journal article·2026-03

Unlock the performance mechanism of N/P-doped g-SiC and Nb2CO2 monolayer materials as lithium/sodium-ion batteries

Zihan Qiu, Lingxia Li, Wenbo Zhang, Junqiang Ren, Xingchang Tang, Xuefeng Lu

Journal of Materials Science· Volume 61· Issue 9
Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Advancements in Battery Materials · MXene and MAX Phase Materials · Boron and Carbon Nanomaterials Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (40)

Show source-provided references
1. Cui K, Wang T, Zhang Q et al (2025) Multi‐functional descriptor design of V‐based double atomic catalysts for room temperature sodium‐sulfur batteries. Small 21(4):240986610.1002/smll.202409866 ↗
2. Li S, Zhang J, Li Y et al (2024) Revisiting N, S co-doped carbon materials with boosted electrochemical performance in sodium-ion capacitors: The manipulation of internal electric field. Nano Res Energy 3(1):912009810.26599/NRE.2023.9120098 ↗
3. Wang T, Xiao J, Cao X et al (2020) Interface effect on promoting reversible conversion for Na2 Se in the metal selenide as sodium ion batteries. J Energy Chem 44:8–1210.1016/j.jechem.2019.09.012 ↗
4. Gomez-Martin A, Gnutzmann MM, Adhitama E et al (2022) Opportunities and challenges of Li2C4O4 as pre-lithiation additive for the positive electrode in NMC622||Silicon/Graphite Lithium ion cells. Adv Sci 9(24):220174210.1002/advs.202201742 ↗
5. Niu B, Xiao J, Xu Z (2022) Advances and challenges in anode graphite recycling from spent lithium-ion batteries. J Hazard Mater 439:12967810.1016/j.jhazmat.2022.129678 ↗
6. Shen Y, Shen X, Yang M et al (2021) Achieving desirable initial coulombic efficiencies and full capacity utilization of Li-ion batteries by chemical prelithiation of graphite anode. Adv Funct Mater 31(24):210118110.1002/adfm.202101181 ↗
7. Goodenough JB, Park K-S (2013) The Li-ion rechargeable battery: a perspective. J Am Chem Soc 135(4):1167–117610.1021/ja3091438 ↗
8. Zhang H, Yang Y, Ren D et al (2021) Graphite as anode materials: fundamental mechanism, recent progress and advances. Energy Storage Mater 36:147–17010.1016/j.ensm.2020.12.027 ↗
9. Jiang M, Danilov DL, Eichel RA et al (2021) A review of degradation mechanisms and recent achievements for Ni-rich cathode-based Li-ion batteries. Adv Energy Mater 11(48):210300510.1002/aenm.202103005 ↗
10. Ahsan MA, He T, Noveron JC et al (2022) Low-dimensional heterostructures for advanced electrocatalysis: an experimental and computational perspective. Chem Soc Rev 51(3):812–82810.1039/D1CS00498K ↗
11. Gbadamasi S, Mohiuddin M, Krishnamurthi V et al (2021) Interface chemistry of two-dimensional heterostructures-fundamentals to applications. Chem Soc Rev 50(7):4684–472910.1039/D0CS01070G ↗
12. Majid A, Fatima A, Khan SU-D et al (2021) Layered silicon carbide: a novel anode material for lithium ion batteries. New J Chem 45(40):19105–1911710.1039/D1NJ04261K ↗
13. Wang H, Wu M, Lei X et al (2018) Siligraphene as a promising anode material for lithium-ion batteries predicted from first-principles calculations. Nano Energy 49:67–7610.1016/j.nanoen.2018.04.038 ↗
14. Afsharpour M, Behtooei H, Shakiba M et al (2022) Novel N, P, S co-doped graphenic SiC layers (g-SiC) in visible-light photodegradation of antibiotics and inactivating the bacteria. Process Saf Environ Prot 166:704–71710.1016/j.psep.2022.08.064 ↗
15. Du L, Duan H, Xia Q et al (2020) Hybrid charge-storage route to Nb2CTx MXene as anode for sodium-ion batteries. ChemistrySelect 5(3):1186–119210.1002/slct.201903888 ↗
16. Zhang W, Jin H, Du Y et al (2021) Sulfur and nitrogen codoped Nb2C MXene for dendrite-free lithium metal battery. Electrochim Acta 390:13881210.1016/j.electacta.2021.138812 ↗
17. Liu R, Cao W, Han D et al (2019) Nitrogen-doped Nb2CTx MXene as anode materials for lithium ion batteries. J Alloy Compd 793:505–51110.1016/j.jallcom.2019.03.209 ↗
18. Ji H, Rágyanszki A, Fournier RA (2023) Machine learning estimation of reaction energy barriers. Comput Theor Chem 1229:11433210.1016/j.comptc.2023.114332 ↗
