined by the ratio of the analytes electrical charge and hydrodynamic drag. The pH of the separation buffer is therefore a critical parameter in resolving the analytes. The effective charge for each species was estimated for pH values from 7 to 11. We used the estimated charge-to-mass ratio as a prediction of the relative mobility of each analyte as a function of pH, normalized to acetate, which was constant across the pH range examined (Figure S1). Although these calculations suggest a pH of 10 would be optimal, we implemented our method using a background electrolyte (BGE) that had previously been used for GEMBE17 having a pH of 8.3 (250 mM Tris, 250 mM boric acid). The consequence of this is that the several of the suspected analytes are not anticipated to be detectable. These analytes are noted in Table S1. Other key operating parameters were optimized empirically. Reducing capillary inner diameter can increase the peak capacity of the separation at the cost of limit of detection. A capillary ID of 10 μm was determined to give the best performance for these samples. After a capillary ID was selected, the maximum field that can be applied without generating significant Joule heating was determined to be 500 V/cm (or 2.5 kV across the 5 cm capillary). The applied pressure was reduced at a rate of −62.5 Pa/s starting at a value of +30 kPa and ending at −26 kPa. The starting pressure for the separation is selected by determining the entry pressure for the fastest analyte of interest. To account for experimental fluctuations, the starting pressure was set 1 kPa higher than the calculated entry pressure. The ending pressure was determined by finding the applied pressure that results in bulk reversal of flow. The separation time scales linearly, whereas the resolution increases with the square root of the pressure ramp rate. We established an arbitrary maximum separation time of 1000 s, including necessary rinse times to calculate the slowest acceptable ramp rate. A characteristic electropherogram is presented in Figure 2A. Each stepwise change in conductivity represents the elution front of an analyte (or unresolved group

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 toward 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.

less, and relies on a counterflow modulated over the course of the separation to achieve selectivity on the basis of electrophoretic mobility. Briefly, a separation channel comprised of fused silica capillary bridges a pressure controlled separation buffer reservoir and a sample reservoir. Voltage is applied between the reservoirs driving electroosmotic flow toward from the buffer reservoir toward the sample, and electromigration of charged species in the sample toward the buffer reservoir. A pressure driven flow is superimposed on the electro-osmotic flow to control the overall flow rate. At the beginning of the separation, the convective velocity at the channel entrance exceeds the electrophoretic velocity for all analytes of interest. The applied pressure is then gradually reduced, resulting in sequential elution of analytes into the capillary in the order of electrophoretic mobility. The eluted boundaries are detectable as steps using a variety of detectors: in the case of this work, capacitively coupled contactless conductivity detection (C4D).18−21 GEMBE can be operated using a wide range of conditions (pH, sample conductivity, etc.)22−24 with relatively inexpensive instrumentation25−27 and can utilize a diversity of detection modes.17,25,27,28 It has also been demonstrated that GEMBE can operate in multidimensional modes to achieve demanding separations,29 can handle samples containing particulates,24,30 and can be used for online monitoring of reactions within a sample.25 The use of a “universal detector” is enabled by orthogonal analysis methods that allow for peak identification, such as the GC×GC-MS method described in our concurrent report.4 Once a select set of compounds are identified using a method such as the GC×GC-TOF/MS method described by Karp et al.,4 GEMBE can be applied to analyze samples more rapidly for B


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ACS Sustainable Chemistry & Engineering

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.

