Bacterial cellulose-graphene oxide composite membranes with enhanced fouling resistance for bio-effluents management
Bibliographic record
Abstract
Bacterial cellulose composites hold promise as renewable bioinspired materials for industrial and environmental applications. However, their use as free-standing water filtration membranes is hindered by low compressive strength, fouling, and poor contaminant selectivity. This study investigates the potential of bacterial cellulose-graphene oxide composites membranes for fouling resistance in pressure-driven filtration. Graphene oxide dispersed in poly(ethylene glycol) (PEG-400) is incorporated as a reinforcing filler into 3D network of bacterial cellulose using an in-situ synthesis method. The effect of graphene oxide on in situ fermentation yield and the formation of percolated-network in the composites shows that the optimal membrane properties are reached at a graphene oxide loading of 2 mg/mL. The two-dimensional graphene oxide nanosheets uniformly dispersed into the matrix of bacterial cellulose nanofibers via hydrogen-bonded interactions demonstrated nearly twofold higher water flux (380 L m −2 h −1 ) with a molecular weight cut-off ranging between 100–200 KDa and a sixfold increase in wet compression strength than pristine BC. When exposed to synthetic organic foulants and bacterial rich feed solutions, the composite membranes showed more than 95% flux recovery. Additionally, the membranes achieved over 95% rejection of synthetic natural organic matter and bacterial rich solutions, showcasing their enhanced fouling resistance and selectivity.
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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".