New Opportunities in the Valorization of Technical Lignins
Bibliographic record
Abstract
Sugar-based biorefineries have faced significant economic challenges. Biorefinery lignins are often classified as low-value products (fuel or low-cost chemical feedstock) mainly due to low lignin purities in the crude material. However, recent research has shown that biorefinery lignins have a great chance of being successfully used as high-value products, which in turn should result in an economy renaissance of the whole biorefinery idea. This critical review summarizes recent developments from our groups, along with the state-of-the-art in the valorization of technical lignins, with the focus on biorefinery lignins. A beneficial synergistic effect of lignin and cellulose mixtures used in different applications (wood adhesives, carbon fiber and nanofibers, thermoplastics) has been demonstrated. This phenomenon causes crude biorefinery lignins, which contain a significant amount of residual crystalline cellulose, to perform superior to high-purity lignins in certain applications. Where previously specific applications required high-purity and/or functionalized lignins with narrow molecular weight distributions, simple green processes for upgrading crude biorefinery lignin are suggested here as an alternative. These approaches can be easily combined with lignin micro-/nanoparticles (LMNP) production. The processes should also be cost-efficient compared to traditional lignin modifications. Biorefinery processes allow much greater flexibility in optimizing the lignin characteristics desirable for specific applications than traditional pulping processes. Such lignin engineering, at the same time, requires an efficient strategy capable of handling large datasets to find correlations between process variables, lignin structures and properties and finally their performance in different applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".