temperature also impacts the conductivity of the APL, however, as higher temperatures result in the production of increasing levels of charged breakdown products. APL samples diluted in BGE that have a different conductivity than the BGE will result in field amplified sample stacking at the capillary entrance,27,31 which results in an increase in step height when the sample is less conductive than the BGE and a reduction in step height when the sample is more conductive than the BGE. This effect limits the ability to compare samples with different conductivities and construct calibrations that apply across all samples. One strategy to mask the variation in conductivity between samples is to increase the conductivity of the BGE so that the conductivity of the diluted APL sample is dominated by the BGE conductivity. Raising the conductivity of the BGE has deleterious effects on the separation, as it raises the limit of detection (LOD) and reduces resolution by decreasing the maximum applied voltage. Given the high conductivity of the APL samples (between 7 and 120 mS cm−1) examined in this report (Table S2), this approach is not feasible. Alternatively, we employed an internal standard to track the extent of sample stacking observed in each separation. Sodium bromide was added to each sample at a concentration of 30 μM. The bromide ion was chosen as the internal standard because it is absent in the APL samples and is both resolved from and higher in electrophoretic mobility than all the other species present including chloride. All calculated step heights are then normalized by the ratio of the bromide step in the sample and a

of analytes). Interpretation of the electropherograms is aided by computing the time derivative of the detector signal (Figure 2B) resulting in a conventional electropherogram. This report contains a detailed discussion of the use of GEMBE for the analysis of corn stover APL and comparison of the method to our GC×GC-MS method.4 The separation conditions identified are also amenable to analysis of APL derived from other feedstocks (e.g., switchgrass, pine, etc.), and for tracking bioconversion of these APL substrates. Representative electropherograms for samples derived from materials other than corn stover are shown in the SI (Figure S2). Sample Properties: Dynamic Range, Conductivity, and Particulates. The components of APL span a large dynamic range in concentration. This makes it challenging to determine the optimal dilution factor for the raw samples in BGE that will allow low abundance species to be detected without the high abundance species saturating the detector, or electrostatically overloading the separation zones. This challenge was addressed by analyzing each sample at two dilution levels. Samples were diluted 100× and 1000× in BGE. High concentration analytes were quantified from 1000× dilution, whereas low concentration analytes were determined from the 100× dilution. The properties of APL samples vary as a function of pretreatment conditions. The conductivity of the sample is of particular importance for GEMBE. The variation in conductivity of APL arises primarily as a function of NaOH loading, which is trivially anticipated. The pretreatment C


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components of coeluting steps. Several steps in the electropherogram remain unidentified. The presence of unidentified steps underscores the value of companion methods4 that allow for determination of chemical identity to guide peak identification. Standards of 2-hydroxybutanoic acid and salicylic acid coelute (step 13), vanillic acid and gluconic acid coelute (step 15), and p-coumaric acid and 2-hydroxyisocaproic acid coelute (step 16). Our orthogonal characterization of comparable APL samples did not detect salicylic acid, and detected gluconic acid and 2-hydroxyisocaproic acid at trace levels.4 We therefore consider step 13 to be representative of the 2-hydroxybutanoic acid concentration, step 15 to be representative of the concentration of vanillic acid, and step 16 to be representative of the concentration of p-coumaric acid. Standards for ferulic acid, syringic acid, vanillin, and catechol coelute (step 17). Prior results show that vanillin is not present in similar samples, and syringic acid is present at trace levels.4 Catechol was not quantifiable using the GC×GC-MS method, but based on literature reports is likely to be present.1 The height of step 17 is therefore assumed to be proportional to the response-weighted sum of the concentrations of catechol and ferulic acid. It is not possible to determine the distribution of components in the step from a single measurement of height. Catechol is also a key metabolic intermediate in biological conversion of APL.32 Future adaptation of this technique to monitoring metabolite consumption and production during biological conversion will benefit from resolution of catechol from ferulic and syringic acid. We also observe several system steps with this BGE. The system effects have an adverse impact on quantification for several of the analytes of interest. The most significant system step arises from the presence of carbonate in the BGE and samples. Carbonate forms a large system step at an elution time of approximately 620 s. We anticipate that the size and variability of the carbonate step interferes with the quantification of muconate. Calibration of muconate was not attempted in this report, as it was not observed to be at detectable concentrations in the APL samples considered. Formic acid, succinic acid, and malic acid (steps 2, 3, and 4 respectively) coelute with system steps. The contribution of the system steps to the observed signal for each of these analytes was determined by analyzing a blank sample (containing only 30 μM NaBr in BGE) each day, and subtracting the value of step height in the blank from the step height determined in the sample. The elution order generally followed the predicted mobility (Figure S1). The notable deviations from predicted mobility are protocatechuic acid and catechol. Both compounds have higher electrophoretic mobility that was anticipated by their charge to mass ratios, particularly catechol, which should be neutrally charged at pH 8.3. As boric acid is known to form covalent anionic complexes with polyhydroxy compounds,33,34 the apparent acceleration of catechol and protocatechuic acid is likely a result of the formation such complexes. The steps corresponding to these two compounds are significantly broadened in comparison to the characteristic step width of other analytes, suggesting that under the conditions employed the relevant species are in a dynamic equilibrium. Given these observations, and the number of target analytes that contain multiple hydroxide groups, it is likely that the boric acid in the BGE is contributing to the resolution of the method.

