Graphene Oxide Membranes for Isotopic Water Mixture Filtration: Preparation, Physicochemical Characterization, and Performance Assessment
Bibliographic record
Abstract
There is an increasing demand for nuclear reactors, driven by the global need for low CO2 producing energy sources. The use of light (H2O) or heavy water (D2O) in a nuclear reactor environment produces radioactive tritiated heavy (HTO, DTO) water as an inevitable contaminant. Considering the need for tritiated water removal and also the high commercial value of purified water isotopes, technologies that can efficiently separate isotopic mixtures of water in nuclear reactors are highly desirable. This study presents an experimental approach for producing graphene oxide (GO) membranes and assessing their performance in the filtration of isotopic water mixtures. Specifically, using D2O/H2O mixtures as model systems, we investigate the effect of physicochemical properties of GO, as well as membrane preparation conditions on membrane filtration efficiency. We find that membranes assembled using larger GO platelets of lower oxidation level generally exhibit higher deuterated water (HDO, D2O) rejection and filtrate flux. Moreover, membrane preparation conditions have a strong impact on the interlayer space between stacked GO nanoplatelets in the membrane, hence as a direct effect on filtration performance. Our experimental results also show a strong, nonmonotonic dependence of separation performance on operating temperature, as well as the existence of local temperature optima. Our work provides guidelines for simple and scalable preparation of GO membranes with very good mechanical stability, capable of achieving efficient separation of isotopic water.
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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 | 0.000 | 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 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".