Editorial: Innovative bioconversion of non-food substrates to fuels
Bibliographic record
Abstract
Editorial on the Research Topic Innovative bioconversion of non-food substrates to fuelsDue to the finite quantity of fossil-derived fuels that can be used for energy and chemical production, coupled with the need to reduce greenhouse gas (GHG) emissions, the development and production of biobased fuels and chemicals have attracted the attention of scientists, engineers, and policymakers as a potential strategy for simultaneous maintenance of energy security and the mitigation of GHG emissions in the environment.Bio-derived fuels and chemicals include, but are not limited to, ethanol, butanol, propanol, butanediol, propanediol, lactic acid, succinic acid, acetic acid, butyric acid, hydrogen (H 2 ), and methane (CH 4 ).Food substrates such as corn and sugarcane are two major renewable substrates that are currently used in large quantities to produce biobased fuels and chemicals.These food substrates also serve as feed for livestock and as consumer products, which creates competition and increases food costs.More economical choices are non-food substrates such as lignocellulosic biomass (LB), inedible food wastes, and gases that are generally considered waste, which are currently being considered as potential alternative feedstocks to produce renewable fuels and chemicals.The LB is the most abundant renewable resource on the planet, with a global annual production of 181.5 billion tons (Dahmen et al., 2019) and has great potential as a substrate for fermentation because it is composed of more than 75% fermentable polymeric sugars such as glucose, cellobiose, xylose, arabinose, and mannose.LB includes, but is not limited to, corn stover, wheat straw, rice straw and hulls, sugarcane bagasse, Napier grass, giant reed grass, sweet sorghum, willow, switchgrass, Miscanthus, eucalyptus, Eastern red cedar, sawdust, wood shavings, and forestry residues, and is a significant component of municipal solid waste (MSW).LB, however, must undergo pretreatment and hydrolysis prior to use in the fermentation of fuels and chemicals.Undesirable lignocellulose-derived microbial inhibitory compounds (LDMICs) that inhibit the growth of fermentation microorganisms are generated during the pretreatment and hydrolysis of LB.LDMICs impede the bioconversion of LB hydrolysates (sugars) to fuels and chemicals and must be either removed prior to use as a fermentation feedstock, fermentation medium modified,
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".