sample containing only sodium bromide. The elution time of the bromide zone was used to align all electropherograms. APL samples also contain significant particulates of varying size. GEMBE can be adversely impacted by sample particulates, and strategies have been developed to mitigate these effects.24 With the conditions employed in this report, biomass particulates were not observed to migrate in the separation channel. We did, however, observe particulates in the BGE. We hypothesize that these were likely borate precipitates. For samples where the passage of a particle would affect determination of the step height associated with an analyte in a given sample, that step was excluded from the data set. The frequency of this occurrence was less than 0.1% of all sample/ analyte combinations characterized in this study. Assignment of Analytes to Corresponding Electropherogram Steps. Each analyte listed in Table S1 was prepared separately at 130 μM in BGE with 30 μM NaBr and analyzed, allowing us to determine the elution order and order of magnitude of the detector response. Several of the listed compounds could not be detected. The resultant electropherograms can be superimposed (Figure S3) to determine which analytes, if present in the sample would be resolved. The mapping of compound to step numbers as noted in Figure 2B is detailed in Table 1. Of the analytes listed, 15 were able to be Table 1. Correspondence between Analyte and Step Number step number

compound

1 2 3 4 5 6 7 8 9 10 11 12 13

oxalic acid formic acid succinic acid malic acid muconic acid acetic acid glycolic acid protocatechuic acid propionic acid 3-hydroxypropionic acid lactic acid glyceric acid 2-hydroxybutanoic acid salicylic acid 4-hydroxybenzoic acid vanillic acid gluconic acid vanillic acid 2-hydroxyisocaproic acid ferulic acid syringic acid catechol vanillin sinapic acid acetovanillone

14 15 16 17

18 19

adequately resolved from each other, 9 were not detectable, and 10 were detected as part of coeluting steps. Several other compounds (noted in Table S1) were analyzed in an effort to identify unassigned steps in the electropherogram resulting in the identification of steps corresponding to chloride, sulfate, carbonate, and phosphate ions. Detectable analytes were pooled at known concentration ratios and spiked into representative APL samples for final identification electropherogram steps. A single representative compound was chosen to represent all the D


