Thermodynamic, spectroscopic, and molecular characterization of rice straw biomass for use as biofuel feedstock
Bibliographic record
Abstract
Abstract India is one of the leading producers of rice in the world. Gasification and pyrolysis are two thermochemical processes. In the gasification process, biomass is transformed into syngas, which serves as an energy source. This conversion occurs under high temperatures with a carefully regulated and restricted air supply. On the other hand, pyrolysis, which transpires at lower temperatures without the presence of air, generates pyrolysis oil as a by‐product. This oil can be further refined into liquid fuels. For the purpose of investigating the feasibility of biofuel production, the current study involved the characterization of rice straw biomass using various techniques such as thermogravimetry differential thermal analysis (TG/DTA), Fourier transform infrared (FTIR), carbon, hydrogen, nitrogen, sulphur and oxygen (CHNS/O), inductively coupled plasma optical emission spectroscopy (ICP‐OES), among others. These analytical methods were employed to assess the potential of rice straw biomass for the production of biofuels. The existence of a significant amount of cellulose (32.1%), volatiles (approximately 67.06%), and high heating value (HHV) (13.18 MJKg −1 ) in rice straw inferred their capability to be used as feedstocks in the production of biofuel. The activation energy of approximately 173.20 KJ/Mol (Flynn Wall Ozawa [FWO]) indicated the viability of the burning process. From master plot ( Z ( α )) analysis, the experimental curve was seen passing through different theoretical curves, indicating the complex nature of the pyrolysis process for rice straw.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".