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Regulating dopant depth in nitrogen-doped biphenylene nanoribbons for efficient oxygen reduction reaction.

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Title: Regulating dopant depth in nitrogen-doped biphenylene nanoribbons for efficient oxygen reduction reaction.
Authors: Li, Tianshu1 (AUTHOR) li_tianshu@stu.ujn.edu.cn, Shi, Hu2 (AUTHOR) hshi@sxu.edu.cn, Kang, Baotao1 (AUTHOR) chm_kangbt@ujn.edu.cn
Source: Journal of Colloid & Interface Science. Sep2026, Vol. 718, pN.PAG-N.PAG. 1p.
Subjects: Oxygen reduction, Electrocatalysts, Carbon nanomaterials, Machine learning, Density functional theory
Abstract: One-dimensional carbon nanostructures offer unique pathways to modulate the local electronic environments of catalytic sites, thereby circumventing the constraints of two-dimensional systems. Here, we identify dopant depth as a key structural descriptor governing the oxygen reduction reaction (ORR) activity in one-dimensional nitrogen-doped biphenylene nanoribbons (BPNNRs), by combining density functional theory (DFT) calculations with machine learning (ML) analysis. Nitrogen doping effectively regulates local charge distributions and optimizes *OOH adsorption, yielding a minimum overpotential of 0.384 V, which is comparable to that of 2D N-doped biphenylene and superior to benchmark Pt catalysts. Crucially, the spatial depth of the dopant relative to the ribbon edge emerges as a decisive activity descriptor: ORR performance systematically improves and converges toward the 2D limit as dopants migrate inward. ML analysis further identifies the coordination angle of the second shell and the nearest-neighbor Bader charge as primary features governing *OOH adsorption. These findings establish dopant-depth engineering as a rational strategy for designing high-performance metal-free electrocatalysts. [Display omitted] [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:One-dimensional carbon nanostructures offer unique pathways to modulate the local electronic environments of catalytic sites, thereby circumventing the constraints of two-dimensional systems. Here, we identify dopant depth as a key structural descriptor governing the oxygen reduction reaction (ORR) activity in one-dimensional nitrogen-doped biphenylene nanoribbons (BPNNRs), by combining density functional theory (DFT) calculations with machine learning (ML) analysis. Nitrogen doping effectively regulates local charge distributions and optimizes *OOH adsorption, yielding a minimum overpotential of 0.384 V, which is comparable to that of 2D N-doped biphenylene and superior to benchmark Pt catalysts. Crucially, the spatial depth of the dopant relative to the ribbon edge emerges as a decisive activity descriptor: ORR performance systematically improves and converges toward the 2D limit as dopants migrate inward. ML analysis further identifies the coordination angle of the second shell and the nearest-neighbor Bader charge as primary features governing *OOH adsorption. These findings establish dopant-depth engineering as a rational strategy for designing high-performance metal-free electrocatalysts. [Display omitted] [ABSTRACT FROM AUTHOR]
ISSN:00219797
DOI:10.1016/j.jcis.2026.140457