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Record W4226309455 · doi:10.1186/s13068-022-02108-y

Polystyrene sulfonate is effective for enhancing biomass enzymatic saccharification under green liquor pretreatment in bioenergy poplar

2022· article· en· W4226309455 on OpenAlexaff
Tian Liu, Peipei Wang, Jing Tian, Jiaqi Guo, Wenyuan Zhu, Yongcan Jin, Huining Xiao, Junlong Song

Bibliographic record

VenueBiotechnology for Biofuels and Bioproducts · 2022
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of New Brunswick
FundersMajor Research PlanGovernment of Jiangsu ProvinceKey ProgrammePriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of ChinaNanjing Forestry University
KeywordsChemistryLigninHydrolysisCellulaseCelluloseEnzymatic hydrolysisPullulanLignosulfonatesBiomass (ecology)BioconversionOrganic chemistryPolysaccharideAgronomy

Abstract

fetched live from OpenAlex

BACKGROUND: Water-soluble lignin (particularly lignosulfonate, LS) has been well documented for its significance on enzymatic saccharification of lignocellulose, though the promotion mechanism has not been fully understood. Much attention has been paid to natural lignin or its derivatives. The disadvantage of using natural lignin-based polymers as promoting agents lies in the difficulty in tailor-incorporating functional groups due to their complex 3D structures. To further improve our understanding on the promotion mechanism of water-soluble lignin in the bioconversion of lignocellulose and to pursue better alternatives with different skeleton structures other than natural lignin, herein we reported a synthetic soluble linear aromatic polymer, sodium polystyrene sulfonate (PSS), to mimic LS for enhancing the efficiency of enzymatic saccharification. RESULTS: The role of PSS in enzymatic saccharification of pure cellulose and green liquor-pretreated poplar (GL-P) was explored by analyzing substrate enzymatic digestibility (SED) under different addition dosages and various pH media, along with LS for comparison. At the cellulase loading of 13.3 FPU/g-glucan, the glucose yield of GL-P increased from 53% for the control to 81.5% with PSS addition of 0.1 g/g-substrate. It outperformed LS with the addition of 0.2 g/g-substrate by 6.3%. In the pH range from 4.5 to 6, PSS showed a positive effect on lignocellulose saccharification with the optimum pH at 4.8, where the most pronounced SED of GL-P was achieved. The underlying mechanism was unveiled by measuring zeta potential and using Quartz Crystal Microbalance (QCM) and Multi-parametric Surface Plasmon Resonance (MP-SPR). The results confirmed that the complexes of cellulase and PSS were conjugated and the negatively supercharged complexes reduced non-productive binding effectively along with the improved saccharification efficiency. The thickness of PSS required to block the binding sites of cellulase film was less than half of that of LS, and the PSS adlayer on cellulase film is also more hydrated and with a much lower shear modulus than LS adlayer. CONCLUSIONS: PSS as LS analogue is effective for enhancing the biomass enzymatic saccharification of GL-pretreated poplar. PSS exhibited a severer inhibition on the enzymatic saccharification of pure cellulose, while a more positive effect on bioconversion of lignocellulose (GL-P) than LS. In addition, a much lower dosage is required by PSS. The dynamic enzymatic hydrolysis indicated PSS could prolong the processive activity of cellulase. The valid data stemmed from QCM and SPR expressed that PSS bound to cellulases and the as-formed complexes reduced the non-productive adsorption of cellulase onto substrate lignin more efficiently than LS due to its flexible skeleton and highly hydrated structure. Therefore, PSS is a promising alternative promoting agent for lignocellulose saccharification. From another perspective, the synthetic lignin mimics with controllable structures enable us to reach an in-depth understanding of the promotion mechanism of soluble lignins on enzymatic saccharification.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.207
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations18
Published2022
Admission routes1
Has abstractyes

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