DOI:
10.1039/D5SC02602D
(Edge Article)
Chem. Sci., 2025,
16, 13382-13389
Controllable construction of cobalt nanoparticles in nitrogen-doped carbon nanotubes for photothermal CO2 methanation†
Received
8th April 2025
, Accepted 17th June 2025
First published on 17th June 2025
Abstract
The development of non-noble metal catalysts for efficient CO2 methanation reaction under mild conditions remains a significant challenge. Herein, a non-noble metal catalyst, cobalt nanoparticles (Co NPs) encapsulated within the hollow channels of nitrogen-doped carbon nanotubes (Co@CN-700), was prepared by a pyrolysis-reduction strategy for photothermal CO2 methanation. Remarkably, the Co@CN-700 catalyst achieved a prominent CH4 production rate of 199.4 mmol gcat−1 h−1 with near-unity selectivity (99.4%) and high CO2 conversion (85.8%) at 250 °C, which is outstanding compared to the catalysts reported. The electromagnetic simulation and density functional theory calculations demonstrated that the plasmonic resonance effect of Co NPs enhances the local electric field and thereby alters the intermediate states and rate-limiting step to facilitate CO2 methanation. This work offers a straightforward and effective approach for designing non-noble metal catalysts with high activity, selectivity, and stability.
Introduction
Use of fossil fuels releases a large amount of CO2, resulting in environmental and climate problems, and much attention has been paid to develop effective mitigation strategies. A promising approach involves converting CO2 with hydrogen into valuable chemicals and fuels such as methane.1–4 The produced methane can be readily integrated into existing natural gas pipeline networks, enabling commercial-scale deployment.5–8 Additionally, this approach offers a practical solution for hydrogen storage, addressing the technical challenges associated with hydrogen storage, transportation, and large-scale utilization.
CO2 methanation, commonly referred as the Sabatier reaction, is an exothermic process that is thermodynamically favored at low temperatures.9–11 Over the past decade, various heterogeneous catalysts have been studied to enhance the efficiency of photothermal CO2 methanation.12–17 Noble metal catalysts have attracted significant attention due to their excellent catalytic performance.15,18,19 For instance, Li et al. developed a Ru@Ni2V2O7 catalyst that achieved a methanation rate of 114.9 mmol gcat−1 h−1 at 350 °C.20 Recently, Zhong et al.15 reported an Au/Ce0.95Ru0.05O2 solid solution catalyst for the photothermal Sabatier reaction with a methane generation rate of 473 mmol gcat−1 h−1. However, the high cost of noble metals limits their industrial applications, highlighting the need for alternative non-noble metal catalysts with comparable activity and stability. Nickel-based catalysts have shown promise in CO2 methanation.21–23 By optimizing the size of Ni nanoparticles and tailoring metal–support interactions, a CH4 production rate of 63 mmol g−1 h−1 with a selectivity of 99.8% was achieved.21 Cobalt-based catalysts have also been extensively studied for CO2 methanation.24–29 However, their performance has been hindered by low CH4 production rates, typically below 20 mmol g−1 h−1 (ESI Table S1†), and the formation of undesirable by-products. Improving the activity of non-noble metal catalysts and minimizing by-product generation remain critical challenges for achieving efficient solar-driven Sabatier reactions.
Here, we report a catalyst (Co@CN-700) where carbon nanotubes serve as anchors for uniformly dispersed Co sites, facilitating the catalytic hydrogenation of carbon dioxide to methane with high selectivity. The Co@CN-700 catalyst achieved a very high photothermal CH4 production rate of 199.4 mmol gcat−1 h−1, with near-unity selectivity (99.4%), and a CO2 conversion of 85.8%. The excellent physical and chemical adsorption of CO2 could induce CO2 coverage on the catalyst surface and accelerate the methanation reaction. The associated spectral characterization and theoretical calculations showed that the synergistic effect of light energy and heat energy accelerates the conversion of *COOH to *CO species, thereby increasing methanation activity.