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of the samples analyzed with GC×GC-MS.4 Each sample was analyzed in triplicate. The 12 samples were randomized across eight experimental days. Five of the samples were repeated within a single day. This allows us to compare the interday and intraday repeatability of the method in aggregate, and for each analyte. Considering only analytes that were determined to be present above the LOQ, the aggregate relative standard deviation for these samples was 8.5%, and the relative standard deviation for the samples repeated within a day was 4.6%. Lignin Degradation Products. We were able to quantify four monoaromatic compounds present in APL using GEMBE: p-coumaric acid, ferulic acid, vanillic acid, and 4-hydroxybenzoic acid (Figure 3). For all these compounds except 4hydroxybenzoic acid, the GEMBE and GC×GC-MS results compare favorably with respect to the total amount present and the overall trend associated with increasing NaOH loading. The results from the GC method detect 4-hydroxybenzoic acid as a trace component, while GEMBE detects this compound at a characteristic level of approximately 1 mg/mL, which is comparable (on a mass basis) to the amount of vanillic acid detected by both methods. The GEMBE method presented here is also capable of quantifying acetovanillone and protocatechuic acid, as demonstrated by the construction of calibration curves. Both compounds were not present at concentrations high enough to be quantifiable with GEMBE in the APL samples. Protocatechuic acid is anticipated to be at trace levels by our GC method and is an important compound to track in bioconversion samples. Acetovanillone is routinely detected by the GC×GC-TOF/MS method, but is between the LOD and LOQ of the GEMBE method due to its partial charge at pH 8.3. Sugar Degradation Products. The GEMBE method is capable of quantifying five of the six sugar degradation products that were reported using GC analysis:4 lactic acid, glyceric acid, glycolic acid, 3-hydroxypropanoic acid, and 2-hydroxybutanoic acid, but unable to detect glycerol. The impact of process temperature and NaOH loading on the concentration of these analytes was determined (Figure 4). Failure to detect glycerol, although it is present at mmol L−1 levels in APL,4 is due to the lack of charge on the analyte under the conditions utilized here. The pH required for glycerol to have a comparable charge-tomass ratio as the slowest eluting analyte detected with the current method (acetovanillone) is approximately 13.3. The GEMBE method was able to quantify five additional compounds that were unable to be quantified using the GC×GC-TOF/MS method: acetic acid, formic acid, oxalic acid, succinic acid, and malic acid. The impact of process temperature and NaOH loading on the concentration of these analytes was determined (Figure 5). Acetic acid could not be quantified with GC×GC-TOF/MS due to its volatility during the dry-down phase, and formic acid was lost in the solvent. The remaining three compoundssuccinic acid, malic acid, and oxalic acidwere all detected at trace (defined as less than 0.1 mg/mL) levels by the GC×GC-TOF/MS method, but were quantified by GEMBE at higher concentrations, within 1 order of magnitude of 0.1 mg/mL. Method Comparison. The trends in concentration of sugarderived compounds are comparable to the trends demonstrated using GC×GC-TOF/MS analysis. Both methods show that higher pretreatment temperatures increase the amounts of glycolic acid, 2-hydroxybutanoic acid, and lactic acid present in the APL. Past work has shown that increasing pretreatment temperatures solubilize more sugars,2 thereby increasing the

Calibration. System calibration was conducted using a mixed pool design. Six subpools consisting of equimolar mixtures of three compounds were prepared. These pools were mixed at known ratios to produce six calibration pools using a complete latin square design (Table S3). The calibration pools were treated as though they were samples, i.e., diluted 100× and 1000× prior to analysis with GEMBE. The concentration range for each analyte in the diluted samples is from 6 to 300 μM. Calibration samples were analyzed in triplicate. Sulfate and phosphate were included in the pool design in order to balance the number of analytes in each subpool. These analytes were not of sufficient interest in our applications to warrant analysis. Calibration curves for 16 of the calibrants are shown in Figure S4. The limit of detection (LOD) and limit of quantitation (LOQ) are defined in this paper as 3 and 10 times the ratio of estimated standard error of the intercept and the estimated slope of the regression line, respectively. The LOD and LOQ for each compound are reported in Table 2, in units of μmol L−1, and mg mL−1. Table 2. Limit of Detection and Limit of Quantification for Analytes LOD

LOQ

analyte

μmol/L

mg/mL

μmol/L

mg/mL

oxalic acid formic acid succinic acid malic acid acetic acid glycolic acid protocatechuic acid 3-hydroxypropionic acid lactic acid glyceric acid 2-hydroxybutanoic acid 4-hydroxybenzoic acid vanillic acid p-coumaric acid ferulic acid sinapic acid acetovanillone

1.05 2.49 1.26 2.12 1.61 2.27 1.26 2.00 2.00 1.95 2.27 1.12 3.57 1.87 0.86 2.45 2.34

