Resorcinol–Azodianiline Covalent Organic Framework Supported FeOOH Quantum Dot-Catalyzed Electrochemical Ammonia Synthesis under Ambient Conditions
Bibliographic record
Abstract
Covalent organic frameworks (COFs) represent bulk crystalline organic polymers characterized by their significant surface area and well-organized pores. By carefully selecting building blocks, we can tailor COF structures with strategically placed heteroatoms. This deliberate inclusion enhances their stability, surface area, and guest-binding ability, which make them highly versatile catalyst supports. Importantly, heteroatoms within COF pores under an applied potential can amplify the catalyst’s desired properties. FeOOH, when grown as nanosized QDs, can exhibit semiconducting band gaps, and their amorphous form can be rich in defects, making them catalytically active. Here, we synthesized a heterogeneous electrocatalyst (FeOOH@COF) consisting of FeOOH supported on an imine-linked covalent organic framework (IISERP-COF33) under mild conditions. Electron microscopy, ICP, and XPS validate the homogeneous distribution (13.2 wt %) of nanosized FeOOH quantum dots (QDs) within the COF (average particle size distribution: 2.7 nm). Thus, the Lewis–Bronsted acidity-rich FeOOH accommodated at keto and nitrogen-rich anchoring sites within the COF pores defines the active and recyclable electrocatalyst. This composite system catalyzes ambient condition ammonia production from nitrogen with a yield of 77.4 μg h –1 mg cat –1 and faradaic efficiency of 46.4% at −0.4 V in 0.1 M LiClO 4 aqueous solution, surpassing other COF and iron-based electrocatalysts reported thus far. This work elucidates an accessible aqueous-stable COF-QD electrocatalyst for energy-efficient ammonia production.
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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".