Rhamnolipid-assisted mycoremediation of polycyclic aromatic hydrocarbons by<i>Trametes hirsuta</i>coupled with enhanced ligninolytic enzyme production
Bibliographic record
Abstract
The present study deals with the development of a wood assisted fungal system (WAFS) from wood chips using Trametes hirsuta to remove polycyclic aromatic hydrocarbons (PAHs) in BRW. The WAFS exhibited a 1.4-fold higher ligninolytic enzyme production than free fungi in the effluent. Further, to understand PAHs bioremediation by T. hirsuta, biodegradation along with biosorption were studied in model PAHs, phenanthrene (Phe) and benzo (a) pyrene (BaP), in the presence of synthesized rhamnolipids. The WAFS mineralized up to an average of 91.26% Phe and 87.72 % BaP along with biosorption of 12.35% Phe and 18.36 % BaP within 12 days. Thus, the addition of rhamnolipids showed 1.2-fold enhanced biodegradation. However, rhamnolipid concentrations beyond 50 ppm reduced the degradation efficiency of WAFS. Moreover, the degradation capability of total aromatic hydrocarbon (TAH) in biorefinery wastewater by WAFS is 1.8-fold higher than that of free fungi, which confirms the effectiveness of the system.Implications: Simultaneous application of white-rot fungus along with surfactant into a pollutant environment affects the microenvironment of the fungus and reduces the production of their degradative enzymes. In addition, the requirement of periodical supplement of external nutrient in the real-time matrix for the growth of white rot fungi may trigger competitive growth of indigenous microorganisms. Considering this glitch, the current work utilizes the carpenter waste for the strategical develop a wood assisted fungal system to protect the microenvironment of the fungi in the presence of rhamnolipids and contribute to their survival in real time matrix, with enhanced PAHs degradation efficiency.
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 | 0.000 | 0.001 |
| 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 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".