Single silicon-doped CNT as a metal-free electrode for robust nitric oxide reduction utilizing a Lewis base site: an ingenious electronic "Reflux-Feedback" mechanism.
Lei YangJiake FanWeihua ZhuPublished in: Physical chemistry chemical physics : PCCP (2023)
The electrocatalytic reduction of nitric oxide (NO) has become the most charming approach for the sustainable synthesis of ammonia (NH 3 ), however, the development of a valid catalyst endowed with low cost, high efficiency, and long-term endurance still faces an enormous challenge. In view of the famous concept of "donate and accept", various transition metal-based electrodes have been predicted and brought into production for electrocatalysis, but metal-free materials or novel activation mechanisms are rarely reported. Here, metal-free electrocatalysts, namely individual silicon (Si) atom-embedded single-walled carbon nanotubes (CNTs), for the NO reduction reaction (NORR) were put forward by performing first-principles calculations. The results disclose that the discarded NO can be converted into value-added NH 3 on Si-CNT(10, 0) with a limiting potential of -0.25 V. Importantly, the doped Si atom acts as a Lewis base site that drives some of the p-orbital electrons to return to the surrounding carbon atoms and then feed adequate electron back to intermediates, rendering it more flat for the electroreduction progress. In summary, the designed carbon-based electrode holds great promise for experimental trial and offers a certain degree of theoretical guidance.
Keyphrases
- room temperature
- nitric oxide
- low cost
- high efficiency
- molecular dynamics
- walled carbon nanotubes
- metal organic framework
- transition metal
- quantum dots
- ionic liquid
- highly efficient
- electron transfer
- reduced graphene oxide
- nitric oxide synthase
- carbon nanotubes
- hydrogen peroxide
- phase iii
- visible light
- solid state
- human health
- study protocol
- gold nanoparticles
- risk assessment
- randomized controlled trial
- big data
- open label
- climate change
- body composition
- deep learning
- perovskite solar cells