Results and discussion
Synthesis and characterization of catalysts
The Co@CN-x (x denotes pyrolysis temperature) catalysts were synthesized by hydrothermal processing, followed by metal deposition and pyrolysis treatment, as outlined in Fig. 1a (detailed synthesis procedures are available in the ESI section†). Inductively coupled plasma optical emission spectrometry (ICP-OES) showed that the Co loading on CN ranged from 6.3% to 11.3% (ESI Table S2†). X-ray diffraction (XRD) patterns revealed no distinct Co metal peaks in the Co@CN-500 sample (Fig. 1b), indicating that Co species are highly dispersed on CN. As pyrolysis temperature increased from 500 to 800 °C, Co metal (PDF# 15-0806) peaks became more pronounced. Besides, Raman spectra (ESI Fig. S1†) showed that Co@CN-700 (ID/IG = 2.02) exhibited fewer defects than CN-700 (ID/IG = 2.41), implying that Co species occupied defective carbon sites. N2 adsorption–desorption isotherms (ESI Fig. S2†) show that the Co@CN-x catalysts comprised micropores and mesopores, with a Brunauer–Emmett–Teller (BET) surface area of approximately 400 m2 g−1 (ESI Table S2†), potentially facilitating the diffusion of reactive molecules and exposing active sites. Scanning and transmission electron microscope (SEM/TEM) showed that all Co@CN-x and CN samples exhibit hollow nanotube structures with surface wrinkles (Fig. 1c, d and ESI Fig. S3–S6†). The average Co nanoparticle size increased from 5.8 to 8.6 nm and then to 20.7 nm with increasing pyrolysis temperatures from 600 to 800 °C (Fig. 1d and ESI Fig. S7 and S8†). Energy-dispersive X-ray spectroscopy (EDS) mapping confirmed uniform N doping on the graphene support (Fig. 1e), while Co species were highly dispersed within the graphene nanotube pores.
 |
| Fig. 1 Preparation and characterisation of the catalysts. (a) Schematic diagram of catalyst synthesis and preparation; (b) XRD patterns of various Co-based catalysts; (c) SEM images of the Co@CN-700 sample; (d) TEM images of Co@CN-700 (inset: the average distribution of Co NP size); (e) EDS mapping images of Co@CN-700; (f) high-resolution N 1s XPS spectra of CN-700 and Co@CN-700 catalysts; (g) XANES spectra of Co@CN-600, Co@CN-700, Co@CN-800, and reference samples; (h) EXAFS spectra of Co@CN-600, Co@CN-700, Co@CN-800, and reference samples in R space. | |
The N 1s XPS spectra showed peaks at 401.0 and 398.6 eV, corresponding to graphitic-N and pyridinic-N,12 respectively (Fig. 1f). X-ray absorption fine structure spectroscopy (XAFS) was applied to investigate the local environment of Co species. The X-ray absorption near-edge structure (XANES) results revealed that the intensity and edge energy of the Co@CN-600 catalyst were higher than those of Co foil but lower than those of CoO (Fig. 1g), indicating a partial positive charge. With an increase in reduction temperature from 600 to 700 °C, intensity and the edge energy increased, suggesting that Co species were approaching zero valence. A further increase in reduction temperature to 800 °C did not markedly change the intensity. Extended X-ray absorption fine structure (EXAFS) spectra and fitting data are presented in Fig. 1h and ESI Fig. S9–S14.† In the R space, Co–Co scattering (2.49 Å) increased notably with higher reduction temperatures, indicating substantial changes in the coordination environment of Co species.30 This result was further corroborated by Co K-edge wavelet transform (WT)-EXAFS analysis (ESI Fig. S15†). The coordination number of Co–Co increased with increasing Co particle size, from 4.2 (Co@CN-600) to 5.5 (Co@CN-700) and 7.7 (Co@CN-800) (details in ESI Table S3†).
