N-Doped-Induced Local Covalency Elevation for Enhancing Cathodic Performance of Solid Oxide Electrolysis Cells
Bibliographic record
Abstract
The Ni-based cathode is central to the performances of solid oxide electrolysis cells (SOECs), yet it suffers from poor oxygen ion conductivity, sluggish electron transport, and inefficient CO 2 /H 2 O activation. This study explores a heteroatom-doping strategy to comprehensively address the ionic, electronic, and molecular issues in solid oxide cells. When operated in SOEC mode, the maximum power density of the N-doped Ni/CGO (NiO/CGON) cathode achieved a 29.6% improvement over its undoped Ni/CGO, along with a 27.3% reduction in polarization resistance. Moreover, a 31.3% increase in maximum current density was obtained along with considerable stable operation over 150 h at an industrial-scale current density of 0.5 A/cm 2 . Combined electrochemical measurements, in situ diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, and density functional theory (DFT) simulations reveal that N-doped-induced local covalency elevation via the formation of Ce–O/N bonds substantially promotes the oxygen ion and electron conductivity and creates the synergistic Lewis acid–base sites for simultaneous activation of both CO 2 and H 2 O, thereby collectively addressing the ionic, electronic, and molecular issues in SOCs in one simple method.
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".