Structure‐dependent nitrogen transformation in hydrothermal liquefaction of amino acids
Bibliographic record
Abstract
Abstract Hydrothermal liquefaction (HTL) offers a promising route for converting wet biomass into biocrude, yet nitrogen‐rich feedstocks like microalgae pose challenges due to nitrogen contamination. This study investigates how the structure of model amino acids—leucine (neutral), arginine (basic), and aspartic acid (acidic)—affects nitrogen migration during HTL. Nitrogen partitioning across product phases was quantified via orthogonal experiments and GC–MS, when degradation mechanisms were elucidated via Density Functional Theory (DFT) simulations. As shown by the experimental results, leucine achieved the highest biocrude yield (31.61%) with nitrogen retained as amides and diketopiperazines. Arginine yielded minimal oil (7.94%) and favored aqueous nitrogen‐heterocycles, while aspartic acid (29.53%) released nitrogen mainly into aqueous and gaseous phases via decarboxylation. DFT confirmed leucine follows two competing routes: DKP formation and oxidative cleavage yielding 2‐pyrrolidone. These findings reveal a structure‐reactivity‐distribution relationship linking amino acid functionality to nitrogen fate, offering guidance for feedstock selection and HTL optimization to enhance biocrude quality.
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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".