Chemicals from agricultural biomass: chemoenzymatic approach for production of vinylphenols and polyvinylphenols from phenolic acids
Bibliographic record
Abstract
Abstract A two-step chemoenzymatic process for the preparation of polyvinylphenols from phenolic acids (PAs), being abundant aromatic constituents found in agricultural biomass, was developed. In the first step, conversion of 4-hydroxycinnamic acid derivatives to the corresponding vinylphenols, mediated by a recombinant phenolic acid decarboxylase, was evaluated using a variety of bioprocessing technologies that include biphasic whole cell and cell free extract biotransformations, a combination of biocatalyst with adsorbent resins for in situ product recovery, and fixed bed reactors using immobilized whole cells. The best yield (90%) with a high space time yield of 4.83 g/l/h was the result of a combination of crude enzyme extracts of the recombinant Escherichia coli ( E. coli ) with water immiscible organic solvents such as toluene. In the second step, cationic and radical polymerizations were tested to produce polyvinylguaiacol (PVG) from vinyl phenols. Characterization of PVG by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and nanoindentation test are reported here for the first time. The feasibility of the chemoenzymatic process for the production of aromatic polymers from biomass was demonstrated by the production of polymers from a mixture of ferulic acid (FA) and p -coumaric acid ( p CA), obtained from alkaline hydrolysis of corn bran. Interestingly, nanoindentation tests showed that both PVG and “mixed” PVG polymers showed significantly higher performances than a commercial polystyrene polymer.
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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 | 0.000 | 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 | 0.001 |
| 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".