0.09 0.11 0.15 0.28 0.09 0.17 0.19 0.18 0.18 0.21 0.20 0.15 0.65 0.30 0.17 0.55 0.39

3.51 8.29 4.19 7.06 5.36 7.56 4.20 6.66 6.66 6.49 7.55 3.73 6.11 6.22 2.88 8.16 7.81

0.31 0.37 0.49 0.93 0.32 0.57 0.65 0.60 0.60 0.69 0.68 0.51 1.11 1.02 0.56 1.82 1.30

We were not able to directly calibrate 3-hydroxypropionic acid because standards of sufficient purity are not available. We applied the calibration curve for lactic acid to approximate quantification of 3-hydroxypropionic acid. The two compounds are structural isomers, and although the pKa values of the carboxylic acid protons are different (3.8 for lactate and 4.2 for 3-hydroxypropionate), both should have identical valence (−1) and molecular mass at pH 8.3, and thereby have nearly the same electrophoretic mobility. The signal generated using C4D is proportional to the concentration of an analyte and its mobility relative to the mobility of both buffer species;21 as such, lactate and 3-hydroxypropionate should produce nearly identical detector responses. The fact that the two compounds are resolvable indicates that their mobilities in this system are not identical; as such, we consider the results reported for 3hydroxypropionic acid to be approximations. Analysis of APL samples. We demonstrated the utility of GEMBE by analyzing a series of corn stover-derived APL samples representing a range of NaOH loadings at two processing temperatures. These samples were process replicates E


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however, is qualitatively different: the concentration profile plateaus with increasing NaOH loading and is unaffected by increases in pretreatment temperature. It is possible that the discrepancy in the two methods is a result due to a difference LOQ for this relatively low abundance species. Concentrations profiles demonstrating this behavior are suggestive of analytes that do not originate from sugar degradation reactions, but rather are likely from another biomass component that is fully solubilized and degraded. Analytes demonstrating this behavior with GEMBE characterization are acetic acid, glyceric acid, oxalic acid, and malic acid. For lignin degradation products, the trend associated with increasing temperature noted using the GC×GC-TOF/MS method is not recapitulated in this report. In our prior study, it was observed that for lower NaOH loading, more p-coumaric acid and ferulic acid were extracted at a process temperature of 130 °C. The GEMBE method shows comparable levels of these analytes for both process temperatures across the entire range of treatment severity. The most likely source of this difference is batch-to-batch variability, as the GC×GC-TOF/MS and GEMBE methods were characterized using samples from different process batches. We cannot rule out the possibility that source of the discrepancy was the coelution of p-coumaric acid with 2-hydroxyisocaproic acid and ferulic acid with catechol and syringic acid. If the extraction of 2-hydroxyisocaproic acid, catechol, or syringic acid increases with temperature, the confounded measurement of the two species by GEMBE could explain the elimination of the difference in extraction at the two treatment temperatures observed by the GC×GC-TOF/MS analysis. There were 8 analytes quantified using both methods. All of the analytes quantified show a high degree of correlation between the measurements except 3-hydroxypropionic acid (Figure 6A−C). The presence and nature of bias between the methods is shown with Bland−Altman35 plots (Figure 6D−F). For four of the analytes (ferulic acid, vanillic acid, p-coumaric acid, and 2-hydroxybutanoic acid), including all the lignin derived aromatic compounds, there appears to be some constant bias, with GEMBE giving values on the order of 1 mg/mL higher than the GC×GC-MS method (Figure 6D). The magnitude of this bias is larger for the samples processed at 160 °C. For the compounds that are correlated but not comparable, the nature of the bias appears to be proportional to the signal (Figure 6E). The proportionality between the methods indicates that relative changes between samples determined using one method would be recapitulated with the other. The samples analyzed were not duplicate samples, but rather process replicates, and the analyses were conducted following different storage protocols prior to analysis. Examination of the role these differences play in the observed bias between the methods is the subject of future work.

Figure 3. 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 pretreatment severity. Corn stover pretreatments were performed for 30 min at temperatures of 130 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.

