Asymmetric Strain in the Oxidized and Reduced States of Heterogeneous Electrocatalysts
Bibliographic record
Abstract
The growing number of functional examples of “strain engineering” is drawing attention to the influence that mechanical or geometric considerations can have on the electronic structure and electrochemical behavior of electrocatalysts. It will be shown that the concept of lattice strain, and the changes in electronic structure that accompany it, can explain the experimentally observed trends in electrochemical behavior of common mixed-metal oxide catalysts for the oxygen evolution reaction. Structural analysis of a series of iron-nickel hydroxide materials reveal a significant change in O-Ni-O bond angles upon electrochemical oxidation. 1 Subsequent analysis shows that insertion of ions with varied ionic radii into a Ni(OH) x host lattice introduces localized geometric distortions in the nickel environments that can be observed by tracking Ni-based d-d transitions in the near-infrared. 2 The type (tensile or compressive) and magnitude of strain is correlated to the oxidation state of nickel ions and the ionic radius of the secondary ion. Experimentally observed trends in electrochemical behavior track the ionic radius of the secondary ion, indicating that the degree of strain directly correlates to electrochemical behavior. Density functional theory calculations indicate that inequivalence of this internalized strain in the oxidized and reduced states of the catalyst material introduces asymmetry into the potential energy surface landscape. This asymmetry effectively decreases the activation energy for electron transfer and may simultaneously decrease the electrochemical transfer coefficient. Evidence suggests that the synthetic protocol employed exerts significant influence over the placement of secondary ions and ability to observe this effect. 3 (1) Smith, R. D. L.; Pasquini, C.; Loos, S.; Chernev, P.; Klingan, K.; Kubella, P.; Mohammadi, M. R.; González-Flores, D.; Dau, H. Energy Environ. Sci 2018 , 11 , 2476–2485. (2) Alsac, E. P.; Whittingham, A.; Liu, Y.; Smith, R. D. L. Chem. Mater. 2019 , 31 , 7522–7530. (3) Rong, W.; Stepan, S.; Smith, R. D. L. MRS Adv. 2019 , 4 , 1843–1850.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".