Tuning the Polarity of Dinitrile-Based Electrolyte Solutions for CO<sub>2</sub> Electroreduction on Copper and Copper-Based Catalysts
Bibliographic record
Abstract
Accumulating carbon dioxide (CO2) concentrations in the atmosphere due to anthropogenic sources has become an increasing threat to the environment because of the resulting effects of global warming. It has therefore become imperative to develop and utilize alternative sustainable sources for fuels and feedstock chemicals. CO2 electrochemical reduction (CO2ER) has been identified as a promising process which can convert CO2 to added value chemicals, such as hydrocarbons. In combination with the use of renewable energy resources, the technology has potential to be a net zero carbon process. Commonly, aqueous electrolytes are used for the reaction such as bicarbonate or phosphate-buffered saline solutions. However, these exhibit low CO2 solubilities, which hinders the performance of the reaction by limiting mass transfer. Organic solvent electrolytes possess relatively higher CO2 solubilities. These are also able to suppress the competing hydrogen evolution reaction (HER) due to lowered proton concentrations. Organic nitrile-solvent-based electrolytes, more commonly studied for Li-ion batteries, are known to possess high thermal and electrochemical stability. Furthermore, the polarity and CO2 miscibility can be tuned in dinitrile solvents by variation of the chain length. As such, the effect of chain length was explored on organic dinitrile-based solvent electrolytes for the application of CO2 electroreduction. The selected dinitrile solvents were Adiponitrile (n=4), Suberonitrile (n=6), and Sebaconitrile (n=8), where n indicates the number of methylene groups in the chain. These dinitriles were also compared to Acetonitrile, a commonly used organic mononitrile solvent. Conductivity was first studied as a function of salt concentration and temperature for all solvents. Then, CO2 solubility was evaluated for the different solvents. Finally, electrochemical tests utilizing copper and copper-based bimetallic catalysts were conducted. The catalysts explored consisted of synthesized copper (I) oxide (Cu2O) and copper-silver (CuAg) nanoparticles, obtained through a chemical reduction method in water using sodium borohydride (NaBH4) at ambient conditions. These particles were evaluated against commercial Cu2O and CuAg nanoparticle counterparts. The Cu-based nanoparticles were characterized using XRD, TEM, EDX, and EELS techniques. The obtained results are summarized and discussed.
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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 | 0.001 |
| 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 | 0.000 | 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".