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CONCLUSIONS In this report, we have demonstrated GEMBE can be used to quantify 17 compounds in APL with a characteristic LOD on the order of 2 μM, and LOQ on the order of 6 μM. We compare the results from our GEMBE characterization of a set of corn stover APL samples to results generated by an orthogonal GC×GC-MS method.4 The analytical performance of the two methods in terms of accuracy and LOD is comparable, but GEMBE offers distinct advantages in terms of analysis time, sample preparation requirements, and cost of equipment. Opportunities to further improve the resolution of

amount of sugar degradation products present in APL. GEMBE analysis of APL samples shows that 3-hydroxypropionic acid (Figure 4), succinic acid, and formic acid (Figure 5) also demonstrate this behavior. Succinic acid and formic acid were not tracked with GC×GC-TOF/MS. The trend observed for 3hydroxypropionic acid in the GC×GC-TOF/MS method, F


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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 GC×GC-TOF/MS-based method.4 General trends are seen in that increasing pretreatment temperature creates more glycolic acid (A), 2-hydroxybutanioc 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 standard deviation of three replicate measurements conducted on different days.

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the GEMBE method without substantially increasing the analysis time include addition of selective agents and adjustment of BGE pH. Data analysis for GEMBE requires custom written pipelines, which represents a significant effort that is not required for the GC×GC-TOF/MS method. The sensitivity of the nonlinear least-squares curve fitting to the initial guess for peak position, and peak asymmetry that results from sample stacking play a role in the uncertainty of the measurement. Improvement of these pipelines represents a significant avenue of improvement for future work. GEMBE is further limited in its ability to detect neutral analytes, e.g., glycerol. Our ability to detect catechol using our current method suggests that BGE chemistries can be employed that generate complexes of neutral analytes with a charged tag allowing their detection. Taken together, the GC×GC-TOF/MS method and GEMBE are powerful companion methods allowing unambiguous identification of target compounds and rapid quantification, respectively. Preliminary characterization of other samples types indicates that GEMBE should be extendable to the other sample types that the GC-based method has been applied to, such as pyrolysis oil and for tracking bioconversion. Given past success with GEMBE for real-time tracking of chemical reactions, and the fact that dilution is the only preparative step required prior to analysis, it is possible to envision GEMBE-instrumented bioreactors for automated real-time monitoring with a time resolution on the order of the separation time (i.e., 20 min).

EXPERIMENTAL SECTION

Production of APL. Alkaline pretreatment of corn stover and switchgrass were performed as previously reported.2,3 Briefly, dried stover was loaded into sealed stainless steel vessels at mass fraction of 10% in water. Sodium hydroxide was added at a concentration between 35 mg/g dry stover and 660 mg/g dry stover, matching previous conditions.2 Vessel temperature was elevated to 130 or 160 °C for 30 min at temperature. APL samples were aliquoted and stored at 4 °C prior to analysis. Repeated analysis of the same sample showed no changes in composition on a time scale of months. Preparation of Materials. Standard solutions of potential analytes were prepared at a nominal concentration of 270 mmol/L in either pyridine or reagent grade deionized water (Sigma-Aldrich) as indicated in Table S1. Standard solutions were stored in glass amber vials with PTFE back silicon septa at room temperature. Background electrolyte (BGE) consisted of 250 mmol/L tris(hydroxymethyl)aminomethane (Sigma-Aldrich) and 250 mmol/L boric acid (Sigma-Aldrich) and filtered with a 0.2 μm disposable vacuum filtration unit (Thermo). Sodium bromide (Sigma-Aldrich) stock solution was prepared at a concentration of 0.5 mol/L in deionized water. Sample dilution buffer was prepared by diluting the sodium bromide stock solution in BGE to a concentration of 30 μmol/L. APL samples were diluted 100× in sample dilution buffer; these samples were subsequently diluted 10× in sample dilution buffer for a net dilution of 1000×. Both dilution levels were analyzed for each sample. All analysis was completed with a single batch of BGE. Because signal generation using capacitively coupled contactless conductivity detection is a result of displacement of BGE ions, small batch-to-batch variations in buffer composition can impact the slope and dynamic range of the calibration curve for each analyte. Measurement of Sample Conductivity. The conductivities of diluted samples, BGE, and sample dilution buffer were measured at room temperature using a conductivity meter (LAQUAtwin, Horiba) following the manufacturer’s instructions. Briefly, the sensor was G


