Low‐Overpotential Electrocatalytic Water Splitting with Noble‐Metal‐Free Nanoparticles Supported in a sp<sup>3</sup> N‐Rich Flexible COF
Bibliographic record
Abstract
Covalent organic frameworks (COFs) are crystalline organic polymers with tunable structures. Here, a COF is prepared using building units with highly flexible tetrahedral sp 3 nitrogens. This flexibility gives rise to structural changes which generate mesopores capable of confining very small (<2 nm sized) non‐noble‐metal‐based nanoparticles (NPs). This nanocomposite shows exceptional activity toward the oxygen‐evolution reaction from alkaline water with an overpotential of 258 mV at a current density of 10 mA cm −2 . The overpotential observed in the COF‐nanoparticle system is the best in class, and is close to the current record of ≈200 mV for any noble‐metal‐free electrocatalytic water splitting system—the Fe–Co–Ni metal‐oxide‐film system. Also, it possesses outstanding kinetics (Tafel slope of 38.9 mV dec −1 ) for the reaction. The COF is able to stabilize such small‐sized NP in the absence of any capping agent because of the COF–Ni(OH) 2 interactions arising from the N‐rich backbone of the COF. Density‐functional‐theory modeling of the interaction between the hexagonal Ni(OH) 2 nanosheets and the COF shows that in the most favorable configuration the Ni(OH) 2 nanosheets are sandwiched between the sp 3 nitrogens of the adjacent COF layers and this can be crucial to maximizing their synergistic interactions.
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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".