(Invited) Membrane Coated Electrocatalysts for Selective and Stable Oxygen Evolution in Seawater
Bibliographic record
Abstract
Seawater electrolysis has the potential to be a more sustainable means of hydrogen production compared to conventional water electrolysis which relies on highly pure water. This is particularly true for arid coastal regions with access to seawater and ideal conditions for harvesting solar and wind energy, but where fresh water is already scarce.[1] Seawater electrolysis is challenging due to the large concentration of chloride ions, which can be detrimental to electrocatalyst stability. Furthermore, the presence of chloride ions allows the chlorine evolution reaction (CER) to compete with the oxygen evolution reaction (OER) at the anode. Although Cl2 is of industrial value, global hydrogen production already exceeds chlorine production, and demand for hydrogen is projected to grow more rapidly. Additionally, since Cl2 is toxic and harmful to the environment, implementing seawater electrolysis is simplified if pure oxygen is produced and can be safely vented to the atmosphere. Our group has shown that ultrathin semi permeable oxide overlayers can be designed to selectively transport reactants to the active catalyst at the buried interface.[2-3] Importantly for seawater electrolysis, the oxide overlayer selectively rejected chloride ions while allowing for water transport.[4] Thus, the oxide overlayer acts as a membrane, and the composite material can be referred to as a membrane coated electrocatalyst (MCEC). An additional advantage of the MCEC architecture compared to conventional electrocatalysts is enhanced stability.[5] This makes MCECs particularly attractive for stable and selective OER in seawater. This work describes how MCECs can (i) improve catalyst stability and (ii) enable selectivity for OER over CER by impeding transport of chloride ions to the catalyst at the buried interface. This work explores the fundamental relationships between chloride ion transport through different oxide overlayer materials. This knowledge is then applied to prepare MCECs supported on high surface area porous electrodes. References [1] S. Dresp, et al., ACS Energy Lett., 4, 933 (2019). [2] N. Y. Labrador, et al. , ACS Catal. , 8, 1767 (2018). [3] M. E. S. Beatty, et al., ACS Appl. Energy Mater., 3 , 12338 (2020). [4] A. A. Bhardwaj, et al. , ACS Catal. , 11, 1316 (2021). [5] N. Y. Labrador, et al . , Nano Lett. , 16 , 6452 (201 6 ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".