Tracking Feni Nanoparticle Surface Inclusions after Electrochemical Aging for the Oxygen Evolution Reaction
Bibliographic record
Abstract
Improving the efficiency of the oxygen evolution reaction (OER) could be highly beneficial to a variety of clean energy applications. Technologies that include fuel cells, electrolyzers, and metal-air batteries are often limited by the cost and scarcity of precious metal catalysts. There is, therefore, a desire to identify earth-abundant electrocatalysts that exhibit reduced overpotentials and to prepare nanocatalyst structures that both efficiently utilize and increase the active surface area of these materials. Recently, the intentional inclusion of Fe into Ni oxide thin films to prepare electrodes of a homogenous composition have exhibited a reduction in their overpotentials and improved overall catalytic activity. The incorporation of discreet FeNi nanoparticles (NPs) into the surfaces of Ni electrodes has not, as of yet, been fully explored and is of interest to assess how these surface inclusions and morphologies may change with prolonged electrochemical aging. In this work, dimpled Ni electrodes supporting FeNi NPs were prepared by electroplating around spherical poly(styrene) (PS) templates (500-nm in diameter). The PS spheres were coated with FeNi NPs using solution-phase assembly techniques. The NP coated PS spheres served two functions: (i) creating regular dimpled features for tracking the electrode morphology; and (ii) positioning the FeNi NPs at the electrode surfaces within these dimpled features. Alkaline electrochemical aging by cyclic voltammetry (CV) was conducted to achieve an adequately stable Ni oxy-hydroxide phase prior to the OER measurements. Changes to the composition and morphology of these electrodes, both before and after electrochemical measurements, were monitored by scanning and transmission electron microscopy techniques including correlative energy dispersive X-ray spectroscopy. The FeNi NPs coated on Ni electrodes demonstrated a higher electrochemical activity for the OER than dimpled Ni electrodes without the FeNi NPs.
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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".