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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 GC×GC-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 standard deviation of three replicate measurements conducted on different days. GEMBE Separation Procedures. Samples were removed and/or added to the sample reservoir using a pipet. A 200 μL sample volume was used in all experiments. To prevent carryover between samples, prior to sample loading, the sample reservoir was rinsed three times with 200 μL of BGE and once with an aliquot of interest. During sample loading, the applied pressure was set to 32 kPa to ensure a flow of run buffer through the capillary and prevent air bubbles. After sample loading, high voltage was applied (2500 V) and the pressure was maintained at 32 kPa for approximately 10 s. The applied pressure was then ramped downward at a rate of 63 Pa/s for 900 s. To rinse rapidly the separated sample from the capillary, the pressure was then increased again to 68 kPa, and held for approximately 20 s, after which the high voltage was turned off. The pressure was then set to 32 kPa for 30 s prior to evacuating the sample from the reservoir. The detector settings were frequency, 2× high; voltage, 0 dB; gain, 200%; offset, 0; filter, slow; data acquisition rate, 19.8 Hz. Calibration. Mixtures were prepared from standard stocks as follows. Six equimolar subpools were composed by mixing equal volumes of three standard solutions. Each subpool consisted of one aqueous standard and two with pyridine as solvent. The assignment into subpools was based on elution order in order to maximize the elution time difference between members of a subpool; e.g., subpool 1 consisted the first (sulfuric acid), seventh (acetic acid), and thirteenth (sulfuric acid) species. Subpools were then mixed at fixed ratios using a latin square design (ESI Table SIII), resulting in six calibration pools each with three components nominal concentrations of at 30, 24, 18, 12, 6, and 0 mmol/L. The pools were structured such that adjacent analytes were at disparate or comparable levels to enable assessment of cross-talk between analytes. Calibration pools were diluted 100× in sample dilution buffer; these samples were subsequently diluted 10× in sample dilution buffer for a net dilution of 1000× prior to analysis, resulting in a calibration range for each analyte of 6 to 300 μM at 10 levels and duplicate null measurements. Calibration curves were

prepared by application of a wetting solution for 10 min followed by rinsing with deionized water, and calibration using a manufacturer provided standard solution with a nominal conductivity of 1.4 mS/cm. The sensor was rinsed with deionized water until the meter read 1 μS/ cm or less. The sensor was then rinsed with an aliquot of sample, prior to measurement. GEMBE Apparatus. GEMBE is able to quantify rapidly and efficiently multiple compounds in complex biomass streams with little to no expensive equipment. The apparatus for performing GEMBE separations has been described previously22,30 (Figure 1). The sample reservoir was machined from acetal copolymer (McMaster-Carr) to accommodate approximately 200 μL. The BGE reservoir was machined from poly(ether imide) (LabSmith) to contain 2 mL. The separation channel consisted of a 5 cm long fused silica capillary (Polymicro Technologies) with nominal outer and inner diameters of 360 and 10 μm, respectively. The separation channel was inserted approximately 1 mm into the lumen of the sample reservoir through a 360 μm drilled hole, passed through a C4D detector (TraceDec, Innovative Sensor Technologies) and approximately 4 mm into the BGE reservoir through a miniature compression fitting (LabSmith). High voltage (PS350, Stanford Research Systems) was applied using platinum wires inserted into the reservoirs. The polarity of the voltage applied to the buffer reservoir was positive relative to the grounded sample reservoir, so that the direction of electroosmotic flow would be from the BGE reservoir toward the sample. A precision pressure controller (Series 600, Mensor), with air as the supply gas, was used to control the headspace pressure inside the sealed run buffer reservoir. Instrument control and data acquisition was automated using LabView software (National Instruments). The same capillary was used in all experiments without repositioning. Changing or repositioning the capillary can slightly alter the calibration due to shifts in the detection volume relative to the sample reservoir. H


