Bio-oil hydrodeoxygenation over acid activated-zeolite with different Si/Al ratio
Bibliographic record
Abstract
Bio-oil includes significant levels of oxygenate molecules, which might induce component instability and reduce its physicochemical qualities. To counteract this, the component must undergo a hydrodeoxygenation (HDO) reaction. Due to the presence of acidic active sites, zeolites have been shown to have high hydrogenation and deoxygenation capabilities. However, natural zeolite has a large number of impurities and low acidity density. Consequently, before being employed as an HDO catalyst, pretreatments such as preparation and activation are required. In this study, the catalyst used was an active natural zeolite whose acidity level varied depending on the Si/Al ratio after dealumination with 3, 5, and 7 M hydrochloric acid, proceeded by calcination with nitrogen gas flow (designated as Z3, Z5, and Z7, respectively). The results showed that dealumination and calcination of zeolite generally caused changes in its physical characteristics and components. The Z5 catalyst showed the best catalytic performance in the HDO process of bio-oil. The higher heating value (HHV) of bio-oil increased from 12 to 18 MJ/kg, the viscosity value doubled, the degree of deoxygenation increased to 77%, and the water content reduced dramatically to about one-third of that of raw bio-oil. Moreover, control compounds, such as carboxylic acids, decreased slightly, but the amount of phenol increased to about twice the content in raw bio-oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".