19. Focassio B, Domina M, Patil U et al (2023) Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations. NPJ Comput Mater 9(1):8710.1038/s41524-023-01053-0 ↗
20. Zhou D, Jin L, Ji Y et al (2024) Rational design of TWO-DIMENSIONAL MA2Z4 monolayers as effective anchoring materials for Lithium-Sulfur batteries. ACS Appl Mater Interfaces 16(45):62213–6222110.1021/acsami.4c15239 ↗
21. Shi Y, Xue H, Mersal GA et al (2025) Rhenium-repulsion induced desegregation of nonmetallic impurities and de-embrittling effect on the Ni Σ11 [110](113) grain boundary. Adv Compos Hybrid Mater 8(1):1–1310.1007/s42114-024-01183-z ↗
22. Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77(18):386510.1103/PhysRevLett.77.3865 ↗
23. Zhao X, Wang P, Lv E et al (2021) Screening MXenes for novel anode material of lithium-ion batteries with high capacity and stability: A DFT calculation. Appl Surf Sci 569:15105010.1016/j.apsusc.2021.151050 ↗
24. Ma S, Zhang H, Cheng Z et al (2023) A novel two-dimensional whorled TiB4 as a high-performance anode material for Li-ion and Na-ion batteries. Appl Surf Sci 639:15808310.1016/j.apsusc.2023.158083 ↗
25. Lin H, Zhang Y, Huang Y (2024) Unlocking the intercalation mechanism of blue phosphorene/MBene heterostructures for lithium/sodium-ion batteries: a first principles study. J Mol Liq 409:12552810.1016/j.molliq.2024.125528 ↗
26. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process-Lett Rev 11(10):203–224
27. Friedman J H (2001) Greedy function approximation: a gradient boosting machine. Annals of statistics: 1189–123210.1214/aos/1013203451 ↗
28. Nalluri M, Pentela M, Eluri N R (2020) A scalable tree boosting system: XG boost. Int. J. Res. Stud. Sci. Eng. Technol 7(12): 36–51
29. Breiman L (2001) Random forests. Mach Learn 45:5–3210.1023/A:1010933404324 ↗
30. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J mach Learn res 12:2825–2830
31. Lin S, Zhang S, Li X et al (2015) Quasi-two-dimensional SiC and SiC2: interaction of silicon and carbon at atomic thin lattice plane. J Phys Chem C 119(34):19772–1977910.1021/acs.jpcc.5b04113 ↗
32. Bijoy T, Murugan P (2019) Lithiation of the two-dimensional silicon carbide-graphene van der waals heterostructure: a first principles study. J Phys Chem C 123(17):10738–1074510.1021/acs.jpcc.8b12492 ↗
33. Şahin H, Cahangirov S, Topsakal M et al (2009) Monolayer honeycomb structures of group-IV elements and III-V binary compounds: first-principles calculations. Phys Rev B 80(15):15545310.1103/PhysRevB.80.155453 ↗
34. Zeng S, Huang X, Ma Y et al (2021) A review of covalent organic framework electrode materials for rechargeable metal-ion batteries. New Carbon Mater 36(1):1–1810.1016/S1872-5805(21)60001-X ↗
35. Cui Z, Guo X, Ren J et al (2021) Enhanced electrochemical performance and storage mechanism of LiFePO4 doped by Co, Mn and S elements for lithium-ion batteries. Electrochim Acta 388:13859210.1016/j.electacta.2021.138592 ↗
36. Xiong F, Chen Y (2020) A first-principles study of Janus monolayer TiSSe and VSSe as anode materials in alkali metal ion batteries. Nanotechnology 32(2):02570210.1088/1361-6528/abb7b4 ↗
37. Tang P, Chen T, Yang W et al (2025) Theoretical insights of 2D Fe3GeTe2/In2Se3 multiferroic vdW heterostructure based gas sensors for high-performance NO detecting. Colloids Surf A Physicochem Eng Asp 705:13561110.1016/j.colsurfa.2024.135611 ↗
38. Flexa C, Gomes W, Moreira I et al (2021) Polygonal coordinate system: visualizing high-dimensional data using geometric DR, and a deterministic version of t-SNE. Expert Syst Appl 175:11474110.1016/j.eswa.2021.114741 ↗
39. Jie J, Hu Z, Qian G et al (2019) Discovering unusual structures from exception using big data and machine learning techniques. Sci Bull 64(9):612–61610.1016/j.scib.2019.04.015 ↗
40. Zhuo Y, Mansouri Tehrani A, Brgoch J (2018) Predicting the band gaps of inorganic solids by machine learning. J Phys Chem Lett 9(7):1668–167310.1021/acs.jpclett.8b00124 ↗