Sustainable poplar biorefinery producing butanol-rich solvents, furfural, and lignin-derived compounds with environmental and economic benefits
Bibliographic record
Abstract
The transformation of poplar biomass into bio-based chemicals, fuels, and lignin-derived products through an integrated biorefinery is essential for realizing its full potential as a sustainable and economically viable feedstock. This study presents a poplar biorefinery approach using mild biphasic pretreatment (p-toluenesulfonic acid/pentanol + AlCl3, 110°C, 40 min) to produce bio-based platform multiple products. The pretreatment achieved efficient fractionation, with 83.2% delignification, 95.2% xylan removal, and minimal cellulose loss (7.8%), enabling high-yield one-pot furfural production (68.5%, 11.3 g/L). Enzymatic hydrolysis of the cellulose-rich residue, combined with fermentation by Clostridium acetobutylicum, produced a bio-solvent mixture of 16.2 g/L, including 10.5 g/L butanol. Depolymerized lignin was recovered and subjected to catalytic hydrogenolysis, yielding 46.4% monomers, 9.3% dimers, and 17.4% oligomers. Processing 140 Mt of poplar biomass annually at scale could deliver substantial environmental and economic gains, avoiding approximately 64.12 Mt of CO2-eq emissions and generating an estimated USD 2.97 billion in annual socioeconomic benefits. Sensitivity analysis confirmed biomass availability as the dominant factor influencing emission reduction. Economic evaluation demonstrated strong financial viability, with an aggregate net present value of USD 65.7 billion projected for full national implementation. This work establishes a holistic and economically compelling biorefinery strategy for the sustainable production of bio-based chemicals and fuels.
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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 | 0.001 | 0.001 |
| 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".