Morphology-Dependent\nEnhancement of Electrocatalytic\nNitrogen Reduction Activity Using Iron Phthalocyanine Nanostructures

Ammonia is one of the most essential raw materials for\ndaily life\napplications. As an alternative to the Haber–Bosch process,\nscientists are focusing on an important domain of electrocatalysis\nfor ammonia production. Herein, we approached a morphological adaptation\nof the electrocatalyst (iron phthalocyanine, FePc) based on hollow\nnanotube and rod types; the catalyst showed different N<sub>2</sub>-to-NH<sub>3</sub> productivity. Under ambient conditions, FePc nanorods\nshowed a good ammonia yield rate and Faradaic efficiency (FE) of 323.44\nμg h<sup>–1</sup> mg<sub>cat.</sub><sup>–1</sup> and 23.33%, respectively, at −0.4 V vs RHE in 0.05 M H<sub>2</sub>SO<sub>4</sub>. However, when the rod was adapted to a hollow\nnanotube structure by control of the temperature and time parameters,\nthe ammonia productivity further improved. Under the same conditions,\nFePc nanotubes showed an excellent ammonia yield rate of 425.46 μg\nh<sup>–1</sup> mg<sub>cat.</sub><sup>–1</sup> and a\ncorresponding FE of 23.61% at −0.4 V vs RHE. In addition to\nexperimental observations, theoretical analysis using density functional\ntheory is also provided to establish the reaction mechanism of ammonia\nsynthesis from nitrogen reduction reaction (NRR) using an FePc electrocatalyst.\nThis work opens an avenue showing geometric structural induction of\nelectrocatalytic activity toward future sustainable ammonia production.

Publication Details

Journal
Figshare
Published
2024-11-22
DOI
https://doi.org/10.1021/acsaem.4c02204.s001
Primary Topic
Ammonia Synthesis and Nitrogen Reduction
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Morphology-Dependent\nEnhancement of Electrocatalytic\nNitrogen Reduction Activity Using Iron Phthalocyanine Nanostructures

Figshare
Ammonia Synthesis and Nitrogen Reduction
article

Morphology-Dependent\nEnhancement of Electrocatalytic\nNitrogen Reduction Activity Using Iron Phthalocyanine Nanostructures

article en

Abstract

Ammonia is one of the most essential raw materials for\ndaily life\napplications. As an alternative to the Haber–Bosch process,\nscientists are focusing on an important domain of electrocatalysis\nfor ammonia production. Herein, we approached a morphological adaptation\nof the electrocatalyst (iron phthalocyanine, FePc) based on hollow\nnanotube and rod types; the catalyst showed different N<sub>2</sub>-to-NH<sub>3</sub> productivity. Under ambient conditions, FePc nanorods\nshowed a good ammonia yield rate and Faradaic efficiency (FE) of 323.44\nμg h<sup>–1</sup> mg<sub>cat.</sub><sup>–1</sup> and 23.33%, respectively, at −0.4 V vs RHE in 0.05 M H<sub>2</sub>SO<sub>4</sub>. However, when the rod was adapted to a hollow\nnanotube structure by control of the temperature and time parameters,\nthe ammonia productivity further improved. Under the same conditions,\nFePc nanotubes showed an excellent ammonia yield rate of 425.46 μg\nh<sup>–1</sup> mg<sub>cat.</sub><sup>–1</sup> and a\ncorresponding FE of 23.61% at −0.4 V vs RHE. In addition to\nexperimental observations, theoretical analysis using density functional\ntheory is also provided to establish the reaction mechanism of ammonia\nsynthesis from nitrogen reduction reaction (NRR) using an FePc electrocatalyst.\nThis work opens an avenue showing geometric structural induction of\nelectrocatalytic activity toward future sustainable ammonia production.

Figshare
Openalex Percentile: Top 85%
Ammonia Synthesis and Nitrogen Reduction
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.