Interaction of enzymes with lignocellulosic materials: causes, mechanism and influencing factors
Bibliographic record
Abstract
Abstract For the production of biofuel (bioethanol), enzymatic adsorption onto a lignocellulosic biomass surface is a prior condition for the enzymatic hydrolysis process to occur. Lignocellulosic substances are mainly composed of cellulose, hemicellulose and lignin. The polysaccharide matrix (cellulose and hemicellulose) is capable of producing bioethanol. Therefore, lignin is removed or its concentration is reduced from the adsorption substrates by pretreatments. Selected enzymes are used for the production of reducing sugars from cellulosic materials, which in turn are converted to bioethanol. Adsorption of enzymes onto the substrate surface is a complicated process. A large number of research have been performed on the adsorption process, but little has been done to understand the mechanism of adsorption process. This article reviews the mechanisms of adsorption of enzymes onto the biomass surfaces. A conceptual adsorption mechanism is presented which will fill the gaps in literature and help researchers and industry to use adsorption more efficiently. The process of enzymatic adsorption starts with the reciprocal interplay of enzymes and substrates and ends with the establishment of molecular and cellular binding. The kinetics of an enzymatic reaction is almost the same as that of a characteristic chemical catalytic reaction. The influencing factors discussed in detail are: surface characteristics of the participating materials, the environmental factors, such as the associated flow conditions, temperature, concentration, etc. Pretreatment of lignocellulosic materials and optimum range of shear force and temperature for getting better results of adsorption are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".