Cost-effective extraction of tannins from Canadian yellow birch barks
Bibliographic record
Abstract
Of the 17 million m3 of forestry barks, approximately 40% is used as a source of energy in Canadian pulp and paper mills. In recent years, due to their high fraction of tannin, lignin and cellulose, forestry barks have attracted growing interest as feedstock for development and commercialization of industrial biochemicals and biomaterials. The purpose of this work is to develop a cost-effective process to extract the key components of barks and convert them into high value-added polymers to contribute in replacing the products made from materials with a more intensive carbon footprint. Tannin and lignin constitute 50% of bark components. With a growing interest in greener water-soluble polyphenols, bark-based tannins are opening up new potential markets such as wood adhesives and rigid foams for building insulation. Global tannin market is expected to witness substantial growth over the next ten years. In this poster presentation, we focus on the cost-effective method for the extraction of tannins from Canadian yellow birch barks. The aim is to maximize tannin yield while minimizing energy and water consumptions and extraction cost. Mechanical preprocessing of the fresh-cut barks was carried out to facilitate the extraction of tannins. Extraction was taken place in hot water with the presence and absence of ultrasound energy, as well as in near-supercritical CO2-H2O system. Different extraction parameters, such as temperature, pressure, time, liquid/solid ratios, pH, CO2/H2O ratios, were used to optimize the technical performance of the tannins extraction processes. Water and energy consumptions for every step in the process were evaluated and minimized.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".