A finalized determinant for complete lignocellulose enzymatic saccharification potential to maximize bioethanol production in bioenergy Miscanthus

Abstract

Background: Miscanthus is a leading bioenergy crop with enormous lignocellulose production potential for biofuels and chemicals. However, lignocellulose recalcitrance leads to biomass process difficulty for an efficient bioethanol production. Hence, it becomes essential to identify the integrative impact of lignocellulose recalcitrant factors on cellulose accessibility for biomass enzymatic hydrolysis. In this study, we analyzed four typical pairs of Miscanthus accessions that showed distinct cell wall compositions and sorted out three major factors that affected biomass saccharification for maximum bioethanol production.

Results: Among the three optimal (i.e., liquid hot water, H₂SO₄ and NaOH) pretreatments performed, mild alkali pretreatment (4% NaOH at 50 °C) led to almost complete biomass saccharification when 1% Tween-80 was co-supplied into enzymatic hydrolysis in the desirable Miscanthus accessions. Consequently, the highest bioethanol yields were obtained at 19% (% dry matter) from yeast fermentation, with much higher sugar-ethanol conversion rates by 94-98%, compared to the other Miscanthus species subjected to stronger pretreatments as reported in previous studies. By comparison, three optimized pretreatments distinctively extracted wall polymers and specifically altered polymer features and inter-linkage styles, but the alkali pretreatment caused much increased biomass porosity than that of the other pretreatments. Based on integrative analyses, excellent equations were generated to precisely estimate hexoses and ethanol yields under various pretreatments and a hypothetical model was proposed to outline an integrative impact on biomass saccharification and bioethanol production subjective to a predominate factor (CR stain) of biomass porosity and four additional minor factors (DY stain, cellulose DP, hemicellulose X/A, lignin G-monomer).

Conclusion: Using four pairs of Miscanthus samples with distinct cell wall composition and varied biomass saccharification, this study has determined three main factors of lignocellulose recalcitrance that could be significantly reduced for much-increased biomass porosity upon optimal pretreatments. It has also established a novel standard that should be applicable to judge any types of biomass process technology for high biofuel production in distinct lignocellulose substrates. Hence, this study provides a potential strategy for precise genetic modification of lignocellulose in all bioenergy crops.

Keywords: Bioethanol yield; Biomass porosity; Biomass saccharification; Miscanthus; Polymer features; Polymer linkages.

Fig. 1

Biomass saccharification under liquid hot water and chemical pretreatments in four typical pairs of Miscanthus accessions. a Hexose yields (% cellulose) released from enzymatic hydrolysis after LHW pretreatments under a time course; b, c hexose yields from enzymatic hydrolysis after H₂SO₄, and NaOH at a series of concentrations. (H) and (L) represented two samples of each pair showing relatively high (H) and low (L) biomass saccharification, and the data as mean ± SD (n = 3)

Fig. 2

Scanning electron microscopic observations of lignocellulose residues after optimal LHW and chemical (4% H₂SO₄, 4% NaOH) pretreatments and sequential enzymatic hydrolysis in the representative Pair II samples. Arrows indicated rough points of lignocellulose surfaces

Fig. 3

Tween-80 enhancement on biomass enzymatic saccharification under optimal pretreatments in four typical pairs of Miscanthus accessions. a Hexose yields (% cellulose) released from enzymatic hydrolysis of the raw materials (without pretreatment) co-supplied with 1% Tween-80. b–d Hexose yields released from enzymatic hydrolysis co-supplied with 1% Tween-80 after optimal LHW, H₂SO₄, and NaOH pretreatments. (H) and (L) represented two samples of each pair showing relatively high (H) and low (L) biomass saccharification, and the data as mean ± SD (n = 3). * and ** as significant difference between the control (without Tween-80) and the sample co-supplied with Tween-80 by t test at P < 0.05 and 0.01, respectively, and the percentage (%) calculated by subtraction of two samples divided by the value of control (without Tween-80)

Fig. 4

Bioethanol production released from yeast fermentation in four typical pairs of Miscanthus accessions. a Ethanol yield (% dry matter) using total hexoses released from enzymatic hydrolysis of raw materials co-supplied with 1% Tween-80. b–d Ethanol yields using total hexoses released from enzymatic hydrolysis of three optimal pretreatments co-supplied with 1% Tween-80. e Correlation analysis between hexoses and ethanol yields from all pretreated biomass residues and raw materials (n = 32). Gray and black column indicated two samples of each pair showing relatively high (H) and low (L) biomass saccharification as shown in Fig. 3, and the data as mean ± SD (n = 3). * and ** as significant difference between two samples of each pair by t test at P < 0.05 and 0.01 (n = 3), and the percentage (%) calculated by subtraction of two samples divided by relative low value

