The Structural Properties and Photoelectrocatalytic Response of Mn‐Doped Hematite Photoanodes Prepared via a Modified Electrodeposition Approach
Bibliographic record
Abstract
Abstract The concept of nanostructuring and doping of hematite (α‐Fe2O3) photoanodes have been widely engaged towards improving their photoelectrocatalytic (PEC) response. Here, a FeCl3‐based solution was modified with 0–10 % polyethylene glycol (PEG) 400 and used as an electrolyte for the electrodeposition of nanostructured α‐Fe2O3 thin films. The electrolyte containing 10 % PEG was further used to prepare Mn‐doped α‐Fe2O3 films by adding 1, 3, 6, and 10 % of MnCl2.4H2O with respect to the molarity of FeCl3. The addition of 10 % PEG into the electrolyte limited particle agglomeration and yielded the best PEC response among the pristine films. The 3 % Mn‐doped α‐Fe2O3 photoanodes produced the highest photocurrent, yielding 2.2 and 6.1‐fold photocurrent enhancement at 1.23 V and 1.5 V vs. RHE respectively, over the pristine films. The improved PEC response is linked to the reduced particle agglomeration and improved charge transport properties observed for the films. Density functional theory (DFT) calculations of the formation energies yielded negative values for the Mn‐doped α‐Fe2O3, which implies that the materials are thermodynamically stable after doping. This work introduces a new pathway for the electrodeposition of doped α‐Fe2O3 films and underscores the roles of Mn‐doping in boosting their PEC response.
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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 | 0.000 |
| 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) | 0.000 | 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".