Diamond and Related Materials, Journal Year: 2024, Volume and Issue: unknown, P. 111914 - 111914
Published: Dec. 1, 2024
Language: Английский
Diamond and Related Materials, Journal Year: 2024, Volume and Issue: unknown, P. 111914 - 111914
Published: Dec. 1, 2024
Language: Английский
Surfaces and Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 106342 - 106342
Published: March 1, 2025
Language: Английский
Citations
0Small, Journal Year: 2025, Volume and Issue: unknown
Published: April 3, 2025
Abstract Electroreduction of carbon dioxide (CO 2 ) is a key strategy for achieving net‐zero emissions. Copper (Cu)‐based electrocatalysts have shown promise CO conversion into valuable chemicals but are hindered by limited C 2+ product selectivity due to competing hydrogen evolution and ineffective dimerization adsorbed intermediate ( * CO). Here, functional‐group‐directed reported enhance using single‐walled nanotubes (SWCNTs) as supports. The catalytic performance Cu nanoparticles strongly influenced the type density functional groups on SWCNTs. Optimized Cu/amine‐functionalized SWCNTs achieved Faradaic efficiency 66.2% partial current −270 mA cm −2 products within flow cell, outperforming Cu/SWCNTs Cu/cyano‐functionalized Density theory calculations revealed that electron‐donating amine can facilitate electron transfer from graphite sheet atoms, thereby shifting d‐band center upward. This shift enhances its hydrogenation derivative adsorption promotes water splitting, leading an increased tendency generation products. In situ infrared Raman spectroscopy confirm enhancement CHO coverage, facilitating C─C coupling. work provides molecular framework exploring interactions between active metals in electrolysis, offering insights designing catalysts broad range electrocatalytic processes.
Language: Английский
Citations
0ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown
Published: April 8, 2025
Biomass-based carbon materials are considered promising metal-free catalysts for the 2e- oxygen reduction reaction (ORR) to synthesize H2O2 and act as air electrodes in Zn-air batteries. However, optimization of catalyst structure is a complex process due diversity biomass precursors synthesis parameters. Machine learning, new artificial intelligence technology, has recently been used various fields owing its ability rapidly analyze large amounts data guide material synthesis. Consequently, we constructed machine learning model based on previously reported experimental guided fabrication boron-doped ORR. The achieved catalytic performance exceeded most ORR terms selectivity (90-95% broad potentials 0.30-0.68 V vs reversible hydrogen electrode), stability (maintaining over 90% 12 h), yield (3450 mmol gcatalyst-1 h-1), Faraday efficiency (over 90%). We applied batteries showed high capacity (2856 mAh g-1) twice that traditional commercial metal catalysts. Therefore, this study proposed an effective biomass-based field electrocatalysis.
Language: Английский
Citations
0Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 162752 - 162752
Published: April 1, 2025
Language: Английский
Citations
0Science China Chemistry, Journal Year: 2025, Volume and Issue: unknown
Published: April 14, 2025
Language: Английский
Citations
0Journal of Colloid and Interface Science, Journal Year: 2024, Volume and Issue: 683, P. 631 - 640
Published: Dec. 7, 2024
Language: Английский
Citations
3Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 485, P. 136845 - 136845
Published: Dec. 13, 2024
Language: Английский
Citations
3Diamond and Related Materials, Journal Year: 2024, Volume and Issue: unknown, P. 111914 - 111914
Published: Dec. 1, 2024
Language: Английский
Citations
1