Fig. 5

Alteration of cell wall composition after three optimal pretreatments in four typical pairs of Miscanthus accessions. A Cellulose level (% dry matter); B hemicelluloses and C lignin. Raw as raw materials without pretreatment. The line and square within the box presented the median and mean values of all data (n = 32); the bottom and top edges of the box indicated 25% and 75% of all data; the top and bottom bars (×) presented maximum and minimum values of all data, and the different letters (a, b, c, d) indicated that the mean values are significantly different from each other by LSD test (P < 0.05), respectively

Fig. 6

Mass balance flow chart of Miscanthus biomass for bioethanol (on a 100 g basis) among three optimal pretreatments, simultaneous saccharification, and fermentation process. a LHW pretreatment of Mlu26 Miscanthus sample; b H₂SO₄ (purity: 98%) pretreatment of Mlu26 Miscanthus sample; c NaOH pretreatment of Mlu11 Miscanthus sample. Mass amount of insoluble wall polymers (cellulose, hemicelluloses, and lignin) are presented in streams 1 and 3. Mass amount of soluble sugars (hexoses and pentoses) and ethanol yield are presented in streams 2, 4, and 5

Fig. 7

Alteration of major wall polymer features after three optimal pretreatments in four typical pairs of Miscanthus accessions. a Cellulose CrI (%); b cellulose DP; c xylose of hemicelluloses (% of total); d X/A ratio of hemicelluloses, and e three lignin monomers (µmol/g). Raw as raw materials without pretreatment. The line and square within the box presented the median and mean values of all data (n = 32); the bottom and top edges of the box indicated 25 and 75 percentiles of all data; the top and bottom bars (×) presented maximum and minimum values of all data, and the different letters (a, b, c, d) indicated that the mean values are significantly different from each other by LSD test, respectively

Fig. 8

Comparison of Fourier transform infrared spectroscopic profiling among the raw materials (black) and three optimal pretreated (LHW-pink, 4% H₂SO₄-blue, 4% NaOH-red) biomass residues of Pair II Miscanthus samples. Dot squares indicated the majorly altered bonds from the optimal pretreatments as elucidated in Additional file 1: Table S11

Fig. 9

Comparison of biomass porosity among the raw materials (raw) and three optimal pretreated (LHW, H₂SO₄, NaOH) biomass residues of Miscanthus samples. A Yellow dye (DY) and total dye from Simons stains in four typical pairs of samples (mg/g). B Ratio (Y/B) between yellow dye (DY) and blue dye (DB) from Simons stains in four typical pairs of samples. C Congo red (CR) dye stain in four typical pairs of samples (m²/g; 1 g CR adsorbed corresponds to the area 1055 m² of biomass). D Mixed-cellulase enzyme adsorption (E_max; mg/g biomass) in four typical pairs of samples. E BET specific surface area and F BJH cumulative pore volume in the Pair II samples. The line among the solid dots as means (n = 8) and the different letters (a, b, c, d) indicated that the mean values are significantly different from each other by LSD test, respectively

Fig. 10

Path coefficient assay between major factors of biomass porosity and hexose yields released from enzymatic hydrolysis in the raw materials and three optimal pretreated biomass residues of four pairs of Miscanthus samples. a Path coefficient assay between hexose yields and Simons’ stains (DY, Y/B ratio, total dye). b Path coefficient assay between hexose yields and Congo red (CR)/enzyme adsorbed (E_max). c Path coefficient assay between hexose yields and CR/DY (n = 32)

Fig. 11

Path coefficient assay between wall polymer features and major factors of biomass porosity in the raw materials and three optimal pretreated biomass residues of four pairs of Miscanthus samples. a, b Path coefficient assay between hemicellulose monosaccharides (Ara, Xyl, A/X ratio) and Congo red (CR)/yellow dye (DY). c, d Path coefficient assay between lignin monomers (H, G, S) and CR/DY (n = 32)

Fig. 12

Gray correlation assay between three wall polymer features (cellulose DP, hemicellulose X/A, and lignin G-monomer) and two factors (DY and CR) of biomass porosity in the raw materials and three optimal pretreated biomass residues of four pairs of Miscanthus samples. a Gray correlation assay between CR and three wall polymer features. b Gray correlation assay between DY and three wall polymer features (n = 32)

Fig. 13

Regression calculation of equations to estimate hexoses and ethanol yields in the raw materials and three optimal pretreated biomass residues of four pairs of Miscanthus samples (n = 32). a Equation between CR of biomass porosity and hexose yields; b equation between CR of biomass porosity and ethanol yields; c equation between five factors (CR and DY of biomass porosity and three major wall polymer features) and hexose yields; d equation between five factors and ethanol yields; e equation between hexose yields and five factors plus their interactions with each other. f Equation between ethanol yields and five factors plus their interactions with each other

Fig. 14

A hypothetical model to highlight an integrative impact on biomass saccharification and bioethanol production subjective to a predominate factor (CR stain) of biomass porosity and four additional minor factors (DY stain, cellulose DP, hemicellulose X/A, lignin G-monomer) under three optimal pretreatments in bioenergy Miscanthus and beyond