Catalytic performances of Co@CN catalysts for CO2 methanation
The CO2 methanation performance of Co@CN catalysts was evaluated in a photothermal reactor with an external heating system. The reaction temperature was controlled by external heating in combination with irradiation from an Xe lamp (ESI Fig. S16 and S17†). Reaction parameters, including the H2/CO2 ratio, pressure, and irradiation time, were optimized for CO2 methanation (ESI Fig. S18–S20†). The optimized conditions (H2/CO2 ratio of 4, a pressure of 1 MPa, and an irradiation time of 4 h) were then applied for the catalytic studies. CO2 methanation activity was tested across Co@CN-x catalysts prepared at different pyrolysis/reduction temperatures (Fig. 2a and b). The catalytic activity displayed a volcano-like trend in relation to the pyrolysis/reduction temperature. Notably, Co@CN-700 achieved a CH4 production rate of 199.4 mmol gcat−1 h−1 with nearly 100% selectivity, setting a new benchmark in photothermal CO2 methanation activity for non-noble metal catalysts (ESI Table S1†). Moreover, no liquid products were detected in 1H NMR spectroscopy (ESI Fig. S21†).
 |
| Fig. 2 Photothermal catalytic performance. (a) CH4 evolution rate over Co@CN-x catalysts; (b) product distribution over Co@CN-x catalysts; (c) control experiments for the Co@CN-700 sample under different conditions. Reaction conditions: 15 mg catalyst, full-arc 300 W UV–xenon lamp, 2.5 W cm−2, 250 °C, irradiation time 4 hours, initial pressure 1 MPa (H2/CO2 = 4/1). (d) CH4 evolution rate over different catalysts; (e) product distribution over different catalysts; (f) temperature-dependent CH4 generation rate over Co@CN-700 under photothermal and thermal conditions; (g) influence of light intensity on the CH4 evolution rate over Co@CN-700; (h) the impact of various light wavelengths on the catalytic performance of the Co@CN-700 catalyst; (i) catalytic stability tests of Co@CN-700 under photothermal conditions. Reaction conditions: 15 mg of catalyst, full-arc 300 W UV–xenon lamp, 2.5 W cm−2, 250 °C, irradiation time 4 h, initial pressure 1 MPa (H2/CO2 = 4/1). | |
Controlled catalytic experiments in the absence of CO2 or H2 revealed that no CH4 or other carbonaceous products were detected (Fig. 2c), which proved that the products originated from CO2 during the reaction. Tests with no catalyst, no thermal input, or no light showed either no or reduced activity compared to the photothermal catalysis on Co@CN-700 (Fig. 2c). These indicate that both the catalysts and photothermal treatments are essential for achieving high CO2 methanation activity and selectivity. The results (ESI Fig. S22†) showed that the CH4 production rates were 1.28, 0.26 and 0.05 mmol g−1 h−1 over the Co-30, Co-50 and Co-100 samples, which were much lower than the catalytic activity of Co@CN-700. This shows that the size of Co NPs and the support have important roles in the catalytic performance. CH4 production rates of 90.1 and 199.4 mmol gcat−1 h−1 were observed for Co@C-700 and Co@CN-700 catalysts, respectively (Fig. 2d). CH4 selectivity increased from 23.5% for Co-30 to 95.7% for Co@C-700 and reached 99.6% for Co@CN-700 (Fig. 2e). Furthermore, Cu@CN-700 and Fe@CN-700 exhibited negligible photothermal catalytic activity, underscoring the superior catalytic performance of Co in this study.
