Oleic-acid functionalized mesoporous silica nanoparticles with a hydroxyapatite core enhanced growth of the hydrocarbon degrader Dietzia maris at oil–water interfaces
Bibliographic record
Abstract
Rapid biodegradation of poorly water-soluble hydrocarbons as nonaqueous (oil) phases in contaminated aquatic environments is enabled by attachment of hydrocarbon-degrading bacteria to the oil–water interface. Herein, we report the synthesis of nanoparticles comprising a hydroxyapatite (Ca 5 (PO 4 ) 3 (OH)) core encapsulated in a mesoporous silica shell and surface-modified with oleic acid (OA–nHAP@MSN) for targeted binding at the oil–water interface and to supply P to bacteria at the interface. P is an essential and often limiting nutrient for bacteria in hydrocarbon-contaminated environments. In microcosm experiments, where the hydrocarbon-degrading bacteria, Dietzia maris strain NWWC4, and OA–nHAP@MSN were inoculated in mineral media in contact with pure liquid hexadecane (sole C source), there was 419.6–fold growth at the hexadecane-water interface, compared to 31.2-fold in identical, but NP-free microcosms. The continuous release of P from the hydroxyapatite core in OA–nHAP@MSN to water was demonstrated in separate experiments in well mixed batch systems and was found to be pH-sensitive. Environmental Scanning Electron Microscopy showed significantly larger cell aggregates and dense biofilms in the OA–nHAP@MSN–amended systems, compared to NP-free systems. Our results demonstrate a strategy for enhancing oil–spill bioremediation using NPs targeting nutrient supply to hydrocarbon-degrading bacteria at oil-water interfaces.
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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".