The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Dual Role of Pyridinic-N Doping in Carbon-Coated Ni Nanoparticles for Highly Efficient Electrochemical CO2 Reduction to CO over a Wide Potential Range
Abstract
Developing efficient and stable electrocatalysts within a wide potential range is vital for the mature applications of the electrocatalytic CO 2 reduction reaction (CO 2 RR) into value-added chemical products. Herein, we engineered a NC@Ni/C nano-composite featuring a core–shell structure of a pyridinic-N-rich carbon layer encapsulating Ni nanoparticles (NPs) as a highly effective electrocatalyst for CO 2 RR to CO over a wide potential range. The catalyst demonstrates a high CO Faradaic efficiency (FE CO ) of >90% in a wide potential range from −0.65 to −1.45 V [vs reversible hydrogen electrode (RHE)] with the maximum FE CO of 97% at −1.05 V (vs RHE). Strikingly, it exhibits an excellent stability with a constant current density and a FE CO > 95% for 92 h at −1.05 V (vs RHE). Structural studies and DFT calculations further reveal that pyridinic-N doping in the carbon shell of Ni NPs plays a dual role in promoting the CO 2 RR activity. It not only alleviates the mass transfer limitation of CO 2 by enhancing the CO 2 adsorption capacity, but it also lowers the reaction energy barrier of the *COOH formation rate-determining step with the electronic structure modulation by Ni. This work may shed more light on the seeking of practical catalysts for high-efficiency electrochemical CO 2 reduction over a broad potential window.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A review of catalysts for the electroreduction of carbon dioxide to produce low-carbon fuels
Qiao, J., Liu, Y., Hong, F., & Zhang, J. (2014) · Chem. Soc. Rev. · doi:10.1039/c3cs60323g · Cited by 2,990
Self-citationAtomically dispersed Ni(i) as the active site for electrochemical CO2 reduction
Yang, H. B., Hung, S.-F., Liu, S., Yuan, K., Miao, S., Zhang, L., Huang, X., Wang, H.-Y., Cai, W., Chen, R., et al. (2018) · Nature Energy · doi:10.1038/s41560-017-0078-8 · Cited by 2,239
CO 2 electrolysis to multicarbon products in strong acid
Huang, J. E., Li, F., Ozden, A., Sedighian Rasouli, A., García de Arquer, F. P., Liu, S., Zhang, S., Luo, M., Wang, X., Lum, Y., et al. (2021) · Science · doi:10.1126/science.abg6582 · Cited by 1,359
Yang, Y., Lin, Z., Gao, S., Su, J., Lun, Z., Xia, G., Chen, J., Zhang, R., & Chen, Q. (2017) · ACS Catalysis · doi:10.1021/acscatal.6b02573 · Cited by 423
Jiang, K., Siahrostami, S., Akey, A. J., Li, Y., Lu, Z., Lattimer, J., Hu, Y., Stokes, C., Gangishetty, M., Chen, G., et al. (2017) · Chem · doi:10.1016/j.chempr.2017.09.014 · Cited by 406
Self-citation
Cited by
Chang, B., Pang, H., Raziq, F., Wang, S., Huang, K.-W., Ye, J., & Zhang, H. (2023) · Energy & Environmental Science · doi:10.1039/d3ee00964e · Cited by 458
Metadata
- Authors
- Lu, Q., Chen, C., Di, Q., Liu, W., Sun, X., Tuo, Y., Zhou, Y., Pan, Y., Feng, X., Li, L., Chen, D., & Zhang, J.
- Venue
- ACS Catalysis
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-01-07
- Volume
- 12
- Issue
- 2
- Pages
- 1364-1374
- ISSN
- 2155-5435 (ISSN-L)
- OpenAlex
- W4206017437
- Cited by
- 161
- References
- 5
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.