Bibliographic record
Abstract
Use of biotechnology in pulp bleaching has attracted considerable attention and achieved interesting results in recent years. Enzymes of the hemicellulolytic type, particularly xylan-attacking enzymes, xylanases are now used commercially in the mills for pulp treatment and subsequent incorporation into bleach sequences. The aims of the enzymatic treatment depend on the actual mill conditions and may be related to environmental demands, reduction of chemical costs or maintenance or even improvement of product quality. The use of oxidative enzymes from white-rot fungi, that can directly attack lignin, is a second-generation approach, which could produce larger chemical savings than xylanase but has not yet been developed to the full scale. It is being studied in several laboratories in Canada, Japan, the U.S.A. and Europe. Certain white-rot fungi can delignify kraft pulps increasing their brightness and their responsiveness to brightening with chemicals. The fungal treatments are too slow but the enzyme manganese peroxidase and laccase can also delignify pulps and enzymatic processes are likely to be easier to optimize and apply than the fungal treatments. Development work on laccase and manganese peroxidase continues. This article presents an overview of developments in the application of hemicellulase enzymes, lignin-oxidizing enzymes and white-rot fungi in bleaching of chemical pulps. The basic enzymology involved and the present knowledge of the mechanisms of the action of enzymes as well as the practical results and advantages obtained on the laboratory and industrial scale are discussed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".