TY - JOUR
T1 - Unveiling the Role of Charge Transfer in Enhanced Electrochemical Nitrogen Fixation at Single-Atom Catalysts on BX Sheets (X = As, P, Sb)
AU - Zafari, Mohammad
AU - Umer, Muhammad
AU - Nissimagoudar, Arun S.
AU - Anand, Rohit
AU - Ha, Miran
AU - Umer, Sohaib
AU - Lee, Geunsik
AU - Kim, Kwang S.
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2022/5/26
Y1 - 2022/5/26
N2 - To tune single-atom catalysts (SACs) for effective nitrogen reduction reaction (NRR), we investigate various transition metals implanted on boron-arsenide (BAs), boron-phosphide (BP), and boron-antimony (BSb) using density functional theory (DFT). Interestingly, W-BAs shows high catalytic activity and excellent selectivity with an insignificant barrier of only 0.05 eV along the distal pathway and a surmountable kinetic barrier of 0.34 eV. The W-BSb and Mo-BSb exhibit high performances with limiting potentials of-0.19 and-0.34 V. The Bader-charge descriptor reveals that the charge transfers from substrate to*NNH in the first protonation step and from*NH3 to substrate in the last protonation step, circumventing a big hurdle in NRR by achieving negative free energy change of*NH2 to*NH3. Furthermore, machine learning (ML) descriptors are introduced to reduce computational cost. Our rational design meets the three critical prerequisites of chemisorbing N2 molecules, stabilizing*NNH, and destabilizing*NH2 adsorbates for high-efficiency NRR.
AB - To tune single-atom catalysts (SACs) for effective nitrogen reduction reaction (NRR), we investigate various transition metals implanted on boron-arsenide (BAs), boron-phosphide (BP), and boron-antimony (BSb) using density functional theory (DFT). Interestingly, W-BAs shows high catalytic activity and excellent selectivity with an insignificant barrier of only 0.05 eV along the distal pathway and a surmountable kinetic barrier of 0.34 eV. The W-BSb and Mo-BSb exhibit high performances with limiting potentials of-0.19 and-0.34 V. The Bader-charge descriptor reveals that the charge transfers from substrate to*NNH in the first protonation step and from*NH3 to substrate in the last protonation step, circumventing a big hurdle in NRR by achieving negative free energy change of*NH2 to*NH3. Furthermore, machine learning (ML) descriptors are introduced to reduce computational cost. Our rational design meets the three critical prerequisites of chemisorbing N2 molecules, stabilizing*NNH, and destabilizing*NH2 adsorbates for high-efficiency NRR.
UR - https://www.scopus.com/pages/publications/85131107556
U2 - 10.1021/acs.jpclett.2c00918
DO - 10.1021/acs.jpclett.2c00918
M3 - Article
C2 - 35576271
AN - SCOPUS:85131107556
SN - 1948-7185
VL - 13
SP - 4530
EP - 4537
JO - Journal of Physical Chemistry Letters
JF - Journal of Physical Chemistry Letters
IS - 20
ER -