A DFT Study of CO, H2, C2H2 and CH4 Adsorption onto SnS2-Based Monolayers: Favorable Sensitivity and Selectivity by Doping Single Pd or Pt Atoms
Abstract
1. Introduction
2. Results and Discussion
3. Calculation Method
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ou, X.; Zhou, D.; Lin, C.; Zhu, L. A review of research on the ageing and service life assessment of oil-immersed power transformers. South. Power Grid Technol. 2015, 9, 58–70. [Google Scholar]
- Li, X.; Tang, Y.; Zhan, J.; Ye, J.; Fan, X.; Li, L. Research on the Application of Fibre Bragg Grating Sensors in Multi-parameter Intelligent Monitoring of Transformers. Prot. Control Power Syst. 2023, 51, 154–160. [Google Scholar]
- Su, L.; Chen, L.; Xu, P.; Lin, J.; Sheng, G.; Jiang, X. Analysis of Transformer Operating Conditions Based on Deep Belief Networks. High-Volt. Appar. 2021, 57, 56–62. [Google Scholar]
- Han, X.; Song, W.; Liu, Y.; Liu, J.; Cheng, S. Analysis of Magnetostriction and Vibration Noise Characteristics of Transformer Cores Made from Different Ferromagnetic Materials. Large Electr. Mach. Technol. 2023, 1, 68–73. [Google Scholar]
- Xia, J.; Miao, S.; Shao, W.; Yang, X.; Xu, Y. Virtual Phase Protection for Transformers Based on Longitudinal Impedance. J. Xi’an Univ. Eng. 2023, 37, 54–62. [Google Scholar]
- Wang, H.; Yao, H.; Guo, Q.; Yu, S.; Zhang, X.; Chong, L. A Transformer Fault Diagnosis Method Based on Multi-Scale Convolutional Neural Networks. J. Electr. Power Sci. Technol. 2023, 38, 104–112. [Google Scholar]
- Mehdi, B.; Lu, M.; Naderi, M.S.; Phung, B.T. Transformer frequency response: A new technique to analyze and distinguish the low-frequency band in the frequency response analysis spectrum. IEEE Electr. Insul. Mag. 2018, 34, 39–49. [Google Scholar] [CrossRef]
- Hu, B.; Deng, X.; Jia, S. Transformer Life Prediction and Condition Assessment Based on ANFIS. Electr. Meas. Instrum. 2022, 59, 61–68. [Google Scholar]
- Qi, B.; Wang, Y.; Zhang, P.; Li, C.; Wang, H. Deep Recurrent Belief Networks for Predicting Trends in Transformer Oil Chromatography. Power Syst. Technol. 2019, 43, 1892–1899. [Google Scholar]
- Cui, H.; Zhang, X.X.; Zhang, G.Z.; Tang, J. Pd- doped MoS2 monolayer: A promising candidate for DGA in transformer oil based on DFT method. Appl. Surf. Sci. 2019, 470, 1035–1042. [Google Scholar] [CrossRef]
- Li, Y.; Li, X.; Sun, W.; Li, R.; Lin, J.; Zhang, D.; Zhang, G. A fault diagnosis method for oil-immersed power transformers based on multi-model cascading. Smart Power 2023, 51, 86–92. [Google Scholar]
- Jia, D.; Han, X.; Dong, X.; Yi, S.; Guo, X. Study on the Adsorption Performance of Dissolved Gases in Transformer Oil Using Pt-C3N Sensors. Smart Power 2024, 52, 40–46+61. [Google Scholar]