Compared to pure thermo-catalysis, the CH4 production rate increased significantly when photon energy was coupled with external thermal energy (Fig. 2f), suggesting that photon energy injection effectively enhances CO2 methanation. The relationship between photothermal methanation activity and light intensity was investigated on Co@CN-700, indicating a substantial increase in the CO2 methanation rate at higher light intensity (Fig. 2g). To evaluate the contribution of different wavelengths, CO2 methanation was conducted under UV, visible (Vis), near-infrared (NIR), and Vis-NIR irradiation (Fig. 2h). The CH4 production rate increased by 16% under UV light (200–400 nm, 126.4 mmol gcat−1 h−1) compared to the dark conditions (108.9 mmol gcat−1 h−1) and by 24% under NIR light (760–1100 nm, 135.3 mmol gcat−1 h−1). Notably, vis light (400–760 nm) led to a 48% increase in the CH4 production rate (161 mmol gcat−1 h−1) over dark conditions, suggesting that the photon energy of visible light plays a dominant role in the photothermal process. These findings demonstrated that the light of different wavelengths displays promotion in CO2 methanation, emphasizing the critical role of photon energy strength in the photothermal reactions.
The stability of Co@CN-700 was studied by cycling tests, in which the CH4 production rate exceeded 160 mmol gcat−1 h−1 and CH4 selectivity was maintained above 99% over nine cycles (Fig. 2i). In order to investigate the slight deactivation of the catalyst, XPS, SEM and TEM were performed. The XPS results (ESI Fig. S25†) showed that the valence states of Co, C and N in the catalyst did not change obviously compared with the fresh catalyst. The SEM results (ESI Fig. S24†) demonstrated that the morphology of the catalyst remained unchanged after utilization. The above results show that the morphology and valence state of the catalyst were not the cause of deactivation. However, the TEM results (ESI Fig. S26†) demonstrated that, in comparison with the particle size of the fresh catalyst (8.6 nm), the particle size of Co increased to 13.2 nm after nine cycles. This finding suggests that the gradual increase in particle size may be the primary factor contributing to the slow deactivation of the catalyst.
Roles of active sites and support of Co@CN catalysts
To elucidate the adsorption properties of Co@CN catalysts, CO2 physical adsorption and CO2 temperature-programmed desorption (CO2-TPD) experiments were conducted. Fig. 3a illustrates that the Co@CN-700 sample exhibited the highest CO2 physical adsorption capacity, with the CN-700 sample showing only a slight decrease in this capacity. This result suggests that CO2 physical adsorption capacity mainly originated from the porous, hollow nanotube structures with surface wrinkles. Interestingly, the CO2 physical adsorption capacity followed the order Co@CN-700 > Co@CN-600 > Co@CN-800 > Co@CN-500, which corresponds directly to the observed methane production capacity.
 |
| Fig. 3 Physical and chemical adsorption of CO2 and CO on catalysts. (a) CO2 adsorption on various Co-based catalysts; (b) CO2-TPD profiles of various Co-based catalysts; (c) CO-TPD profiles of various Co-based catalysts; optimized configuration for CO2 adsorption on the CN substrate (d) and Co@CN surface (e); optimized configuration for CO adsorption on the CN substrate (f) and Co@CN surface (g). | |
The CO2-TPD profiles in Fig. 3b display a pronounced peak between 400 and 450 °C for all Co@CN-X catalysts, while the CN-700 sample displayed no detectable CO2 desorption peaks, indicating that efficient CO2 chemical adsorption occurs specifically on Co sites. Furthermore, density functional theory (DFT) calculations were performed to study adsorption behavior of CO2 on various surfaces. The calculated adsorption energies of CO2 on the nitrogen-doped carbon substrates and Co@CN-700 are 0.015 eV and −2.46 eV, respectively (Fig. 3d and e), confirming that Co sites significantly enhance CO2 adsorption, consistent with the CO2-TPD results.
