Pelletization can unlock the unrealized potential of lignocellulose as a resilient feedstock for biomanufacturing: enzymatic saccharification of biomass pellets
Bibliographic record
Abstract
Lignocellulose, as a plentiful and renewable carbonaceous resource, presents an alluring alternative to fossil fuels for sustaining industries in the pursuit of a resilient bio-based economy. Sugars derived from lignocellulosic biomass play a central role as versatile platform intermediates for feeding microorganisms or as starting chemicals for manufacturing value-added fuels, chemicals, and materials. However, commercialization faces challenges due to the complexity and high costs associated with feedstock logistics and conversion processes. Pelleting offers a potential solution by addressing logistical issues while providing additional benefits for downstream conversion that may outweigh the extra costs associated with pelleting. To fully unlock the economic and sustainable potential of lignocellulosic biomass in biorefineries, recent advances in pelleting technologies and their impacts on downstream pretreatments and enzyme-mediated conversion are critically reviewed. Pelleting has been shown to improve enzymatic digestibility yields by 5‒20%. The process variables, product attributes, and their influences on bioconversion are discussed. More significantly, a thorough discussion of the effect of pelleting on various pretreatments, concerning diverse feedstocks, as well as their interplay, is provided to inform the design of future pelleting and pretreatment processes. Finally, practical considerations, including energy consumption, costs, and environmental impacts, are discussed, alongside an exploration of cutting-edge technologies and strategies in this field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".