Applications of Ferric Oxide in Water Splitting by Electrolysis: A Comprehensive Review
Bibliographic record
Abstract
In water electrolysis, the use of an efficient catalyst derived from earth-abundant materials which is cost-effective and stable is essential for the economic sustainability of hydrogen production. A wide range of catalytic materials have been reported upon so far, among which Fe2O3 stands out as one of the most credible candidates in terms of cost and abundance. However, Fe2O3 faces several limitations due to its poor charge transfer properties and catalytic ability; thus, significant modifications are essential for its effective utilization. Considering the future of water electrolysis, this review provides a detailed summary of Fe2O3 materials employed in electrolytic applications with a focus on critically assessing the key electrode modifications that are essential for the materials’ utilization as efficient electrocatalysts. With this in mind, Fe2O3 was implemented in a heterojunction/composite, doped, carbon supported, crystal facet tuned system, as well as in metal organic framework (MOF) systems. Furthermore, Fe2O3 was utilized in alkaline, seawater, anion exchange membrane, and solid oxide electrolysis systems. Recently, magnetic field-assisted water electrolysis has also been explored. This comprehensive review highlights the fact that the applicability of Fe2O3 in electrolysis is limited, and hence, intense and strategically focused research is vital for converting Fe2O3 into a commercially viable, cost-effective, and efficient catalyst material.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 0.001 |
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".