Given that CO is an intermediate in the CO2 methanation, it is crucial to investigate adsorption behavior of CO on different catalysts. The CO-TPD profile (Fig. 3c) for the Co@CN-700 sample shows a strong CO desorption peak at 432 °C, while the CN-700 sample exhibited no CO desorption peaks, suggesting that efficient CO chemical adsorption occurs on Co sites. DFT calculations revealed that the adsorption energies of CO on the nitrogen-doped carbon substrate and Co@CN-700 catalyst were −0.024 eV and −1.60 eV, respectively (Fig. 3f and g). The above results demonstrate that the N-doped graphene nanotube structure enhances the local concentration of CO2 near the catalyst via physical adsorption, followed by further capture through chemical adsorption at the Co sites. Concurrently, H2 is efficiently activated at these Co sites,31,32 initiating the hydrogenation process by reacting with the adsorbed CO2 on the surface. Moreover, the Co@CN-700 catalyst exhibits substantially improved CO adsorption capacity, which retards CO desorption from the catalyst surface and improves methane selectivity.
Light and thermal properties of Co@CN catalysts
The efficient utilization of light is a critical factor that influences photothermal catalytic activity. To study light absorption capacity, the UV–Vis–NIR absorption spectra of various samples were measured (Fig. 4a). The CN-700 sample demonstrated a broad absorption peak from 250 to 2500 nm, implying strong broad spectrum light adsorption. The Co@CN-700 sample exhibited even stronger light absorption across the entire solar spectral range, attributed to the localized surface plasmon resonance (LSPR) effect of Co metal.33 This result indicates that metal-carbon composites enhance the light absorption properties of the catalysts via the plasmon–photon coupling. In addition, Co@CN-700 displays a more pronounced photothermal effect than CN-700 when irradiated at a power density of 2.5 W cm−2 (Fig. 4b and ESI Fig. S27†).34,35 A three-dimensional finite difference time domain (FDTD) simulation was conducted to simulate the local electric field distribution in the catalysts. Under single-wavelength light (700 nm), the electric field distribution appeared uneven, with peak intensities on the surfaces of metal nanoparticles and an exponential decrease with distance from the surface (Fig. 4d). However, the local electric field intensity induced by LSPR significantly increased upon the incorporation of Co nanoparticles and a carbon layer, indicating an enhancement in hot electron production capabilities. These hot electrons can transfer to the adsorbed reactant molecules, thereby facilitating their activation at the nanoparticle–molecule interface.36–38 This process increases the probability of electronic or vibrational transitions in the adsorbed molecules, ultimately accelerating the chemical reaction.33
 |
| Fig. 4 Light and thermal properties of catalysts. (a) UV–vis–IR absorption spectra of the CN-700 and Co@CN-700 samples. (b) Surface temperature of different samples collected using an infrared thermometer. Spatial distribution of electric field intensity induced by the localized surface plasmon resonance, from FDTD simulation of (c) 8.6 nm Co NPs, and (d) composite of Co NPs (8.6 nm) and a carbon layer (1 nm). | |
Reaction mechanism
To get insight into the reaction mechanism of CO2 methanation under photothermal conditions, in situ Fourier-transform infrared spectroscopy (FTIR) was implemented to detect surface chemical intermediates. Under thermal catalysis (Fig. 5a and b), vibration peaks at 1508 (m-CO32−), 1543 (COOH*), and 2096 cm−1 (*CO)21,35 became more prominent as the temperature increased from 100 °C to 250 °C. Notably, a peak at 3016 cm−1, corresponding to CH4 appeared at 250 °C and intensified over time (Fig. 5b). In photothermal catalysis, the FTIR results showed similar peaks for COOH*, m-CO32−, and the *CO intermediate (Fig. 5c and d). However, the bands at 1305 (bending vibration of C–H) and 3016 cm−1 (stretching vibration of C–H)39 assigned to CH4 emerged at 140 °C, implying that CO2 methanation can proceed at lower temperatures under photothermal conditions. Additionally, the intensities of intermediate peaks in photothermal catalysis were consistently higher than those observed in pure thermal catalysis.
