New Insights for the Future Design of Composites Composed of Hydrochar and Zeolite for Developing Advanced Biofuels from Cranberry Pomace
Bibliographic record
Abstract
This study provides fundamental insight and offers a promising catalytic hydrothermal method to harness cranberry pomace as a potential bioenergy and/or hydrochar source. The physical and chemical properties of Canadian cranberry pomace, supplied by Fruit d’Or Inc., were examined and the optimum operational conditions, in terms of biocrude yield, were obtained by the I-optimal matrix of Design Expert 11. Afterward, cranberry pomace hydrochar (CPH) and zeolite were separately introduced to the hydrothermal liquefaction (HTL) process to investigate the benefits and disadvantages associated with their catalytic activity. CPH was found to be a better host than zeolite to accommodate cellulosic sugars and showed great catalytic performance in producing hydrocarbons. However, high amounts of corrosive amino and aliphatic acids hinder the practical application of CPH as a catalyst. Alternatively, zeolite, as a commercial high surface area catalyst, had a higher activity for deoxygenation of compounds containing carbonyl, carboxyl, and hydroxyl groups than CPH and resulted in higher selectivity of phenols. Due to the low hydrothermal structural stability, coke formation, and narrow pore size distribution, further activations and modifications are needed to improve the catalytic behavior of zeolite. Our results suggest that a composite composed of CPH and zeolite can resolve the abovementioned limitations and help with the development and commercialization of advanced biofuels from cranberry pomace.
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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 | 0.001 | 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".