Pure-Water-Fed Forward-Bias Bipolar Membrane CO 2 Electrolyzer.
Matthias HeßelmannJason Keonhag LeeSudong ChaeAndrew TrickerRobert Gregor KellerMatthias WesslingJi SuDouglas I KushnerAdam Z WeberXiong PengPublished in: ACS applied materials & interfaces (2024)
Coupling renewable electricity to reduce carbon dioxide (CO 2 ) electrochemically into carbon feedstocks offers a promising pathway to produce chemical fuels sustainably. While there has been success in developing materials and theory for CO 2 reduction, the widespread deployment of CO 2 electrolyzers has been hindered by challenges in the reactor design and operational stability due to CO 2 crossover and (bi)carbonate salt precipitation. Herein, we design asymmetrical bipolar membranes assembled into a zero-gap CO 2 electrolyzer fed with pure water, solving both challenges. By investigating and optimizing the anion-exchange-layer thickness, cathode differential pressure, and cell temperature, the forward-bias bipolar membrane CO 2 electrolyzer achieves a CO faradic efficiency over 80% with a partial current density over 200 mA cm -2 at less than 3.0 V with negligible CO 2 crossover. In addition, this electrolyzer achieves 0.61 and 2.1 mV h -1 decay rates at 150 and 300 mA cm -2 for 200 and 100 h, respectively. Postmortem analysis indicates that the deterioration of catalyst/polymer-electrolyte interfaces resulted from catalyst structural change, and ionomer degradation at reductive potential shows the decay mechanism. All these results point to the future research direction and show a promising pathway to deploy CO 2 electrolyzers at scale for industrial applications.
Keyphrases
- carbon dioxide
- ionic liquid
- bipolar disorder
- room temperature
- wastewater treatment
- reduced graphene oxide
- open label
- ion batteries
- double blind
- single cell
- placebo controlled
- heavy metals
- cell therapy
- highly efficient
- optical coherence tomography
- clinical trial
- stem cells
- gold nanoparticles
- randomized controlled trial
- bone marrow
- metal organic framework
- climate change
- data analysis