Nitric Oxide Adsorption in Cu<sub>3</sub>btc<sub>2</sub>-Type MOFs—Physisorption and Chemisorption as NONOates

Khan, Arafat Hossain; Peikert, Katharina; Hoffmann, Frank; Fröba, Michael; Bertmer, Marko

doi:10.1021/acs.jpcc.8b11919

Cited by 24 publications

(19 citation statements)

References 61 publications

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“…Polynuclear copper complexes can serve as functional and structural models of multicopper enzymes 16,102,103 . Dinuclear copper complexes constitute important building blocks for materials such as metal-organic frameworks [104][105][106] , and an important test case for electronic structure methods 16,[27][28][29]107 .…”

Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Exchange Spin Coupling from Gaussian Process Regression

et al. 2020

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Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science. File list (4) download file view on ChemRxiv supp.pdf (2.07 MiB) download file view on ChemRxiv ms.pdf (5.17 MiB) download file view on ChemRxiv Cu2-dataset.zip (22.06 MiB) download file view on ChemRxiv py_regression.zip (17.79 MiB)

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Section: Gaussian Process Regression (Gpr)mentioning

confidence: 99%

Exchange Spin Coupling from Gaussian Process Regression

et al. 2020

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“…16) was used to load NO which showed NONOate formation additional to the coordination of NO to unsaturated copper centers. 17 However, one of the major shortcomings of MOFs is their instability in the presence of moisture, and some researchers tried to enhance its stability in water or PBS. For example, a modied CuBTC with plasma enhanced chemical vapor deposition of peruorohexane was reported by Decoste et al to prevent the formation of water clusters around the Cu sites.…”

Section: Introductionmentioning

confidence: 99%

“… 16 ) was used to load NO which showed NONOate formation additional to the coordination of NO to unsaturated copper centers. 17 …”

Section: Introductionmentioning

confidence: 99%

Stearic acid modified nano CuMOFs used as a nitric oxide carrier for prolonged nitric oxide release

Huang

Zhang

et al. 2022

RSC Adv.

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“…[33][34][35][36][37] Alternatively NO can also be attached to and released from functional moieties incorporated into the organic ligands of MOFs. 38,39 With regards to employing CUSs as the binding mechanism, we have shown that the CPO-27 family of MOFs is particularly effective in this regard. NO binds to the CUSs via a metal-nitrogen coordinate bond and is released from the MOF following substitution by water ( Fig.…”

Section: Introductionmentioning

confidence: 99%