Understanding electrochemically induced olefin complexation: towards electrochemical olefin–paraffin separations
Bibliographic record
Abstract
Olefin-paraffin separation is a critical yet energy-intensive process in the chemical industry, accounting for over 250 trillion BTU/year of global energy consumption. This work explores the use of redox-active nickel maleonitriledithiolate complex for olefin-paraffin separations. Key performance factors, namely the electrochemical oxidation of the complex and the olefin capture utilization fraction, were systematically quantified. Electrochemical studies revealed near-complete oxidation of Ni(II) to Ni(IV) species, suggesting that the electrochemical oxidation step is not a limiting factor in olefin capture. The utilization fraction was found to be strongly dependent on the complexation equilibrium behavior between olefin-bound and unbound state of the complex. Time-resolved kinetic measurements unveiled a sluggish complexation rate, requiring over 36 hours to approach equilibrium. These insights highlight the importance of driving the complexation equilibrium and improving the kinetics to enhance the performance of Ni-based electrochemical swing absorbers for energy-efficient olefin-paraffin separations. The findings lay the groundwork for future optimization strategies and industrial implementation of this sustainable separation technology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".