Bio-Oil Upgrading: Impact of Phenol on Acetic Acid Esterification with Amberlyst-15
Bibliographic record
Abstract
Bio-oil is a complex organic liquid mixture generally derived from biomass pyrolysis that has potential as a sustainable fuel. Its low energy density, corrosiveness, and low stability, however, limit its use. Upgrading technologies such as the esterification of acids can be used, but the impact of the other bio-oil constituents, including phenolic compounds, on this reaction is not well understood. Thus, this work assessed the effect of phenol on the conversion of acetic acid with methanol over Amberlyst-15. At 80 °C and a methanol:acetic acid ratio of 1.6:1 (w/w), the esterification reaction happens without a catalyst in solution. The conversion was the same with or without phenol, reaching 27% after 4 h. In the presence of the solid-acid catalyst, the conversion increased to 90% over the same time. With the addition of phenol in the range of 0.06 to 0.33 (phenol-to-acetic acid mass ratio), the acid conversion decreased by 8%, while a higher ratio (0.60 w/w) had no impact on the conversion, because this higher concentration of phenol could have accelerated the regeneration of acid sites on the Amberlyst-15. A pseudo-first-order reaction in acetic acid was fit to the data and suggested that the addition of phenol did not alter the reaction mechanism. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) revealed that phenol and acetic acid adsorb on the same sites of the Amberlyst-15 structure, but acetic acid adsorbs more strongly, which explained the decrease in the catalyst performance. X-ray photoelectron spectroscopy (XPS) confirmed catalyst surface modifications due to phenol interference. The results showed the formation of noncovalent interactions between phenol and the vinylbenzene sulfonated structure of Amberlyst-15, as well as H···π interactions under distinct environments. Overall, this study highlighted the inhibitory effects of phenol at concentrations lower than 0.60 w/w on solid-acid catalysts, providing insights for upgrading processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".