Vertically Aligned Ni Nanowires as a Platform for Kinetically Limited Water-Splitting Electrocatalysis
Bibliographic record
Abstract
Electrochemical templating through porous membranes is applied to form arrays of micrometer long Ni nanowires (NWs). Detailed structural and electrochemical characterization, including electrochemical impedance spectroscopy (EIS), was conducted to assess the electrocatalytic properties of these Ni NW arrays for the O 2 evolution reaction (OER) in 1 M KOH. Detailed structural analysis showed that Ni NWs have a diameter of ca. 350 nm and a mean 80 nm average distance between the NW center. For the longest NWs (20 μm long), the ratio between the pore opening and pore length is 0.4%. From detailed HR-TEM and EELS analysis providing information on the chemical state of atoms from quantitative analysis of the signals, Ni NWs are composed of a Ni metallic core surrounded by a Ni(OH) 2 layer that thickens from 10 to 20 nm after extensive electrochemical tests. Three different methods, namely, SEM geometry measurements, the α-Ni(OH) 2 charge method, and the capacitance method, were used to assess how the current varies with the NW length. The three different methods are all in agreement, and the current increases with length (or mass) precisely and only because of the surface area effect. The most surprising result is that the OER process occurs with exactly the same intrinsic catalytic activity at the bottom of these deep pores, and gas is evolved without any significant effects of electrolyte resistance, mass transport of dissolved oxygen, or bubble occlusion of the pores. Accordingly, vertically aligned 1D NWs can be used as an effective platform that mitigates the negative effects of gas evolution. Increasing the intrinsic activity by incorporating more active materials will further improve this type of electrode.
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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".