Inorganic-Organic Sites Synergy in Trinuclear Metal-Cluster-based COFs for Highly Selective CO2 Electroreduction
Bibliographic record
Abstract
Metal-based covalent organic frameworks (COFs) have recently emerged as promising heterogeneous electrocatalysts for CO2 reduction, yet achieving molecular-level control over product selectivity remains challenging. To address this limitation, we report the synthesis of two trinuclear metal-cluster-based mesoporous COFs-AgTrzPh and CuTrzPh prepared under ambient and scalable reaction conditions. These materials represent the first examples of COFs in-corporating atomically precise trinuclear metal cluster nodes. Notably, the two frameworks exhibit distinct CO2 elec-troreduction behaviors dictated by the intrinsic electronic characteristics of their metal centers. AgTrzPh achieves ex-ceptionally high CO selectivity (92%), whereas CuTrzPh generates a mixed product distribution with 41% CO and 35% HCOOH, marking the highest CO and HCOOH selectivity reported to date for pristine COF catalysts. The superior catalytic response originates from the synergistic interplay between the inorganic metal-cluster nodes and the imine-linked organic framework, where both contribute as active sites while supporting extended π-electron transport. Complementary theoretical investigations corroborate the experimental observations, revealing energetically favora-ble pathways and identifying dual catalytic sites responsible for controlling product formation. Overall, this work demonstrates a strong structure–activity relationship and highlights metal-cluster-integrated COFs as a highly tuna-ble platform for selective CO₂ electroreduction.
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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".