Sustainable microalgae-based technology for biotransformation of benzalkonium chloride in oil and gas produced water: A laboratory-scale study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many countries have implemented stringent regulatory standards for discharging produced water (PW) from the oil and gas extraction process. Among the different chemical pollutants occurring in PW, surfactants are widely applied in the oil and gas industry to provide a barrier from metal corrosion. However, the release of these substances from the shale formation can pose serious hazardous impacts on the aquatic environment. In this study, a low-cost and eco-friendly microalgae laboratory-scale technology has been tested for biotransformation of benzalkonium chloride (BACC12 and BACC14) in seawater and PW during 14-days of treatment (spiked at 5 mg/L). From the eight microalgae strains selected, Tetraselmis suecica showed the highest removal rates of about 100% and 54% in seawater and PW, respectively. Suspect screening analysis using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allowed the identification of 12 isomeric intermediates generated coming from biotransformation mechanisms. Among them, the intermediate [OH-BACC12] was found as the most intense compound generated from BACC12, while the intermediate [2OH-BACC14] was found as the most intense compound generated from BACC14. The suggested chemical structures demonstrated a high reduction on their amphiphilic properties, and thus, their tendency to be adsorbed into sediments after water discharge. In this study, Tetraselmis suecica was classified as the most successful specie to reduce the surfactant activity of benzalkonium chloride in treated effluents.
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.
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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it