Synergistic niobium and manganese co-doping into RuO2 nanocrystal enables PEM water splitting under high current
Bibliographic record
Abstract
Low-cost ruthenium-based catalysts with high activity have emerged as promising alternatives to iridium-based counterparts for acidic oxygen evolution reaction (OER) in proton exchange membrane water electrolyzers (PEMWE), but the poor stability under high current density remains as a key challenge. Here, we utilize the synergistic complementary strategy of introducing earth-abundant Mn and Nb dopants in ruthenium dioxide (RuO2) for Nb0.1Mn0.1Ru0.8O2 nanoparticle electrocatalyst that exhibits a low overpotential of 209 mV at 10 mA cm−2 and good stability of > 400 h at 0.2 A cm−2 in 0.5 M H2SO4. Significantly, a PEMWE device fabricated with Nb0.1Mn0.1Ru0.8O2 anode can operate continuously at least for 1000 h at 0.5 A cm−2 with 59 μV h−1 decay rate. Operando Raman spectroscopy analysis, differential electrochemical mass spectroscopy measurements, X-ray absorption spectroscopy analysis and theoretical calculations indicate that OER reaction on Nb0.1Mn0.1Ru0.8O2 primarily follows the adsorbate evolution mechanism with much favorable energy barrier accompanied by a locally passivated lattice oxygen mechanism (AEM-LPLOM) and the co-existed Nb and Mn in RuO2 crystal lattice could not only stabilize the lattice oxygen, but also relieve the valence state fluctuation of Ru site to stabilize the catalyst during the reaction. Ruthenium-based catalysts for the acidic oxygen evolution reaction under high current exhibit poor stability. Here, authors develop a doped ruthenium oxide nanocrystal, which improves water splitting performance by stabilizing lattice oxygen and limiting the ruthenium site valence oscillation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".