An Electrochemical Treatment for Improving the Activity of Nickel and Other Metals for the Hydrogen Evolution and Other Reactions
Bibliographic record
Abstract
The electrolysis of water to produce hydrogen gas may form an important part of the world’s future economy as a way to store the energy for sustainable sources for later use. Part of what will determine if this use is practical is the efficiency of the catalysts used for the two half reactions that make up the electrolysis reaction. Presently in industry the cathodic reaction, the hydrogen evolution reaction (HER), is catalysed by nickel most commonly. Thus, there is a motivation to increase the activity of nickel toward the HER. Traditionally this was done by creating a high specific surface area or by increasing the intrinsic activity of nickel through alloying with other metals. Recently the importance using bifunctional catalysts for the HER was seen, this opened up a new line of investigation for methods to increase the activity of nickel HER catalysts. In this work a novel procedure to modify a nickel surface into highly active bifunctional catalyst is presented. The method is an electrochemical treatment that works by applying an alternating voltage to the nickel surface in a mildly acidic environment that causes it to oxidize in a manner similar to platinum. The platinum-like oxidation of the surface yields the Ni(OH)x structure (with x=0-2 across the surface), and allows a way to side step the problem of clumping of phases which is seen in bifunctional catalysts. The surface created by the treatment not only catalyses the HER, but also the urea electrooxidation, among other reactions. In this thesis the treated nickel surface is characterized and shown to be a highly active stable bifunctional catalyst, when the optimal parameters for the treatment are used. The characterization also shows that treated surfaces conform to the expectations of the novel Ni(OH)x structure. The treated surface is also seen to be active toward urea electrooxidation, but for slightly different parameters than the optimal parameters for a HER catalyst. Several practical elements of implementing the treatment in an industrial setting are dealt with. Finally, after concluding remarks, several new lines of enquiry that have been opened by this novel technique are briefly discussed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".