Optimization of reverse osmosis flowback water treatment using halotolerant microbes naturally enriched in fractured shales
Bibliographic record
Abstract
Flowback water recovered after hydraulic fracturing operations poses a serious environmental concern due to the sheer quantity produced and its toxic chemical composition. Traditional methods of wastewater treatment cannot be used for flowback water treatment due to its high concentration of non-biodegradable dissolved solids. Consequently, alternative technology has been developed to address this problem. Reverse osmosis (RO) treatment is one such example. However, guar gum gelling agents found in flowback water impede membrane permeability and water flux rate of RO, consequently decreasing the efficiency and practicality of this desirable, environment-friendly technology. Previously, a biological solution using activated sludge to degrade guar gum prior to RO treatment was attempted with limited success due to the inhibitory effects of hypersalinity (characterized by high total dissolved solids content) on microbial activity. To solve this problem, several recently discovered strains of bacteria and archaea found to be naturally enriched in fractured shales may be utilized through genetic modification to degrade guar gum under hypersaline conditions. These microbes are naturally halotolerant and thrive under hypersaline conditions, making them prime targets for genetic modification targeting various chemical additives in flowback water. Here, we provide a proof of concept model using these microbes to selectively target guar gum degradation to improve the efficiency of RO treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".