Advancements in adsorption and membrane technologies for hydrogen isotope separation: Exploring new materials and emerging techniques
Bibliographic record
Abstract
Separating hydrogen isotopes, such as deuterium (D) and tritium (T), is one of the most demanding challenges in modern separation technology. These isotopes are critical for industrial applications, and achieving high-purity separation is of significant economic value. However, due to their similar properties, traditional methods such as cryogenic distillation (CD) and girdler sulfide (GS) are energy-intensive, costly, and offer limited efficiency. Emerging techniques using porous materials have shown some improvement through kinetic quantum sieving (KQS) and chemical affinity quantum sieving (CAQS). However, these methods still face challenges such as limited selectivity, scalability issues, and the need for precise control over material properties. This review critically examines the development and application of adsorbents and membranes for hydrogen isotope separation, focusing on quantum sieving (QS) effects in materials such as metal–organic frameworks (MOFs) and zeolites. It also compares these approaches with conventional methods. Special emphasis is placed on membranes, which provide a continuous, cost-effective, and scalable solution. The review covers metallic and non-metallic membranes, including advanced 2D materials like graphene oxide (GO). Future research opportunities and practical applications are discussed to support the ongoing development of this promising technology
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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 | 0.001 | 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".