Supercritical water gasification of biomass in diamond anvil cells and fluidized beds
Bibliographic record
Abstract
Abstract With the shift in interest towards renewable energy, hydrogen as an alternative gaseous fuel seems to attract much attention. Waste biomass is an ideal option for the synthesis of biofuels due to its abundance and no net CO2 emissions. Hydrogen can be produced through supercritical water gasification of waste biomass. Hydrogen is an attractive energy carrier which can be used as a direct fuel or in fuel cells to generate electricity and in other energy‐producing processes. Gasification of biomass in supercritical water can be performed for hydrogen generation in both batch and continuous modes with/without the application of catalysts. In spite of the progress made in various gasification technologies, diamond anvil cells and fluidized beds as the new‐generation batch and continuous reactors, respectively, are not fully recognized. The current review is focused on understanding the design, application and limitations of these two new reactor configurations to help motivate their wide‐scale utilization. The review also discusses the potential of diamond anvil cells in studying the involved chemical reactions, thermodynamics, and phase behavior of biomass components during gasification. Nonetheless, the caliber of fluidized beds in continuously gasifying biomass for hydrogen production in supercritical water is also documented. © 2014 Society of Chemical Industry and John Wiley & Sons, Ltd
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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".