A surface-modified Ni (Fe-Cr-Mo) alloy as electrocatalyst for hydrogen production in alkaline or acid medium, pp. 217-225
Bibliographic record
Abstract
This study evaluated a surface-modified Ni (Fe-Cr-Mo) based alloy as an electrocatalyst to enhance hydrogen evolution reactions (HER) in comparison to an A304 stainless steel.An acidic electrolyte (10% H2SO4) and an alkaline electrolyte (10 g/L NaOH) were used with DSA-type counter electrodes (Ti|RuO2 and Ti|IrO2).A solution and quenching thermal treatment were applied to the Ni alloy and the surface was chemically treated with a 0.6 M solution of FeCl3 to generate a change in the structure and provide a greater active surface area.Voltammetry and chronoamperometry tests were performed in a range of cathodic potentials between 0.0 to -2.0 V to evaluate the current density during HER, showing that the Ni alloy presented lower overpotentials for the initiation of HER, as well as a higher current density than A304 steel.The alkaline medium provided greater overpotentials (-1.3 V) for the initiation of HER with lower current density values than the acidic medium (-0.5 V).Furthermore, a notable increase in current density and a lower overpotential (-0.3 V) was observed for the HER outset when the Ni alloy was subjected to heat and surface chemical treatments to achieve a cathodic current density of 2.0 A•cm -2 at -1.5 V.
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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".