Less‐Energy Consumed Hydrogen Evolution Coupled with Electrocatalytic Removal of Ethanolamine Pollutant in Saline Water over Ni@Ni<sub>3</sub>S<sub>2</sub>/CNT Nano‐Heterostructured Electrocatalysts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Energy crises, environmental pollution, and freshwater deficiency are critical issues on the planet. Electrolytic hydrogen generation from saline water, particularly from salt‐contained hazardous wastewater, is significant to both environment and energy concerns but still challenging due to the high energy cost, severe corrosion, and the absence of competent electrocatalysts. Herein, a novel strategy is proposed for energy‐efficient hydrogen production coupled with electro‐oxidation removal of ethanolamine pollutant in saline water. To achieve this, an active and durable heterostructured electrocatalyst is developed by in situ growing Ni@Ni 3 S 2 core@shell nanoparticles in cross‐linked 3D carbon nanotubes’ (CNTs) network, achieving high dispersibility and metallic property, low packing density, and enriched exposed active sites to facilitate fast electron/mass diffusion. The unique Ni@Ni 3 S 2 /CNTs nano‐heterostructures are competent for long‐term stably electro‐oxidizing environmental‐unfriendly ethanolamine at a high current density of 100 mA cm −2 in saline water, which not only suppresses oxygen and chloride evolution reactions but also decreases the energy consumption to boost hydrogen production. Associated with experimental results, density functional theory studies indicate that the collaborative adsorption of electrolyte ions and ethanolamine molecules can synergistically modulate the adsorption/desorption properties of catalytic active centers on Ni@Ni 3 S 2 /CNTs surface, leading to long‐term stabilized electrocatalysis for efficient ethanolamine oxidation removal and less‐energy hydrogen simultaneous production in saline water.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it