Space‐Confined Metal Ion Strategy for Carbon Materials Derived from Cobalt Benzimidazole Frameworks with High Desalination Performance in Simulated Seawater
Bibliographic record
Abstract
Abstract Various metal ions with different valence states (Mg2+, Al3+, Ca2+, Ti4+, Mn2+, Fe3+, Ni2+, Zn2+, Pb2+, Ba2+, Ce4+) are successfully confined in quasi‐microcube shaped cobalt benzimidazole frameworks using a space‐confined synthesis strategy. More importantly, a series of derived carbon materials that confine metal ions are obtained by high‐temperature pyrolysis. Interestingly, the derived carbon materials exhibited electric double‐layer and pseudocapacitance properties because of the presence of metal ions with various valence states. Moreover, the presence of additional metal ions within carbon materials may create new phases, which can accelerate Na+ insertion/extraction and thus increase electrochemical adsorption. Density functional theory results showed that carbon materials in which Ti ions are confined exhibit enhanced insertion/extraction of Na+ resulting from the presence of the characteristic anatase crystalline phases of TiO2. The Ti‐containing materials have an impressive desalination capacity (62.8 mg g−1) in capacitive deionization (CDI) applications with high cycling stability. This work provides a facile synthetic strategy for the confinement of metal ions in metal–organic frameworks and thus supports the further development of derived carbon materials for seawater desalination by CDI.
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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".