Selective reductive conversion of CO2 to CH2-bridged compounds by using a Fe-functionalized graphene oxide-based catalyst
Bibliographic record
Abstract
Anthropogenic climate change drastically affects our planet, with CO2 being the most critical gaseous driver. Despite the existing carbon dioxide capture and transformation, there is much need for innovative carbon dioxide hydrogenation catalysts with excellent selectivity. Here, we present a fast, effective, and sustainable route for coupling diverse alcohols, amines and amides with CO2 via heterogenization of a natural metal-based homogeneous catalyst through decorating on functionalized graphene oxide (GO). Combined synthetic, experimental, and theoretical studies unravel mechanistic routes to convergent 4‑electron reduction of CO2 under mild conditions. We successfully replace the toxic and expensive ruthenium species with inexpensive, ubiquitously available and recyclable iron. This iron-based functionalized graphene oxide (denoted as Fe@GO-EDA, where EDA represents ethylenediamine) functions as an efficient catalyst for the selective conversion of CO2 into a formaldehyde oxidation level, thus opening the door for interesting molecular structures using CO2 as a C1 source. Overall, this work describes an intriguing heterogeneous platform for the selective synthesis of valuable methylene-bridged compounds via 4‑electron reduction of CO2. Carbon dioxide in the atmosphere can be captured and transformed to useful chemicals with hydrogenation catalysts. Here, iron-functionalized graphene oxide-based catalyst functions as an effective catalyst for the selective conversion of carbon dioxide into a formaldehyde oxidation level.
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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".