Equilibrium Moisture Mediated Esterification Reaction to Achieve Over 100% Lignocellulosic Nanofibrils Yield
Bibliographic record
Abstract
Abstract Lignocellulosic nanofibrils (LCNFs) isolation is recognized as an efficient strategy for maximizing biomass utilization. Nevertheless, achieving a 100% yield presents a formidable challenge. Here, an esterification strategy mediated by the equilibrium moisture in biomass is proposed for LCNFs preparation without the use of catalysts, resulting in a yield exceeding 100%. Different from anhydrous chemical thermomechanical pulp (CTMP 0% ), the presence of moisture (moisture content of 7 wt%, denoted as CTMP 7% ) introduces a notably distinct process for the pretreatment of CTMP, comprising the initial disintegration and the post‐esterification steps. The maleic acid, generated through maleic anhydride (MA) hydrolysis, degrades the recalcitrant lignin‐carbohydrate complex (LCC) structures, resulting in esterified CTMP 7% (E‐CTMP 7% ). The highly grafted esters compensate for the mass loss resulting from the partial removal of hydrolyzed lignin and hemicellulose, ensuring a high yield. Following microfluidization, favorable LCNF 7% with a high yield (114.4 ± 3.0%) and a high charge content (1.74 ± 0.09 mmol g −1 ) can be easily produced, surpassing most previous records for LCNFs. Additionally, LCNF 7% presented highly processability for filaments, films, and 3D honeycomb structures preparation. These findings provide valuable insights and guidance for achieving a high yield in the isolation of LCNFs from biomass through the mediation of equilibrium moisture.
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) | 0.001 | 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".