The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework
Open accessAbstract
A new metal-organic framework, Fe-BTTri (Fe3[(Fe4Cl)3(BTTri)8]2·18CH3OH, H3BTTri =1,3,5-tris(1H-1,2,3-triazol-5-yl)benzene)), is found to be highly selective in the adsorption of CO over a variety of other gas molecules, making it extremely effective, for example, in the removal of trace CO from mixtures with H2, N2, and CH4. This framework not only displays significant CO adsorption capacity at very low pressures (1.45 mmol/g at just 100 μbar), but, importantly, also exhibits readily reversible CO binding. Fe-BTTri utilizes a unique spin state change mechanism to bind CO in which the coordinatively unsaturated, high-spin Fe(II) centers of the framework convert to octahedral, low-spin Fe(II) centers upon CO coordination. Desorption of CO converts the Fe(II) sites back to a high-spin ground state, enabling the facile regeneration and recyclability of the material. This spin state change is supported by characterization via infrared spectroscopy, single crystal X-ray analysis, Mössbauer spectroscopy, and magnetic susceptibility measurements. Importantly, the spin state change is selective for CO and is not observed in the presence of other gases, such as H2, N2, CO2, CH4, or other hydrocarbons, resulting in unprecedentedly high selectivities for CO adsorption for use in CO/H2, CO/N2, and CO/CH4 separations and in preferential CO adsorption over typical strongly adsorbing gases like CO2 and ethylene. While adsorbate-induced spin state transitions are well-known in molecular chemistry, particularly for CO, to our knowledge this is the first time such behavior has been observed in a porous material suitable for use in a gas separation process. Potentially, this effect can be extended to selective separations involving other π-acids.
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
Millward, A. R., & Yaghi, O. M. (2005) · Journal of the American Chemical Society · doi:10.1021/ja0570032 · Cited by 2,876
Sumida, K., Horike, S., Kaye, S. S., Herm, Z. R., Queen, W. L., Brown, C. M., Grandjean, F., Long, G. J., Dailly, A., & Long, J. R. (2010) · Chemical Science · doi:10.1039/c0sc00179a · Cited by 347
Self-citationIndustrial Gas Handbook: Gas Separation and Purification
Kerry, F. G. (2007) · doi:10.1201/9781420008265 · Cited by 297
Cited by
Metal–Organic Frameworks for Food Safety
Wang, P.-L., Xie, L.-H., Joseph, E. A., Li, J.-R., Su, X.-O., & Zhou, H.-C. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00257 · Cited by 533
Liu, X., Zhou, Y., Zhang, J., Tang, L., Luo, L., & Zeng, G. (2017) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b02563 · Cited by 312
Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
Nandy, A., Duan, C., Janet, J. P., Gugler, S., & Kulik, H. J. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b04015 · Cited by 161
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
Taylor, M. G., Yang, T., Lin, S., Nandy, A., Janet, J. P., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c01458 · Cited by 81
Metadata
- Authors
- Reed, D. A., Xiao, D. J., Gonzalez, M. I., Darago, L. E., Herm, Z. R., Grandjean, F., & Long, J. R.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-04-20
- Volume
- 138
- Issue
- 17
- Pages
- 5594-5602
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2340900686
- Cited by
- 162
- References
- 3
- FWCI
- 9.02
- Type
- journal article
- Language
- en
- Open access
- green
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: Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework. https://app.cochat.ai/papers/10.1021/jacs.6b00248
- The paper
- Reed, D. A., Xiao, D. J., Gonzalez, M. I., Darago, L. E., Herm, Z. R., Grandjean, F., & Long, J. R. (2016). Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.6b00248
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.