Understanding and enhancing the phase stability of fast pyrolysis oils through ternary phase diagrams
Bibliographic record
Abstract
• FPO phase stability is determined by the fractions of PL, MS, and water. • Phase separations consistently occurred at 25 °C when PL content exceeded 56 wt%. • Phase separations consistently occurred at 25 °C when MS content was below 28 wt%. • Solvents with more –OH groups were less effective in enhancing stability. • For mono-alcohols, a lower ratio of –OH groups was more favorable. Fast pyrolysis oils (FPOs), derived from various biomass feedstocks, hold promise as carbon–neutral fuels. However, their industrial use is hindered due to phase separations during storage, transportation, and co-processing, with limited understanding of the root causes that drive this phenomenon. This study aims to elucidate the kinetic stability of FPO by developing ternary phase diagrams and exploring the effects of the temperature and addition of organic solvents on the aging. The phase stability of FPO was found to be determined by the fraction of pyrolytic lignin (PL), mixed solvent (MS), and water. Even at 25 °C, phase separations could consistently occur when the PL content exceeded 56 wt% or the MS content fell below 28 wt%. Heating the FPO to 50 °C–80 °C led to a decrease in the water-soluble content and an increase in the water-insoluble content, while the water content remained relatively unchanged, indicating the transition of the water-soluble content to the water-insoluble content was closely related to FPO phase separation during aging at elevated temperatures. Moreover, the solvents could slow the aging of the FPO in the order of 2-propanol > ethanol > methanol > ethylene glycol > glycerol. Thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR-spectroscopy) analysis further revealed changes in both the chemical compositions and polymerizations during aging, in which alcohols were identified as effective inhibiters to slow the aging. These new findings provide promising pathways to controlling the FPO phase stability through solvent selections and will advance the adoptions of FPOs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".