Abiotic sugar synthesis from CO2 electrolysis
Bibliographic record
Abstract
CO2 valorization is aimed at converting waste CO2 to value-added products. While steady progress has been achieved through diverse catalytic strategies, including CO2 electrosynthesis, CO2 thermocatalysis, and biological CO2 fixation, each of these approaches have distinct limitations. Inorganic catalysts only enable synthesis beyond C2 and C3 products with poor selectivity and with a high energy requirement. Meanwhile, although biological organisms can selectively produce complex products from CO2, their slow autotrophic metabolism limits their industrial feasibility. Here, we present an abiotic approach leveraging electrochemical and thermochemical catalysis to complete the conversion of CO2 to life-sustaining carbohydrate sugars akin to photosynthesis. CO2 was electrochemically converted to glycolaldehyde and formaldehyde using copper nanoparticles and boron-doped diamond cathodes, respectively. CO2-derived glycolaldehyde then served as the key autocatalyst for the formose reaction, where glycolaldehyde and formaldehyde combined in the presence of an alkaline earth metal catalyst to form a variety of C4 - C8 sugars, including glucose. In turn, these sugars were used as a feedstock for fast-growing and genetically modifiable Escherichia coli. Altogether, we have assembled a platform that pushes the boundaries of product complexity achievable from CO2 conversion while demonstrating CO2 integration into life-sustaining sugars.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".