Rational Design of Coordination Bond Connected Metal Organic Frameworks/MXene Hybrids for Efficient Solar Water Splitting
Bibliographic record
Abstract
Abstract Metal organic frameworks (MOFs) have attracted great interest in photocatalysis, but their activity is hampered by the issue of severe carrier recombination. Here, through a carboxyl group assisted coordination route, MXene decorated with carboxyl groups provides chelation sites enabling coordination with UiO‐66‐NH2(Zr/Ti) (UZT) to fabricate a tightly connected UiO‐66‐NH2(Zr/Ti)/carboxyl‐functionalized MXene (UZT/CFMX) heterostructure. This is the first instance of direct chemical bonding of MOFs‐involved heterostructure via a coordination bond. The critical role of decorated carboxyl groups can be determined so that 1) these can help to establish a strong coordination bond between two materials; 2) act as bridge to promote the electrons transfer from MOFs to MXene, thus relieving carrier recombination, and 3) most interestingly, the carbon atom on the carboxyl group forms a bond with the oxygen from water stimulating the water to dissociate into OH* and H*, thus adding additional reaction pathways for promoting photocatalytic water splitting. Accordingly, the resulting UZT/CFMX shows efficient solar‐driven photocatalytic performance for water splitting. The H2 evolution rate is as high as 2187 µmol g−1 h−1, 20 times higher than that of UZT and 4 times higher than that of UiO‐66‐NH2 (Zr/Ti)/MXene (UZT/MX), also surpassing the majority of reported MOF‐based photocatalysts.
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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".