Diamond and Related Materials, Год журнала: 2024, Номер unknown, С. 111914 - 111914
Опубликована: Дек. 1, 2024
Язык: Английский
Diamond and Related Materials, Год журнала: 2024, Номер unknown, С. 111914 - 111914
Опубликована: Дек. 1, 2024
Язык: Английский
Surfaces and Interfaces, Год журнала: 2025, Номер unknown, С. 106342 - 106342
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Small, Год журнала: 2025, Номер unknown
Опубликована: Апрель 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.
Язык: Английский
Процитировано
0ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown
Опубликована: Апрель 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.
Язык: Английский
Процитировано
0Chemical Engineering Journal, Год журнала: 2025, Номер unknown, С. 162752 - 162752
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Science China Chemistry, Год журнала: 2025, Номер unknown
Опубликована: Апрель 14, 2025
Язык: Английский
Процитировано
0Journal of Colloid and Interface Science, Год журнала: 2024, Номер 683, С. 631 - 640
Опубликована: Дек. 7, 2024
Язык: Английский
Процитировано
3Journal of Hazardous Materials, Год журнала: 2024, Номер 485, С. 136845 - 136845
Опубликована: Дек. 13, 2024
Язык: Английский
Процитировано
3Diamond and Related Materials, Год журнала: 2024, Номер unknown, С. 111914 - 111914
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
1