Fabrication effect on SLM and cast Inconel 718 properties for energy conversion
Bibliographic record
Abstract
This study investigates the effect of fabrication methods on the microstructural, mechanical, and electrochemical durability of nickel alloy IN718, aiming to establish it as a cost-effective electrode material for direct seawater electrolysis using renewable energy sources. Selective laser melting (SLM) and conventional casting methods were used to fabricate IN718 alloy electrodes. Microstructural and mechanical characterization results show SLM samples have better compaction with maximum average hardness and densification of 347 (HV 0.5) and 99.2 %, respectively. This attribute is due to ultra-fast cooling rate and high-temperature gradient during sintering of 3D-printed samples in a layer-by-layer fashion. The electrochemical durability was investigated in 3.5 wt% NaCl electrolyte solution as a function of temperature (30, 50, and 80 °C). Electrochemical impedance spectroscopy (EIS) and Potentiodynamic polarization (PD) tests were conducted to evaluate electrochemical behavior. It was found that 3D printed samples have lower corrosion current density (52 μA/cm 2 ) and dissolution rate (21.26 mpy) at higher temperatures. Protective film capacitance increases with reduced film thickness due to fine-grained and desirable microstructure. Therefore, modern technology (3D printing), compared to conventional production methods, offers advantages and has the potential to produce low-cost and efficient electrode material (nickel-based alloys) for energy conversion systems and other modern engineering applications.
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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) | 0.001 | 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".