(Invited) Core-Shell Fe<sub>3</sub>O<sub>4</sub>@CoFe<sub>2</sub>O<sub>4</sub> Spinel Nanoparticles for the Oxygen Evolution Reaction in Alkaline Media
Bibliographic record
Abstract
Transition metal oxides have attracted much attention as promising anode materials for anion exchange membrane water electrolysis (AEMWE). Herewith we introduce core-shell nanoparticles comprised of a Fe3O4 core and a CoFe2O4 shell as prospective catalysts for the oxygen evolution reaction (OER) at the cathode of an AEMWE. We show that confining the active component in a thin (~1-2 nm) shell and taking advantage from the core-shell synergistic interaction allows one to reach an exceptional OER activity of 2800 A at 1.65 V vs. RHE per gram of cobalt [1]. We further show that to correctly determine the OER activity, it is necessary to study the OER in a wide interval of the catalyst loadings [2]. To shed light of the origin of the aforementioned synergistic effect between the core and the shell, the Fe3O4@CoFe2O4 nanoparticles were investigated using operando near edge X-ray absorption fine structure (NEXAFS) spectroscopy in a total electron yield (TYE) detection mode, under potential and during the OER conditions [3] . We documented a strong influence of the Fe3O4 core on the redox behavior of the CoFe2O4 shell under the OER conditions. Contrary to the previously studied Co3O4 anode, whose surface at high potentials has been shown to transform into CoOOH [4], the shell of the Fe3O4@CoFe2O4 nanoparticles maintains its spinel structure with Co(II) stabilized by the Fe(III) of the core. The results suggest the occurrence of a cation-redox OER mechanism involving cooperative redox behavior between Co and Fe atoms. It is remarkable that the core-shell structure is essentially preserved under the OER conditions. Figure 1. Left-hand side: cartoon showing the composition of the core-shell nanoparticle and charge transfer from Co to Fe occurring under positive polarization. Right-hand side: Fe L-edge difference spectra acquired at 1.2, 1.4 and 1.8 V vs RHE (the reference itself taken at 1.0 V vs. RHE). Reproduced from Ref. [3]. Finally, we study the influence of the shell composition by replacing Co by Ni on the OER activity and stability of core-shell nanoparticles. Acknowledgements The authors are indebted to S. Hettler and R. Arenal of INMA (Zaragoza, Spain), S. Holdcroft of SFU (Vancouver, Canada) and J. Velasco-Vélez of Alba Synchrotron facility (Spain) for their cooperation. We thank the HZB für Materialien und Energie for the allocation of synchrotron radiation beamtime. Financial support from Jean-Marie-Lehn foundation is gratefully acknowledged. REFERENCES [1] L. Royer, S. Hettler, R. Arenal, T. Asset, B. Rotonnelli, A. Bonnefont, E. Savinova, B. Pichon, submitted. [2] L. Royer, J. Guehl, M. Zilbermann, T. Dintzer, C. Leuvrey, B. Pichon, E. Savinova, A. Bonnefont, submitted. [3] L. Royer, A. Bonnefont, T. Asset, B. Rotonnelli, J. Velasco-Vélez, S. Holdcroft, S. Hettler, R. Arenal, B. Pichon, E. Savinova, ACS Catalysis, https://doi.org/10.1021/acscatal.2c04512. [4] F. Reikowski, F. Maroun, I. Pacheco, T. Wiegmann, P. Allongue, J. Stettner, O. Magnussen, ACS Catal. 2019, 9 (5), 3811–3821; https://doi.org/10.1021/acscatal.8b04823. Figure 1
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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) | 0.001 | 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".