Nanomechanics of Lignin–Cellulase Interactions in Aqueous Solutions
Bibliographic record
Abstract
Efficient enzymatic hydrolysis of cellulose in lignocellulose to glucose is one of the most critical steps for the production of biofuels. The nonproductive adsorption of lignin to expensive cellulase highly impedes the development of biorefinery. Understanding the lignin–cellulase interaction mechanism serves as a vital basis for reducing such nonproductive adsorption in their practical applications. Yet, limited report is available on the direct characterization of the lignin–cellulase interactions. Herein, for the first time, the nanomechanics of the biomacromolecules including lignin, cellulase, and cellulose were systematically investigated by using a surface force apparatus (SFA) at the nanoscale in aqueous solutions. Interestingly, a cation−π interaction was discovered and demonstrated between lignin and cellulase molecules through SFA measurements with the addition of different cations (Na+, K+, etc.). The complementary adsorption tests and theoretical calculations further confirmed the validity of the force measurement results. This finding further inspired the investigation of the interaction between lignin and other noncatalytic-hydrolysis protein (i.e., soy protein). Soy protein was demonstrated as an effective, biocompatible, and inexpensive lignin-blocker based on the molecular force measurements through the combined effects of electrostatic, cation−π, and hydrophobic interactions, which significantly improved the enzymatic hydrolysis efficiencies of cellulose in pretreated lignocellulosic substrates. Our results offer quantitative information on the fundamental understanding of the lignin–cellulase interaction mechanism. Such unraveled nanomechanics provides new insights into the development of advanced biotechnologies for addressing the nonproductive adsorption of lignin to cellulase, with great implications on improving the economics of lignocellulosic biorefinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".