 |
| Fig. 5 Proposed reaction mechanism. In situ FTIR spectra of thermal (a and b) and photothermal (c and d) methanation reactions over Co@CN-700. Reaction conditions: full-spectrum light irradiation, a H2 and CO2 (50/50 vol%) atmosphere; (e) free energy of Co@CN-700 at the ground state and electron-rich state, respectively; (f) proposed photothermal catalytic reaction mechanism for CO2 hydrogenation. | |
First-principles density functional theory (DFT) calculations40,41 were conducted for both the ground and the electron-rich states (Fig. 5e). In the ground state, the rate-limiting step is the conversion of *COOH to *CO, with a barrier energy of 0.13 eV. In the electron-rich state, the conversion of *COOH to *CO becomes more favorable, while the rate-limiting step shifts to the conversion of *CO2 to *COOH, which requires a lower barrier energy of 0.11 eV. This phenomenon can be attributed to the direct involvement of hot electrons in the reaction, altering the intermediate states and facilitating subsequent steps (Fig. 5f). Consequently, the intensified local electric field, induced by anchoring of Co nanoparticles on carbon nanotubes, enhanced CO2 methanation by promoting the conversion of *COOH to *CO.
Conclusions
In summary, a collaborative photothermal catalytic strategy was developed to enhance the efficiency of CO2 hydrogenation to CH4. The Co@CN-700 catalyst achieved record-breaking performance, with a CH4 production rate of 199.4 mmol gcat−1 h−1 and a selectivity of 99.4% at 250 °C. The LSPR effect of Co nanoparticles amplified the local electric field, enhancing the photothermal effect in synergy with thermal energy to change the rate-limiting step and lower the barrier energy of reactions. This study provides valuable insights for designing efficient and stable non-noble metal catalysts for photothermal CO2 methanation.
Data availability
All data are available in the main text or the ESI.†
Author contributions
Conceptualization: ZX, JZ and XC. Methodology: ZX and LL. Investigation: ZX, CX, SJ and MD. Visualization: ZX, SJ, JJ and MD. Supervision: ZX, WH, XZ and TH. Writing—original draft: ZX and LL. Writing – review & editing: ZX, LL, HW and BH.
Conflicts of interest
The authors declare no competing interests.
Acknowledgements
This work was supported by the National Key Research and Development Program of China (2023YFA1507901), the National Natural Science Foundation of China (22293015, 22293012, and 22121002), the Fundamental Research Funds for the Central Universities, and Island “Atmosphere and Ecology” Category IV Peak Discipline (No. QN202505).The authors thank the Shanghai Synchrotron Radiation Facility for access to the beamlines BL13SSW, respectively.
Notes and references
- W. Wang, S. Wang, X. Ma and J. Gong, Chem. Soc. Rev., 2011, 40, 3703–3727 RSC.
- M. Aresta, A. Dibenedetto and A. Angelini, Chem. Rev., 2014, 114, 1709–1742 CrossRef CAS PubMed.
- L. Wan, Q. Zhou, X. Wang, T. E. Wood, L. Wang, P. N. Duchesne, J. Guo, X. Yan, M. Xia and Y. F. Li,
et al.
, Nat. Catal., 2019, 2, 889–898 CrossRef CAS.
- J. Cored, A. García-Ortiz, S. Iborra, M. J. Climent, L. Liu, C.-H. Chuang, T.-S. Chan, C. Escudero, P. Concepción and A. Corma, J. Am. Chem. Soc., 2019, 141, 19304–19311 CrossRef CAS PubMed.
- X. Yan, W. Sun, L. Fan, P. N. Duchesne, W. Wang, C. Kübel, D. Wang, S. G. H. Kumar, Y. F. Li and A. Tavasoli,
et al.
, Nat. Commun., 2019, 10, 2608 CrossRef PubMed.
- C. Vogt, M. Monai, G. J. Kramer and B. M. Weckhuysen, Nat. Catal., 2019, 2, 188–197 CrossRef CAS.
- X. Meng, T. Wang, L. Liu, S. Ouyang, P. Li, H. Hu, T. Kako, H. Iwai, A. Tanaka and J. Ye, Angew. Chem., Int. Ed., 2014, 53, 11478–11482 CrossRef CAS PubMed.
