A new process for the production of bio-jet fuel precursors from common carbohydrate sources, using CO2 as a green catalyst.
Bibliographic record
Abstract
With the demand for greener bio-fuels ever increasing, new methods for their production using environmentally friendly catalysts for their production are gaining interest. This work expands on recent work looking at the use of CO2 as a catalyst in the formation of bio-jet fuel precursors from common carbohydrates. CO2 used as both the catalyst in dehydration of glucose to Hydroxymethyl furfural (5-HMF) and aldol condensation with acetone. This work uses a two-step, one-pot reaction for the conversion of common household materials (“soda pop” and acetone “nail polish remover”) to bio-jet fuel precursors. To show the effectiveness of CO2 as a catalyst in this system and the ease of reaction, this work uses carbonated beverages (“soda pop”), as the source for sugars and catalyst, along with added acetone to produce the bio-jet fuel precursors. These precursors are then hydrodeoxygenated and hydrogenated to produce linear hydrocarbons. This work shows that the precursors for bio-jet fuel can be produced using the CO2 present in “soda pop” as a green catalyst from a common source. Additionally through the use of flow chemistry it is shown that it is possible to produce bio-jet fuels from these precursors using commercially available catalysts. Discipline: Chemistry Faculty Mentor: Dr. Roland Lee
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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 | 0.001 | 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".