Low Cost Electrodes for Alkaline Water Electrolysis
Bibliographic record
Abstract
To succeed with the green transition, one must have cheap and reliable ways to store intermittent renewable energy. It is believed that hydrogen will play a key role in this regard, preferably through electrochemical water splitting. Alkaline water electrolysis is a mature technology, using nickel materials in most key components. Although Ni and Ni-S are active materials towards the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively, it is desirably to develop more active, durable and cheaper electrodes through a simple activation procedure of low-Ni containing materials. Moureaux et al. [1, 2] and Schäfer et al. [3] have shown that cheap stainless steel electrodes can be activated through electrooxidation in high pH electrolytes creating a nickel enriched surface. In this work we have employed a similar activation procedure, activating SS316 at 1.7 V for 18 hours at room temperature in a 3-electrode electrochemical cell using electrolytes with various KOH concentrations. The surface composition was analyzed with XPS and GD-OES before and after activation, and revealed that the Ni content in the surface increased with increasing KOH, at the expense of Fe and Cr. The composition converged to 73% Ni and 27% Fe for KOH concentration of 7.5 and higher. This composition showed the best OER performance of all surfaces prepared, including pure Ni electrodes. A similar enrichment of Ni was observed for SS304, reaching the same surface composition as SS316. However, the Ni content in the surface after activation changed less and was lower than SS316 and SS304 after activation for plate materials with higher nominal Ni bulk composition (Inconel718, Incoloy800 and NiFe 50:50 alloy). Hence, the surface composition can be tuned by selecting an electrode material and pH of the activation electrolyte. Furthermore, we have tested the activated SS316 electrodes at relevant operating conditions in a single cell alkaline water electrolyzer test rig (30wt% KOH, 80°C and 9 bar) and compared it with Ni and as-received SS316 electrodes. The activated SS316 electrodes outperformed the other electrodes and showed a stable performance during 255 hours operation at 0.8 A cm-2. The OER electrode activation procedure was successfully attempted in-situ in the alkaline single cell water electrolyzer test rig. However, the application of high currents needed for the activation led to high cell voltages (3 V) and hence harmful conditions. Finally, the effect of the activation procedure on the HER performance was also investigated and tied to the purity of the electrolyte. Deposition of Cu and Fe metal impurities was observed on the HER electrode, which increased with a lower grade of the KOH electrolyte. HER activity was observed to increase after activation in low grade KOH, which was attributed to an increased surface area and higher Fe to Cu ratio in the deposit. [1] F. Moureaux, P. Stevens, G. Toussaint, M. Chatenet, J. Power Sources 229 (2013) 123-132. [2] F. Moureaux, P. Stevens, G. Toussaint, M. Chatenet, Appl. Catal. B-Environ 258 (2019) 117963. [3] H. Schäfer, D.M. Chevrier, P. Zhang, J. Stangl, K. Müller‐Buschbaum, J.D. Hardege, K. Kuepper, J. Wollschläger, U. Krupp, S. Dühnen, Adv. Funct. Mater. 26 (2016) 6402-6417.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".