- M. He, Y. Sun and B. Han, Angew. Chem., Int. Ed., 2022, 61, e202112835 CrossRef CAS PubMed.
- A. Parastaev, V. Muravev, E. H. Osta, T. F. Kimpel, J. F. M. Simons, A. J. F. van Hoof, E. Uslamin, L. Zhang, J. J. C. Struijs and D. B. Burueva,
et al.
, Nat. Catal., 2022, 5, 1051–1060 CrossRef CAS.
- H. Xin, L. Lin, R. Li, D. Li, T. Song, R. Mu, Q. Fu and X. Bao, J. Am. Chem. Soc., 2022, 144, 4874–4882 CrossRef CAS PubMed.
- C. Wang, E. Guan, L. Wang, X. Chu, Z. Wu, J. Zhang, Z. Yang, Y. Jiang, L. Zhang and X. Meng,
et al.
, J. Am. Chem. Soc., 2019, 141, 8482–8488 CrossRef CAS PubMed.
- J. Ma, J. Yu, G. Chen, Y. Bai, S. Liu, Y. Hu, M. Al-Mamun, Y. Wang, W. Gong and D. Liu,
et al.
, Adv. Mater., 2023, 35, 2302537 CrossRef CAS PubMed.
- M. Cai, C. Li, X. An, B. Zhong, Y. Zhou, K. Feng, S. Wang, C. Zhang, M. Xiao and Z. Wu,
et al.
, Adv. Mater., 2023, 36, 2308859 CrossRef PubMed.
- H. Zhang, T. Wang, J. Wang, H. Liu, T. D. Dao, M. Li, G. Liu, X. Meng, K. Chang and L. Shi,
et al.
, Adv. Mater., 2016, 28, 3703–3710 CrossRef CAS PubMed.
- H. Jiang, L. Wang, H. Kaneko, R. Gu, G. Su, L. Li, J. Zhang, H. Song, F. Zhu and A. Yamaguchi,
et al.
, Nat. Catal., 2023, 6, 519–530 CrossRef CAS.
- F. Raziq, C. Feng, M. Hu, S. Zuo, M. Z. Rahman, Y. Yan, Q.-H. Li, J. Gascon and H. Zhang, J. Am. Chem. Soc., 2024, 146, 21008–21016 CrossRef CAS PubMed.
- N. Keller, J. Ivanez, J. Highfield and A. M. Ruppert, Appl. Catal., B, 2021, 296, 120320 CrossRef CAS.
- P. G. O'Brien, K. K. Ghuman, F. M. Ali, A. Sandhel, T. E. Wood, J. Y. Y. Loh, J. Jia, D. Perovic, C. V. Singh and N. P. Kherani,
et al.
, Energy Environ. Sci., 2018, 11, 3443–3451 RSC.
- X. Yan, M. Cao, S. Li, P. N. Duchesne, W. Sun, C. Mao, R. Song, Z. Lu, X. Chen and W. Qian,
et al.
, J. Am. Chem. Soc., 2023, 145, 27358–27366 CrossRef CAS PubMed.
- Y. Chen, Y. Zhang, G. Fan, L. Song, G. Jia, H. Huang, S. Ouyang, J. Ye, Z. Li and Z. Zou, Joule, 2021, 5, 3235–3251 CrossRef CAS.
- Q. Li, C. Wang, H. Wang, J. Chen, J. Chen and H. Jia, Angew. Chem., Int. Ed., 2024, 63, e202318166 CrossRef CAS PubMed.
- Z. Wu, C. Li, Z. Li, K. Feng, M. Cai, D. Zhang, S. Wang, M. Chu, C. Zhang and J. Shen,
et al.
, ACS Nano, 2021, 15, 5696–5705 CrossRef CAS PubMed.
- D. Mateo, N. Morlanes, P. Maity, G. Shterk, O. F. Mohammed and J. Gascon, Adv. Funct. Mater., 2020, 31, 2008244 CrossRef.
