Structural Flexibility of Hydrated RHO Nanosized Zeolite Synthesized via Green Synthesis Approach at Subfreezing Conditions
Bibliographic record
Abstract
Abstract Understanding the structural flexibility of zeolites under cryogenic conditions is essential for optimizing gas separation and storage performance. This study investigates nanosized RHO zeolite synthesized via green synthesis (without organic structural directing agent) upon hydration and cooling to low temperatures (<273 K) using in situ XRPD, in situ FTIR spectroscopy, and DFT simulations. Template‐free synthesis is performed at low temperature (363 K), avoiding calcination or postsynthetic activation, yielding highly crystalline nanosized zeolite with minimal energy consumption and no toxic by‐products. Upon hydration at 300 K, nanosized RHO zeolite adopts a two‐phase expanded‐contracted structure due to distinct water‐cation interactions. Upon cooling to 248 K, the hydrated zeolite transitions into a single expanded phase, remaining stable after reheating to 300 K, forming a metastable state. In situ FTIR analysis indicates freezing‐induced water molecule rearrangement leads to persistent hydrogen‐bonding networks, preventing structural reversion. This metastable state exhibits CO 2 adsorption capacities comparable to conventionally activated RHO zeolite (623 K), achieved through significantly lower energy input. This performance underscores the viability of mild, green chemistry‐aligned activation approaches eliminating energy‐intensive high‐temperature treatments. This novel approach contributes to sustainable separation processes and provides a blueprint for future innovation in porous materials guided by green chemistry principles.
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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) | 0.001 | 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".