Efficient amino-acid-based reactive capture of CO2 via nickel molecular catalyst
Bibliographic record
Abstract
Reactive capture integrates CO2 capture and electrochemical conversion into CO — a key building block in the synthesis of industrial chemicals and fuels — avoiding costly regeneration steps and improving efficiency. Amino acid salt solutions, which offer rapid CO2 capture, facile CO2 release, O2 tolerance, and low toxicity, are promising sorbents for reactive capture. However, we find that amino acids can adsorb to common CO-producing catalysts, covering the active sites and deactivating the catalyst, and that they bind less to nickel phthalocyanine (NiPc). Still, when tested for reactive capture systems — where CO2 supply is inherently limited — NiPc’s performance is constrained by its weak CO2 adsorption and activation. Here we develop a nickel molecular catalyst supported on carbon nanotubes with a conjugated NiPc framework that resists amino acid adsorption and a coordinatively unsaturated Ni-N3 structure that promotes CO2 adsorption and enhances CO selectivity. As a result, we achieve 94% CO Faradaic efficiency at 100 mA cm–2 with an energy efficiency of 42% and an energy cost of 25 GJ tCO–1. Reactive capture bypasses CO2 regeneration, enabling efficient CO production but with low Faradaic efficiency. The authors report a Ni–N3 molecular catalyst that resists amino acid adsorption and promotes efficient CO production in amino-acid systems.
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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".