- Fu, W.J.; Zhao, X.; Zheng, W. Growth of vertical graphene materials by an inductively coupled plasma with solid-state carbon sources. Carbon 2021, 173, 91–96. [Google Scholar] [CrossRef]
- Wang, Y.J.; Zhou, W.Z.; Cao, K.; Hu, X.; Gao, L.; Lu, Y. Architectured graphene and its composites: Manufacturing and structural applications. Compos. Part. A Appl. Sci. Manuf. 2021, 140, 106177. [Google Scholar] [CrossRef]
- Li, G.Y.; Feng, H.F.; Shi, X.H.; Chen, M.; Liang, J.; Zhou, Z. Highly sensitive electrochemical aptasensor for Glypican-3 based on reduced graphene oxide-hemin nanocomposites modified on screen-printed electrode surface. Bioelectrochemistry 2021, 138, 107696. [Google Scholar] [CrossRef] [PubMed]
- Song, X.F.; Hu, J.L.; Zeng, H.B. Two-dimensional semiconductors: Recent progress and future perspectives. J. Mater. Chem. C 2013, 1, 2952–2969. [Google Scholar] [CrossRef]
- Yin, S.K.; Sun, L.L.; Zhou, Y.J.; Li, X.; Li, J.; Song, X.; Huo, P.; Wang, H.; Yan, Y. Enhanced electronhole separation in SnS2/Au/g-C3N4 embedded structure for efficient CO2 photoreduction. Chem. Eng. J. 2021, 406, 126776. [Google Scholar] [CrossRef]
- Shan, W.; Fu, Z.; Zhang, F.; Ma, M.; Liu, Z.; Li, Y. Preparation of SnS2 nanosheets and their application in NO2 gas detection. J. Inorg. Mater. 2020, 35, 497–504. [Google Scholar]
- Jin, Y.; Jin, J.; Li, W.; Ren, Q.; Xu, W.; Li, J. First-principles study of gas molecule adsorption on Ti-doped SnS2. Micro-Nano Electron. Technol. 2024, 61, 122–131. [Google Scholar]
- Gui, Y.G.; Li, T.; He, X.; Ding, Z.; Yang, P. Pt cluster modified h- BN for gas sensing and adsorption of dissolved gases in transformer oil: A density functional theory study. Nanomaterials 2019, 132, 1145–1154. [Google Scholar] [CrossRef]
- Fan, Q.; Hou, P.F.; Choi, C.H.; Wu, T.-S.; Hong, S.; Li, F.; Soo, Y.-L.; Kang, P.; Jung, Y.; Sun, Z. Activation of Ni Particles into single Ni-N atoms for efficient electrochemical reduction of CO2. Adv. Energy Mater. 2020, 54, 101–113. [Google Scholar] [CrossRef]
- Wang, Y.; Yang, X.; Hu, C.; Wu, T. Rh-doped ZnO monolayer as a potential gas sensor for air decomposed species in a ring main unit: A first- principles study. Acs Omega 2021, 28, 15878–15884. [Google Scholar] [CrossRef] [PubMed]
- Tian, Z.; Yang, L.; Zhang, Y.; Dong, Y.; Zhao, W.; Yang, H. SO2 Adsorption Properties of Non-Metal Doped Single-Layer WSe2: A First-Principles Study. Surf. Sci. 2025, 763, 122851. [Google Scholar] [CrossRef]
- Kresse, G.; Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys. Rev. B Condens. Matter 1994, 49, 14251–14269. [Google Scholar] [CrossRef]
- Perdew, J.P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865–3868. [Google Scholar] [CrossRef]
- Blochlp, E. Projector augmented-wave method. Phys. Rev. B 1994, 50, 17953–17979. [Google Scholar] [CrossRef] [PubMed]
- Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 2010, 132, 154104. [Google Scholar] [CrossRef] [PubMed]
