Adsorption of Cellulase on Cellulolytic Enzyme Lignin from Lodgepole Pine
Bibliographic record
Abstract
Enzymatic hydrolysis of lignocellulosic materials is significantly affected by cellulase adsorption onto the lignocellulosic substrates and lignin. The presence of lignin plays an important role in lignocellulosic hydrolysis and enzyme recycling. Three cellulase preparations (Celluclast, Spezyme CP, and MSUBC) were evaluated to determine their adsorption onto cellulolytic enzyme lignin (CEL) from steam-exploded Lodgepole pine (SELP) and ethanol (organosolv)-pretreated Lodgepole pine (EPLP). The adsorption affinity of cellulase (Celluclast) onto isolated lignin (CEL-EPLP and CEL-SELP) was slightly higher than that from corresponding EPLP and SELP substrates on the basis of the Langmuir constants. Effects of temperature, ionic strength, and surfactant on cellulase adsorption onto isolated lignin were also explored in this study. Thermodynamic analysis of enzyme adsorption onto isolated lignin (Gibbs free energy change DeltaG(0) approximately -30 kJ/mol) indicated this adsorption was a spontaneous process. The addition of surfactant (0.2% w/v) could reduce the adsorption of cellulase onto CEL-SELP by 60%. Two types of adsorption isotherm were compared for cellulase adsorption onto isolated lignin. A Langmuir adsorption isotherm showed better fit for the experimental data than a Freundlich adsorption isotherm.
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 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) | 0.000 | 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 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".