Probing Structural Transformations and Degradation Mechanisms by Direct Observation in SIFSIX-3-Ni for Direct Air Capture
Bibliographic record
Abstract
Despite global efforts to reduce carbon dioxide (CO 2 ) emissions, continued industrialization threatens to exacerbate climate change. This work investigates methods to capture CO 2, with a focus on the SIFSIX-3-Ni metal–organic framework (MOF) as a direct air capture (DAC) sorbent. SIFSIX-3-Ni exhibits promising CO 2 adsorption properties but suffers from degradation processes under accelerated aging, which are akin to column regeneration conditions. Herein, we have grown the largest SIFSIX-3-Ni single crystals to date, facilitating single crystal X-ray diffraction analyses that enabled direct observation of the H 2 O and CO 2 dynamics through adsorption and desorption. In addition, a novel space group ( I 4/ mcm ) for the SIFSIX-3-Ni is identified, which provided insights into structural transitions within the framework and elucidated water’s role in degrading CO 2 uptake performance as the material ages. In situ X-ray scattering methods revealed long-range and local structural transformations associated with CO 2 adsorption in the framework pores as well as a temperature-dependent desorption mechanism. Pair distribution function analysis revealed a partial decomposition to form nonporous single-layer nanosheets of edge-sharing nickel oxide octahedra upon aging. The formation of these nanosheets is irreversible and reduces the amount of active material for the CO 2 sorption. These findings provide crucial insights for the development of efficient and stable DAC sorbents, effectively reducing greenhouse gases, and suggest avenues for enhancing MOF stability under practical DAC conditions.
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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".