Structural and compositional changes induced by hydrothermal and organosolv pretreatments impacts enzymatic hydrolysis of a tropical forage grass grown under future climate conditions
Bibliographic record
Abstract
Panicum maximum represents an alternative as a dedicated energy crop for biorefinery. However, both pretreatment step and climate conditions impact its chemical composition and possibly its hydrolytic performance. Therefore, this study looks into integrating the future effect of the climate conditions and the pretreatment methods on the hydrolytic performance of P. maximum. Through a Trop-T-FACE system, P. maximum grown under three hypothetical future climate conditions: elevated temperature ((eT), which is 2 °C more than the ambient canopy temperature), elevated atmospheric CO2 concentration ((eC) up to 600 μmol mol-1) and the combined effects (eT + eC). Subsequently, the groups were subjected to the liquid hot water (LHW) and organosolv (ORG) pretreatments, which enhanced biomass digestibility by mostly hemicellulose and lignin removal, respectively. Furthermore, optimized hydrolysis and fluorescent protein-tagged carbohydrate-binding modules binding suggested that P. maximum pretreated by LHW has greater saccharification yields and higher cellulose surface accessibility/exposure. This shows that hemicellulose directly impacts P. maximum recalcitrance, and its removal was the optimum pretreatment strategy. Concerning the studied expected future climate conditions, LHW samples did not show significant differences in hydrolysis yields at low solids loading (2%). However, at 10 % solids, eC had higher conversion yields (likely due to minimum end-product inhibition). Similarly, for organosolv pretreated samples, eC (at 2 and 10 % solid loading) exhibited greater cellulose-glucan conversion yields among the climate conditions groups and the highest cellulose accessibility. Thus, this work contributes to understand better the influence of both studied pretreatment methods and future climate conditions on the conversion efficiency of a critical energy crop and might assist in selecting the best pretreatment method to mitigate the climate effects and achieve greater hydrolysis yields.
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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 | 0.000 | 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".