Testing and assessment of various catalysts for uniquely designed cathodes for hydrogen evolution reactions
Bibliographic record
Abstract
This study reports the electrodeposition of various unique catalysts for 3D-printed cathodes for alkaline water electrolysis. This paper explores hydrogen evolution reaction performance for nickel, nickel-copper, nickel-iron, and nickel-molybdenum 3D-printed electrodes. In particular, this study reports a novel electrodeposition of nickel-iron and nickel-molybdenum on conductive PLA 3D-printed electrode surfaces. The performance of the electrodes is assessed through electrochemical models, including cyclic voltammetry (CV), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). The results of the study show considerable differences at a standard current density of 10 mA/cm 2 . The nickel-iron and nickel-copper coated 3D-printed electrodes are found to have an overpotential of 270 mV and 275 mV, respectively. The nickel-copper and nickel-iron coated electrodes also showed to have a low resistance. The amount of metal deposited also showed to have an important role. At a potential of −2.5 V, nickel coated electrode Ni4x, with four times the nickel mass deposition (0.178 g/cm 2 ) of another nickel coated electrode Ni1x (0.044 g/cm 2 ), is found to have a current density of −106 mA/cm 2 in comparison to −44 mA/cm 2 , respectively. These findings provide important insights for optimizing additive manufacturing for electrochemical systems. • This study reports a novel electrodeposition of Ni-Fe and Ni-Mo for 3D-printed cathodes. • The paper investigates the impact of the catalyst mass deposition on the current density. • The performance of the electrodes is evaluated based on electrochemical models. • Electrodes with higher metal amounts deposited had better electrochemical performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".