In vitro plant tissue culture as the fifth generation of bioenergy
Bibliographic record
Abstract
Developing and applying a novel and sustainable energy crop is essential to reach an efficient and economically feasible technology for bioenergy production. In this study, plant tissue culture, also referred to as in vitro culture, is introduced as one of the most promising and environmentally friendly methods for the sustainable supply of biofuels. The current study investigates the potential of in vitro -grown industrial hemp calli obtained from leaf, root, and stem explants as a new generation of energy crop. For this purpose, the in vitro grown explants were first fully characterized in terms of elemental and chemical composition. Secondly, HTL experiments were designed by Design Expert 11 with a particular focus on biocrude. Finally, the chemical components, functional groups, and petroleum-like hydrocarbons present in the biocrude were identified by PY-GCMS. A 22.61 wt.% biocrude was produced for the sample grown through callogenesis of the leaf (CL). The obtained biocrude for CL consisted of 19.55% acids, 0.42% N compounds, 15.44% ketones, 16.03% aldehydes, 2.21% furans, 20.01% aromatics, 5.2% alcohols, and 19.88% hydrocarbons. To the best of the authors' knowledge, this is the first report that in vitro -grown biomass is hydrothermally liquefied toward biocrude production; the current work paves the way for integrating plant tissue culture and thermochemical processes for the generation of biofuels and value-added chemicals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".