Paired Electrolysis for Carbon Management: Pathways to Energy-Efficient CO2 Capture and Upgrade
Bibliographic record
Abstract
Electrolysis using renewable electricity presents the opportunity to valorize abundant feedstocks such as water and CO2. Electrochemistry enables CO2 upgrade for chemical and fuel, water electrosplitting for clean hydrogen, and CO2 capture. Each of these technologies today is, however, highly energy-intensive.In my PhD thesis, I explore paired electrolysis as a strategy to lower the energy requirements in such reactions. Paired electrolysis refers to combining a cathodic reaction (CO2 electroreduction reaction (CO2RR), hydrogen evolution reaction (HER), or atmospheric oxygen reduction reaction (ORR) for CO2 capture) with an anodic reaction that, in general, minimizes the overpotential on the anode, decreasing the voltage required compared to the traditional case of pairing with anodic oxygen evolution reaction (OER). I first study the co-production of CO and acrolein, pairing acidic CO2RR to CO with allyl alcohol oxidation to acrolein. Compared to the OER-paired case, the full-cell voltage is lower by 0.7 V with 96% Faradaic efficiency (FE) of CO and 85% FE of acrolein. The system reduces energy consumption to produce 1 kg of CO by 1.6× compared to the prior acidic CO2-to-CO electrolysis systems. I next pursue carbon-negative hydrogen production via bio-derived feedstock electrosplitting: pairing HER with the oxidation of glycerol. I study the reaction pathway via Raman and density functional theory (DFT) to reduce the overpotential at the anode. I report as a result a 100-hour average full-cell voltage of 0.58 V. The system produces separated streams of H2 for use and CO2 for sequestration, with an overall energy consumption of 205 MJ/kg H2 and greenhouse gas (GHG) emissions of −8 kg CO2 equivalent (CO2e)/kg H2. Finally, I extend the application of oxidation of bio-derived feedstock to high-purity CO2 to non-point-source carbon management by pairing it with atmospheric ORR-driven direct air capture (DAC). In this system, atmospheric CO2 is captured and released as high-purity CO2 while bio-derived feedstocks undergo simultaneous oxidation to CO2. I report an energy consumption of 3 MJ for removing 1 kg of net CO2e. In summary, this thesis contributes to the emerging field of paired electrolysis through the examination of both systems-level and catalyst design aspects.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".