The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
High-throughput, combinatorial synthesis of multimetallic nanoclusters
Open accessAbstract
Significance: Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials.
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
Combining theory and experiment in electrocatalysis: Insights into materials design
Seh, Z. W., Kibsgaard, J., Dickens, C. F., Chorkendorff, I., Nørskov, J. K., & Jaramillo, T. F. (2017) · Science · doi:10.1126/science.aad4998 · Cited by 12,114
George, E. P., Raabe, D., & Ritchie, R. O. (2019) · Nature Reviews Materials · doi:10.1038/s41578-019-0121-4 · Cited by 5,047
Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles
Ferrando, R., Jellinek, J., & Johnston, R. L. (2008) · Chemical Reviews · doi:10.1021/cr040090g · Cited by 3,690
Highly Crystalline Multimetallic Nanoframes with Three-Dimensional Electrocatalytic Surfaces
Chen, C., Kang, Y., Huo, Z., Zhu, Z., Huang, W., Xin, H. L., Snyder, J. D., Li, D., Herron, J. A., Mavrikakis, M., et al. (2014) · Science · doi:10.1126/science.1249061 · Cited by 2,765 · cited 1×
“These inhomogeneities originate from the substantial challenge in controlling the kinetics and dynamics of chemical reactions at the nanoscale among these dissimilar constituents (1, 4 – 6, 10 – 12, 14, 17).”
Homogeneously dispersed multimetal oxygen-evolving catalysts
Zhang, B., Zheng, X., Voznyy, O., Comin, R., Bajdich, M., García-Melchor, M., Han, L., Xu, J., Liu, M., Zheng, L., et al. (2016) · Science · doi:10.1126/science.aaf1525 · Cited by 2,447
Wang, D., Xin, H. L., Hovden, R., Wang, H., Yu, Y., Muller, D. A., DiSalvo, F. J., & Abruña, H. D. (2013) · Nature Materials · doi:10.1038/nmat3458 · Cited by 2,082
Self-citationHigh-Entropy Alloys as a Discovery Platform for Electrocatalysis
Batchelor, T. A. A., Pedersen, J. K., Winther, S. H., Castelli, I. E., Jacobsen, K. W., & Rossmeisl, J. (2019) · Joule · doi:10.1016/j.joule.2018.12.015 · Cited by 982
Cited by
High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery
Yao, Y., Dong, Q., Brozena, A., Luo, J., Miao, J., Chi, M., Wang, C., Kevrekidis, I. G., Ren, Z. J., Greeley, J., et al. (2022) · Science · doi:10.1126/science.abn3103 · Cited by 1,081
Self-citationHigh-entropy energy materials: challenges and new opportunities
Ma, Y., Ma, Y., Wang, Q., Schweidler, S., Botros, M., Fu, T., Hahn, H., Brezesinski, T., & Breitung, B. (2021) · Energy & Environmental Science · doi:10.1039/d1ee00505g · Cited by 963
High-entropy materials for catalysis: A new frontier
Sun, Y., & Dai, S. (2021) · Science Advances · doi:10.1126/sciadv.abg1600 · Cited by 942
Recent progress of high-entropy materials for energy storage and conversion
Amiri, A., & Shahbazian-Yassar, R. (2021) · Journal of Materials Chemistry A · doi:10.1039/d0ta09578h · Cited by 592
Self-citationFeng, G., Ning, F., Song, J., Shang, H., Zhang, K., Ding, Z., Gao, P., Chu, W., & Xia, D. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c07643 · Cited by 560
Metadata
- Authors
- Yao, Y., Huang, Z., Li, T., Wang, H., Liu, Y., Stein, H. S., Mao, Y., Gao, J., Jiao, M., Dong, Q., Dai, J., Xie, P., Xie, H., Lacey, S. D., Takeuchi, I., Gregoire, J. M., Jiang, R., Wang, C., Taylor, A. D., Shahbazian-Yassar, R., & Hu, L.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2020-03-10
- Volume
- 117
- Issue
- 12
- Pages
- 6316-6322
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W3011550352
- Cited by
- 238
- References
- 7
- FWCI
- 10.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: High-throughput, combinatorial synthesis of multimetallic nanoclusters. https://app.cochat.ai/papers/10.1073/pnas.1903721117
- The paper
- Yao, Y., Huang, Z., Li, T., Wang, H., Liu, Y., Stein, H. S., Mao, Y., Gao, J., Jiao, M., Dong, Q., Dai, J., Xie, P., Xie, H., Lacey, S. D., Takeuchi, I., Gregoire, J. M., Jiang, R., Wang, C., Taylor, A. D., . . . Hu, L. (2020). High-throughput, combinatorial synthesis of multimetallic nanoclusters. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1903721117
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.
