Opportunities and challenges for n-alkane and n-alkene biosynthesis: A sustainable microbial biorefinery
Bibliographic record
Abstract
Alkanes and alkenes are high-value platform chemicals that can be synthesized by microorganisms, utilizing organic residues from agri-food industries and municipalities, thereby offering an alternative opportunity in resource recovery. Current research and technological advancements for the biosynthesis of alkanes and alkenes are mainly impeded by low product titers, obstructing the bioprocess upscaling and large-scale applications. Thus, current scientific investigations aim to improve productivity by utilizing natural and engineered metabolic pathways in various microbial chassis to suppress competing metabolic pathways, coupled with bioprocess optimization. Additionally, to reduce costs, research is being conducted on utilizing inorganic carbon sources such as CO2 to promote the green synthesis of alkanes and alkenes. Therefore, this review critically discusses the opportunities and challenges in alkane and alkene biosynthesis, aiming to examine the current technological advancements. In this review, the limitations of five major metabolic pathways for alkane and alkene biosynthesis are thoroughly discussed, highlighting their shortcomings. Additionally, various techniques, including metabolic engineering, autotrophic metabolic pathways, and new non-biosynthetic routes, are investigated as potential methods to enhance product titers. Furthermore, this review offers valuable insights into the economic and environmental aspects of alkane and alkene biosynthesis while also presenting perspectives for future research directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".