Extraction of crude soybean urease using ethanol and its effect on soil cementation
Bibliographic record
Abstract
One nature-based soil improvement method is the Enzyme Induced Carbonate Precipitation (EICP) approach using the crude soybean urease solution. This study aims to investigate the extraction of soybean urease using different contents of ethanol, and its effect on the cementation of EICP-treated sand. Crude urease enzyme was extracted from the soybean powder using the grinding-extraction method with distilled water containing ethanol. The activity of crude soybean urease extracted with various ethanol contents was studied. Sand columns treated using the crude soybean urease were tested to evaluate the calcium carbonate precipitation and unconfined compressive strength. The test results show that the proposed extraction method using ethanol can produce clearer crude urease solution from soybean powder. The urease activity and turbidity of the extracted urease enzyme solution are highly dependent on the content of ethanol, with the optimal ethanol content of 20%∼30% (v/v) for the preparation of 100 g/L crude soybean urease solution. The extracted relatively clear urease solution contributes to improve the uniformity of calcium carbonate distribution and thus the strength of the EICP-treated sand column.
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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) | 0.000 | 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 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".