- D. Mateo, J. Albero and H. García, Energy Environ. Sci., 2017, 10, 2392–2400 RSC.
- X. Chen, Q. Li, M. Zhang, J. Li, S. Cai, J. Chen and H. Jia, ACS Appl. Mater. Interfaces, 2020, 12, 39304–39317 CrossRef CAS PubMed.
- Z.-H. He, Z.-H. Li, Z.-Y. Wang, K. Wang, Y.-C. Sun, S.-W. Wang, W.-T. Wang, Y. Yang and Z.-T. Liu, Green Chem., 2021, 23, 5775–5785 RSC.
- G. Chen, R. Gao, Y. Zhao, Z. Li, G. I. N. Waterhouse, R. Shi, J. Zhao, M. Zhang, L. Shang and G. Sheng,
et al.
, Adv. Mater., 2017, 30, 1704663 CrossRef PubMed.
- S. Ullah, E. C. Lovell, T. H. Tan, B. Xie, P. V. Kumar, R. Amal and J. Scott, Appl. Catal., B, 2021, 294, 120248 CrossRef CAS.
- Y. Gu, J. Ding, X. Tong, H. Yao, R. Yang and Q. Zhong, J. CO2 Util., 2022, 61, 102003 CrossRef CAS.
- Z. Xia, L. Niu, Q. Wu, Y. An and G. Bai, Green Chem., 2023, 25, 9313–9321 RSC.
- C. J. Weststrate, D. Garcia Rodriguez, D. Sharma and J. W. Niemantsverdriet, J. Catal., 2022, 405, 303–312 CrossRef CAS.
- A. Parastaev, V. Muravev, E. Huertas Osta, A. J. F. van Hoof, T. F. Kimpel, N. Kosinov and E. J. M. Hensen, Nat. Catal., 2020, 3, 526–533 CrossRef CAS.
- Q. Hao, Z. Li, Y. Shi, R. Li, Y. Li, L. Wang, H. Yuan, S. Ouyang and T. Zhang, Angew. Chem., Int. Ed., 2023, 62, e202312808 CrossRef CAS PubMed.
- F. Zhang, Y.-H. Li, M.-Y. Qi, Y. M. A. Yamada, M. Anpo, Z.-R. Tang and Y.-J. Xu, Chem Catal., 2021, 1, 272–297 CrossRef CAS.
- J. Zhai, Z. Xia, B. Zhou, H. Wu, T. Xue, X. Chen, J. Jiao, S. Jia, M. He and B. Han, Nat. Commun., 2024, 15, 1109 CrossRef CAS PubMed.
- S. Linic, S. Chavez and R. Elias, Nat. Mater., 2021, 20, 916–924 CrossRef CAS PubMed.
- F. Tong, X. Liang, Z. Wang, Y. Liu, P. Wang, H. Cheng, Y. Dai, Z. Zheng and B. Huang, ACS Catal., 2021, 11, 10814–10823 CrossRef CAS.
- E. Peiris, S. Sarina, E. R. Waclawik, G. A. Ayoko, P. Han, J. Jia and H. Y. Zhu, Angew. Chem., Int. Ed., 2019, 58, 12032–12036 CrossRef CAS PubMed.
- X. Zhu, H. Zong, C. J. V. Pérez, H. Miao, W. Sun, Z. Yuan, S. Wang, G. Zeng, H. Xu and Z. Jiang,
et al.
, Angew. Chem., Int. Ed., 2023, 62, e202218694 CrossRef CAS PubMed.
- T. Shao, X. Wang, H. Dong, S. Liu, D. Duan, Y. Li, P. Song, H. Jiang, Z. Hou and C. Gao,
et al.
, Adv. Mater., 2022, 34, 2202367 CrossRef CAS PubMed.
- N. Lu, X. Jiang, Y. Zhu, L. Yu, S. Du, J. Huang and Z. Zhang, Adv. Mater., 2025, 37, 2413931 CrossRef CAS PubMed.
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