Reversible iodine vapor capture using bipyridine-based covalent triazine framework: Experimental and computational investigations
Bibliographic record
Abstract
To address the environmental issues arising from the emission of radiotoxic iodine from nuclear waste streams, developing high-capacity and recyclable adsorbents is urgently demanded. In this study, a nitrogen-rich covalent-triazine framework (CTF-bpy) was synthesized through the ionothermal synthetic method and was used as a reusable adsorbent to capture iodine vapor for sequential cycles. The obtained CTF-bpy adsorbent showed ultrahigh iodine vapor capture capacity of 4.52 g.g−1 at 90 °C and atmospheric pressure, which ranks among the highest values reported to date. CTF-bpy could be simply recycled by washing and heating while preserving above 89.6% of its initial iodine capture capacity after five consecutive cycles, demonstrating its excellent structural stability. Assessment of the adsorption kinetics of the iodine vapor through the fractal-like pseudo-first-order (FL-PFO) kinetic model revealed that the diffusion through micropores was the rate-controlling mechanism. Moreover, the density functional theory (DFT) calculations further demonstrated the significance of the surface's basicity and aromaticity of the structure in efficiently capturing the iodine species. This study may shed light on designing and developing novel adsorbents suitable for solving one of the main environmental issues.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".