- Kresse, G.; Furthmullerr, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 1996, 54, 11169–11186. [Google Scholar] [CrossRef]
- Vydrov, O.A.; Heyd, J.; Krukau, A.V.; Scuseria, G.E. Importance of short-range versus long-range Hartree-Fock exchange for the performance of hybrid density functionals. J. Chem. Phys. 2006, 125, 074106. [Google Scholar] [CrossRef]





| Systems | Eads (eV) | Qt (e) | D (Å) | |
|---|---|---|---|---|
| C2H2 | SnS2 | −0.007 | −0.090 | 2.918 |
| Pd-SnS2 | −0.980 | −0.260 | 2.130 | |
| Pt-SnS2 | −0.718 | −0.290 | 1.984 | |
| CH4 | SnS2 | −0.027 | −0.060 | 0.892 |
| Pd-SnS2 | −0.521 | −0.230 | 1.905 | |
| Pt-SnS2 | −0.099 | −0.180 | 1.909 | |
| CO | SnS2 | −0.005 | 0 | 1.280 |
| Pd-SnS2 | −1.246 | −0.010 | 1.733 | |
| Pt-SnS2 | −0.866 | −0.010 | 1.704 | |
| H2 | SnS2 | −0.008 | −0.050 | 0.974 |
| Pd-SnS2 | −0.111 | −0.140 | 1.397 | |
| Pt-SnS2 | −0.165 | −0.170 | 1.387 | |
| Systems | 298 K | 398 K | 498 K | |
|---|---|---|---|---|
| CH4 | SnS2 | 1.31 × 10−12 | 1.23 × 10−12 | 1.18 × 10−12 |
| Pd/SnS2 | 3.70 × 104 | 2.54 | 8.21 × 10−3 | |
| Pt/SnS2 | 1.38 | 1.23 × 10−3 | 1.84 × 10−4 | |
| C2H2 | SnS2 | 2.86 × 10−12 | 2.20 × 10−12 | 1.88 × 10−12 |
| Pd/SnS2 | 6.43 × 10−4 | 3.94 × 10−6 | 1.87 × 10−7 | |
| Pt/SnS2 | 4.72 × 10−11 | 1.79 × 10−11 | 1.01 × 10−11 | |
| CO | SnS2 | 1.21 × 10−12 | 1.16 × 10−12 | 1.12 × 10−12 |
| Pd/SnS2 | 1.16 × 109 | 5.93 × 103 | 4.03 | |
| Pt/SnS2 | 4.38 × 102 | 9.17 × 10−2 | 5.77 × 10−4 | |
| H2 | SnS2 | 1.37 × 10−12 | 1.26 × 10−12 | 1.20 × 10−12 |
| Pd/SnS2 | 7.53 × 10−11 | 2.54 × 10−11 | 1.33 × 10−11 | |
| Pt/SnS2 | 6.16 × 10−10 | 1.23 × 10−10 | 4.67 × 10−11 | |
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Cheng, W.; Pan, H.; Zhang, Y.; Ni, J. A DFT Study of CO, H2, C2H2 and CH4 Adsorption onto SnS2-Based Monolayers: Favorable Sensitivity and Selectivity by Doping Single Pd or Pt Atoms. Molecules 2026, 31, 2062. https://doi.org/10.3390/molecules31122062
Cheng W, Pan H, Zhang Y, Ni J. A DFT Study of CO, H2, C2H2 and CH4 Adsorption onto SnS2-Based Monolayers: Favorable Sensitivity and Selectivity by Doping Single Pd or Pt Atoms. Molecules. 2026; 31(12):2062. https://doi.org/10.3390/molecules31122062
Chicago/Turabian StyleCheng, Wenming, Hao Pan, Yuxing Zhang, and Jiaming Ni. 2026. "A DFT Study of CO, H2, C2H2 and CH4 Adsorption onto SnS2-Based Monolayers: Favorable Sensitivity and Selectivity by Doping Single Pd or Pt Atoms" Molecules 31, no. 12: 2062. https://doi.org/10.3390/molecules31122062
APA StyleCheng, W., Pan, H., Zhang, Y., & Ni, J. (2026). A DFT Study of CO, H2, C2H2 and CH4 Adsorption onto SnS2-Based Monolayers: Favorable Sensitivity and Selectivity by Doping Single Pd or Pt Atoms. Molecules, 31(12), 2062. https://doi.org/10.3390/molecules31122062
