Directly‐Deposited Ultrathin Solid Polymer Electrolyte for Enhanced CO<sub>2</sub> Electrolysis
Bibliographic record
Abstract
Abstract The economic viability of carbon dioxide electroreduction (CO2R) relies on improved performance accompanied by scalable system design. Membranes are commonly used for the separation of reduction and oxidation products as well as to provide a suitable micro‐environment for CO2R. Commercial membranes often address only one of the key challenges in CO2R: either they offer a suitable micro‐environment for CO2R (e.g., anion exchange membrane) or suppress carbonate cross‐over (e.g., cation exchange membrane and bipolar membrane). This work presents a cation‐infused ultrathin (≈3 µm) solid polymer electrolyte (CISPE) that concomitantly addresses both challenges via a bidirectional ion transport mechanism and suppressed cathode flooding. This directly‐deposited CISPE (that substitutes the commonly used pre‐made membrane) enables record high full‐cell energy efficiency of 28% at 100 mA cm−2 for one‐step CO2 electrolysis to ethylene (C2H4) with ≈110 h of stable operation. This translates into a record low energy cost of 290 GJ per ton C2H4 for the end‐to‐end process (i.e., CO2 capture and electroreduction, carbonate regeneration, CO2 separation from anode and cathode streams) in a membrane electrode assembly CO2R. The present work offers a versatile design paradigm for functional polymer electrolytes, opening the door to stable, and efficient electrolysis of high‐value feedstock chemicals and fuels using low‐cost catalysts.
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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 | 0.001 |
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".