Aminopentol, a possible novel biomarker tracer for methane hydrate stability in sedimentary records
Bibliographic record
Abstract
Microbially produced gamma-polyglutamic acid (gamma-PGA) is a commercially important biopolymer with many applications in biopharmaceutical, food, cosmetic and waste-water treatment industries. Owing to its increasing demand in various industries, production of gamma-PGA is well documented in the literature, however very few methods have been reported for its recovery. In this paper, we report a novel method for the selective recovery and purification of gamma-PGA from cell-free fermentation broth of Bacillus licheniformis. The cell-free fermentation broth was treated with divalent copper ions, resulting in the precipitation of gamma-PGA, which was collected as a pellet by centrifugation. The pellet was resolubilized and dialyzed against de-ionized water to obtain the purified gamma-PGA biopolymer. The efficiency and selectivity of gamma-PGA recovery was compared with ethanol precipitation method. We found that 85% of the original gamma-PGA content in the broth was recovered by copper sulfate-induced precipitation, compared to 82% recovery by ethanol precipitation method. Since ethanol is a commonly used solvent for protein precipitation, the purity of gamma-PGA precipitate was analyzed by measuring proteins that co-precipitated with gamma-PGA. Of the total proteins present in the broth, 48% proteins were found to be co-precipitated with gamma-PGA by ethanol precipitation, whereas in copper sulfate-induced precipitation, only 3% of proteins were detected in the final purified gamma-PGA, suggesting that copper sulfate-induced precipitation offers better selectivity than ethanol precipitation method. Total metal content analysis of the purified gamma-PGA revealed the undetectable amount of copper ions, whereas other metal ions detected were in low concentration range. The purified gamma-PGA was characterized using infrared spectroscopy.
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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 | 0.001 | 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) | 0.004 | 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".