An experimental performance evaluation of newly designed flow-through electrodes for hydrogen production
Bibliographic record
Abstract
This study reports a unique flow-through electrode design for hydrogen production in alkaline water electrolysis. This paper investigates a variety of electrodeposition coatings on the 3D-printed electrodes, including nickel, nickel-copper, and nickel-iron. The catalytic activity and properties of the electrodes are examined through electrochemical measurements involving linear sweep voltammetry, cyclic voltammetry, electrochemical impedance spectroscopy, and surface morphology analysis. An experimental setup is also developed to measure hydrogen production and evaluate the efficiency of the designed electrodes. The study finds the flow-through electrodes coated with nickel-copper and nickel-iron achieve the current densities of 54 mA/cm 2 and 45 mA/cm 2 , respectively. The activation overpotentials for the nickel-copper coated electrode are reported as 539 mV, and 408 mV for the nickel-iron coated electrode, both at a current density of 10 mA/cm 2 . The highest production rate is obtained within the first 15 minutes, 0.273 μg/s for the Ni-Cu flow-through electrode and 0.255 μg/s for the Ni-Fe flow-through electrode. The study further shows that flow-through electrodes produce hydrogen with an efficiency approximately 74% higher than traditional electrodes, suggesting a potentially better design for better hydrogen production in industrial applications. • This study reports a new flow-through electrode design for hydrogen production. • The electrodes are fabricated using 3D-printing and coated using electrodeposition. • The performance is evaluated using electrochemical and hydrogen measurements. • The study finds that flow-through electrodes perform better than traditional. • The flow-through electrodes consume less energy with the same or higher production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".