An end-to-end microbial platform for 100% bio-based long-chain polyester: From renewable substrate to eco-friendly polymer
Bibliographic record
Abstract
The development of sustainable, eco-friendly polyesters from renewable resources is crucial for reducing dependence on petroleum-based plastics. However, despite advances in microbial production of bioplastics, significant challenges remain in achieving high conversion efficiency and scalability for industrial applications. This study is the first to report the synthesis of a 100% bio-based polyester using both 1,12-dodecanedioic acid (1,12-diacid) and 1,12-dodecanediol (1,12-diol) via a two-step microbial bioconversion from a single plant oil-derived alkane. An engineered Candida tropicalis strain produced 150 g/L of 1,12-diacid with a productivity of 1.53 g/(L·h) in a 5 L fed-batch system using a two-phase biotransformation strategy. Escherichia coli engineered to express carboxylic acid reductase, which reduces carboxylic acids to aldehydes, and its activation enzyme phosphopantetheinyl transferase, converted 1,12-diacid into 68 g/L 1,12-diol with a productivity of 1.42 g/(L·h) in a 5 L fed-batch system, representing high titer and productivity for microbial production of long-chain α,ω-diols. Both monomer production processes were successfully scaled up to a 50 L pilot fermenter, validating their potential for industrial implementation. A highly efficient downstream purification process was developed, achieving > 98% purity and recovery rates for both monomers. The bio-derived monomers enabled the synthesis of polyesters with molecular weight and thermal characteristics similar to petroleum-based monomers of the same chemical structure. This integrated approach establishes a robust and scalable microbial platform that converts renewable lipid feedstocks into fully bio-based polyesters, thereby demonstrating an environmentally sustainable and industrially viable route to circular bioeconomy-based polyester production.
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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".