Nitrogen-doped nanoporous graphene induced by a multiple confinement strategy for membrane separation of rare earth
Bibliographic record
Abstract
Rare earth separation is still a major challenge in membrane science. Nitrogen-doped nanoporous graphene (NDNG) is a promising material for membrane separation, but it has not yet been tested for rare earth separation, and it is limited by multi-complex synthesis. Herein, we developed a one-step, facile, and scalable approach to synthesize NDNG with tunable pore size and controlled nitrogen content using confinement combustion. Nanoporous hydrotalcite from Zn(NO 3 ) 2 is formed between layers of graphene oxide (GO) absorbed with phenylalanine via confinement growth, thus preparing the sandwich hydrotalcite/phenylalanine/GO composites. Subsequently, area-confinement combustion of hydrotalcite nanopores is used to etch graphene nanopores, and the hydrotalcite interlayer as a closed flat nanoreactor induces two-dimensional space confinement doping of planar nitrogen into graphene. The membrane prepared by NDNG achieves separation of Sc 3+ from the other rare earth ions with excellent selectivity (∼3.7) through selective electrostatic interactions of pyrrolic-N, and separation selectivity of ∼1.7 for Tm 3+ /Sm 3+ .
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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".