Ether Bond Formation in Waste Biomass–Derived, Value-Added Technical Hardwood Kraft Lignin Using Glycolic Acid
Bibliographic record
Abstract
Ether bond formation in technical hardwood kraft lignin (THKL) by crosslinking using glycolic acid was investigated for bio-adhesive applications. Industrial hardwood kraft black liquor was used to extract the THKL utilized by acidification. Chemical and thermal properties of the THKL with and without crosslinking were analyzed by Fourier transform infrared (FTIR) spectroscopy, solid-state 13C cross-polarization/magic angle spinning nuclear magnetic resonance (13C CP/MAS NMR) spectroscopy, X-ray photoelectron spectroscopy (XPS), gel permeation chromatography (GPC), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). FTIR results revealed a new peak corresponding to the ether bond and hemiacetal formation due to crosslinking at 1075 cm-1 and 1324 cm-1. 13C CP/MAS NMR spectra revealed the presence of a higher number of ether bonds due to the reduced aromatic and aliphatic hydroxyl groups in THKL and new bonds formed at 62-64 ppm and 168-191 ppm due to crosslinking. XPS results revealed that new bonds were formed between glycolic acid and THKL, leading to increased atomic oxygen percentage and carbon–oxygen bonds in crosslinked THKL detected by peak intensity changes at 287.7 and 288.8 related to O–C–O and O–C=O. Also, the oxygen content increased from 14.88% to 31.76% due to bond formation. GPC confirmed a higher molecular weight and broader molecular-weight distribution of THKL. DSC and TGA curves of crosslinked THKL revealed exothermic behavior, high thermal stability, and low thermal degradation rate. Owing to a significant amount of kraft black liquor being generated by wood pulp industries and attractive chemical properties of THKL, THKL demonstrates promise as a raw material to produce green, sustainable bio-adhesives via the crosslinking of its different hydroxyl groups using glycolic acid.
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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) | 0.000 | 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".