Demethylation of Wheat Straw Alkali Lignin for Application in Phenol Formaldehyde Adhesives
Bibliographic record
Abstract
Lignin is a natural biopolymer with a complex three-dimensional network. It is the second most abundant natural polymer on earth. Commercially, lignin is largely obtained from the waste liquors of pulping and bioethanol productions. In this study, wheat straw alkali lignin (WSAL) was demethylated by using an in-situ generated Lewis acid under an optimized demethylation process. The demethylation process was monitored by a semi-quantitative Fourier Transform Infrared Spectroscopy (FTIR) method. The demethylated wheat straw alkali lignin (D-WSAL) was further characterized by Proton Nuclear Magnetic Resonance (1H NMR), Gel Permeation Chromatography (GPC), and titration methods. After the demethylation process, it was found that the relative value of the methoxy group decreased significantly from 0.82 to 0.17 and the phenolic hydroxyl group increased from 5.2% to 16.0%. Meanwhile, the hydroxyl content increased from 6.6% to 10.3%. GPC results suggested that the weighted averaged molecular weight of D-WSAL was lower than that of WSAL with a smaller polydispersity index. The D-WSAL was then used to replace 60 wt % of phenol to prepare lignin-based phenol formaldehyde adhesives (D-LPF). It was found that both the free formaldehyde content and the free phenol content in D-LPF were less than those of the lignin-based phenol formaldehyde adhesives without lignin demethylation (LPF). Gel time of D-LPF was shortened. Furthermore, the wet and dry bonding strengths of lap shear wood samples bonded using D-LPF were higher than those of the samples bonded using LPF. Therefore, D-WSAL has shown good potential for application in phenol formaldehyde adhesives.
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".