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Figure 6. Comparison of measured concentrations for analytes quantified using both the GC×GC-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 2hydroxybutanoic acid (B) but not well correlated for 3-hydroxypropionic acid (C). The nature of the bias between the two methods is demonstrated using Bland−Altman plots. Ferulic acid, p-coumaric acid, vanillic acid, and 2-hydroxybutanoic acid show constant bias that is temperature dependent (D) whereas lactic acid, glycolic acid, and glyceric acid each demonstrate proportional bias (E). Differences are calculated by subtracting GEMBE values from GC×GC-TOF/MS values. Error bars are the standard deviation of three replicate measurements conducted on different days. collected in full on three different days, along with appropriate blank samples to normalize for stacking. The order of analysis of the calibrants was randomized within a day. Data Processing. Data processing pipelines were custom coded in R. Raw electropherograms were converted to peaks for data visualization using a 161-point Savitsky−Golay derivative. Individual steps were modeled as the sum of an error function and a quadratic baseline. Steps were fit individually when there was adequate resolution. Bromide, oxalic acid, formic acid, protocatechuic acid, and acetovanillone were fit as single species. Other analytes were combined into a single zone and fit simultaneously as the sum of the appropriate number of error functions as follows: succinic acid/system peak/malic acid, acetic acid/glycolic acid, 3-hydroxypropanoic acid/ lactic acid/glyceric acid, 2-hydroxybutanoic acid/4-hydroxybenzoic acid, vanillic acid/p-coumaric acid, and ferulic acid/impurity/sinapic acid. Steps corresponding to chloride, sulfate, and muconate/ carbonate/phosphate were not fit, as they were of limited interest. Step heights for each analyte zone were determined for either the 100× or 1000× dilution sample. Oxalic acid, formic acid, and acetic acid/glycolic acid zones were fit using the 1000× dilutions. Remaining analytes were determined from the 100× dilution samples. Because of the sensitivity of the parameter algorithm to the initial values, the data processing pipeline supplied graphical summaries of all curve fits. Initial positions and data windows were adjusted as necessary for individual electropherograms until stable representative fits were obtained for all species of interest. The height of the bromide step was fit for every sample to compensate for differences in stacking arising from differences in sample conductivity. For each sample, the “stacking ratio” was defined as the ratio of the height of the bromide step in the sample to the height of the bromide step in a control sample consisting only of

sample dilution buffer (corrected for the small differences in bromide concentration arising from dilution of the sample). The stacking ratio was used to scale the heights determined for each step in the electropherogram. Calibration curves (described above) were used to determine the concentration of each analyte in the diluted sample, which was then scaled by the dilution factor to recover the concentration in the raw sample.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.6b02076.

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Measurement error and chemical identification information (PDF)

AUTHOR INFORMATION

Corresponding Author

*Gregg T. Beckham. Email: [email protected]. Funding

E.M.K., C.T.N., and G.T.B. thank the U.S. Department of Energy Bioenergy Technologies Office (DOE-BETO) for funding via Contract No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. Notes

The authors declare no competing financial interest. I


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ACKNOWLEDGMENTS M.S.M. and M.S. acknowledge Drew Endy and Christina Smolke for graciously hosting this work in their laboratories, Stephanie Galanie for logistical support, Sarah Munro, Ariel Hecht, and Jerod Parsons for useful support in the construction of data analysis pipelines, and Scott Pine for assistance in designing calibration pools. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Certain commercial equipment, instruments, or materials are identified in this report to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

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Gradient Elution Moving Boundary Electrophoresis Enables Rapid

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