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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]
Copyright of Journal of Colloid & Interface Science is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193619304
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PubTypeId: academicJournal
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  Data: Regulating dopant depth in nitrogen-doped biphenylene nanoribbons for efficient oxygen reduction reaction.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Tianshu%22">Li, Tianshu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> li_tianshu@stu.ujn.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shi%2C+Hu%22">Shi, Hu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hshi@sxu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Kang%2C+Baotao%22">Kang, Baotao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chm_kangbt@ujn.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Colloid+%26+Interface+Science%22">Journal of Colloid & Interface Science</searchLink>. Sep2026, Vol. 718, pN.PAG-N.PAG. 1p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Oxygen+reduction%22">Oxygen reduction</searchLink><br /><searchLink fieldCode="DE" term="%22Electrocatalysts%22">Electrocatalysts</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+nanomaterials%22">Carbon nanomaterials</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Density+functional+theory%22">Density functional theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Colloid & Interface Science is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.jcis.2026.140457
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Oxygen reduction
        Type: general
      – SubjectFull: Electrocatalysts
        Type: general
      – SubjectFull: Carbon nanomaterials
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Density functional theory
        Type: general
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      – TitleFull: Regulating dopant depth in nitrogen-doped biphenylene nanoribbons for efficient oxygen reduction reaction.
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            NameFull: Li, Tianshu
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            NameFull: Shi, Hu
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            NameFull: Kang, Baotao
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            – D: 15
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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              Value: 718
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