Sequential hydrothermal gasification of biomass to hydrogen
Bibliographic record
Abstract
Abstract A new technology, in which a renewable biomass is used to produce hydrogen fuel, is described. This hydrogen can be used as a feed for fuel cells to generate electricity or in other energy-producing processes. Degradation and gasification of cellulose-based biomass in compressed water was studied in the 100–400 °C temperature range. Phase behavior of the cellulose in subcritical water was studied in a diamond-anvil cell, coupled with optical microscopy, at heating rates of 1 and 5 °C/s. Homogeneous conditions of a single water-cellulose phase were established. Complete dissolution of the cellulose was achieved at 333 °C. The evolution mechanism based on a rapid hydrolysis of the cellulose to oligomers and glucose is suggested. Glucose was then used as a model compound to characterize the chemistry of biomass gasification. A 0.1-M glucose solution was fed into a continuous-flow reactor at a pressure of 100 bar using an HPLC pump. Catalytic effects of Pt/Al 2 O 3 on the gasification temperature were determined. Gas product composition was analyzed using online GC-TCD. A mixture of H 2 , CO 2 , and CH 4 gas was produced. Quantitative analysis of the total organic carbon in the liquid residue indicated 67% carbon gasification efficiency at 330 °C. Qualitative analyses of liquid residues showed that the main decomposition products in the liquid phase were alcohols and carboxylic acids. It was shown that the hydrogen fuel could be efficiently generated from biomass.
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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 | 0.000 | 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) | 0.001 | 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 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".