Recent Advances in Hydroliquefaction of Biomass for Bio-oil Production Using In Situ Hydrogen Donors
Bibliographic record
Abstract
With the rapid growth of energy demand and environmental concerns associated with traditional fossil fuels, renewable and sustainable energy sources have attracted intensive research attention in recent years. Biomass is considered as one of the most promising alternative energy resources due to its many advantages including abundance, renewability, carbon neutrality, and worldwide distribution. Liquefaction is an efficient thermochemical conversion route for the production of bioderived fuels and chemicals under mild reaction conditions. In addition, this process does not require energy-intensive drying as a preprocessing step of the wet biomass feedstocks. Nevertheless, subsequent hydrogenation and upgrading treatment of the bio-oil with high oxygen content are imperative for practical applications mainly for improving the calorific value of the bio-oil. The cost and safety issues of external hydrogen are main obstacles for the liquefaction-upgrading route, which can be somewhat offset by the use of in situ hydrogen. The research on in situ hydrogen generation and use for biomass liquefaction is nascent. Therefore, the latest research developments on hydroliquefaction of biomass feedstocks in the presence of various in situ hydrogen donors are reviewed in this article. Several commonly applied in situ hydrogen donors including formic acid, alcohols (isopropyl alcohol, methanol and ethanol), and zerovalent metals (zinc, aluminum, iron, etc.) are discussed in detail focusing mainly on their influence on the distribution of liquefied products and the mechanisms of hydrogen-donation/hydrogenation reactions. Moreover, future research directions toward the commercial applications of biomass liquefaction technology with in situ hydrogenation strategies are presented.
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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 | 0.001 | 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 | 0.000 | 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".