Mechanistic insights into lepidocrocite conversion to hematite from variable temperature Raman microscopy
Bibliographic record
Abstract
Abstract The consistent fabrication of high performance α -Fe 2 O 3 photoanodes for the oxygen evolution reaction remains a challenge. We work towards resolving this issue by developing in situ variable temperature Raman spectroscopy as a means to better understand the formation of α -Fe 2 O 3 , using the conversion of γ -FeOOH to α -Fe 2 O 3 under varied gaseous environments as a model case. The sensitivity of Raman spectroscopy to structural changes provides mechanistic insights that are not readily available in more conventional approaches, such as thermal gravimetric analysis and differential scanning calorimetry. The Raman spectra are combined with conventional thermal analyses to interpret the photoelectrocatalytic performance of a series of α -Fe 2 O 3 photoanodes prepared by systematic variation of a three-stage annealing protocol. The combined results suggest that protohematite, a form of α -Fe 2 O 3 where trapped hydroxyl ligands are balanced by Fe(III) vacancies, forms between 200 °C and 400 °C in a reaction environment-dependent fashion. This protohematite is shown to be remarkably persistent once formed, degrading photoelectrocatalytic performance. This research advances understanding of the γ -FeOOH to α -Fe 2 O 3 structural transformation, illustrates a powerful method to study solid state phase transitions, and provides guidance for the synthesis of high quality α -Fe 2 O 3 from a convenient precursor.
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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 | 0.001 |
| 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".