Reversibility of imido-based ionic liquids: a theoretical and experimental study

Cao, Bobo; Du, Jiuyao; Cao, Ziping; Sun, Haitao; Sun, Xuejun; Fu, Hui

doi:10.1039/c7ra00008a

Cited by 6 publications

(3 citation statements)

References 59 publications

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“…Followed by a photochemical 6 π ‐electrocyclization, an intermediate trans ‐A was generated by the ring closure. It is consistent with the literature reports [33–35] that the presence of organic acids promotes the tautomerization of amides. Then, trans ‐B was expected via a suprafacial [1,5]‐H shift, [36] which is driven by the rearrangement of the benzene ring.…”

Section: Resultssupporting

confidence: 93%

Photoinduced Annulation of N‐Phenylbenzamides for the Synthesis of Phenanthridin‐6(5H)‐Ones

Wang

et al. 2022

Adv Synth Catal

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A general concise method for the synthesis phenanthridin‐6(5H)‐ones via photoinduced intramolecular annulation of N‐phenylbenzamides was developed. Under argon atmosphere and room temperature, phenanthridin‐6(5H)‐ones were obtained via irradiation N‐phenylbenzamides with a 280 nm UV lamp in the presence of methanesulfonic acid in toluene. The mechanism is illustrated and believed to proceed in the order of amides tautomerization, 6π‐electric cyclization, [1,5]‐H shift, amide‐imidine tautomerization, keto‐enol tautomerism and evolution hydrogen.

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Section: Resultssupporting

confidence: 93%

Photoinduced Annulation of N‐Phenylbenzamides for the Synthesis of Phenanthridin‐6(5H)‐Ones

Wang

et al. 2022

Adv Synth Catal

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“…As such, two approaches are recommended for further research. The first one is the development of novel ILs, which requires experimental effort, and computational investigations, such as the work done by Gomez and co‐workers looking to develop new inorganic electrolytes, or through the work of Sun and co‐workers looking to achieve the same goal, but through simulation based methods …”

Section: Electrolytes For Nibsmentioning

confidence: 99%

Electrolytes, SEI Formation, and Binders: A Review of Nonelectrode Factors for Sodium‐Ion Battery Anodes

Bommier

2018

Small

276

181

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Through intense effort in recent years, knowledge of Na-ion batteries has been advanced significantly, pertaining to electrodes. Often, such progress has been accompanied by using a convenient choice of electrolyte or binder. Nevertheless, it has been witnessed that "external" factors to electrodes, such as electrolytes, solid electrolyte interphase, and binders, affect the functions of electrodes profoundly. And generally, certain types of electrodes favor some electrolytes or binders. With a rapidly increasing number of publications in the area, trends in terms of electrolytes and binders are possibly exploitable. Unfortunately, the field has yet to see a review article that devotes itself to these nonelectrode aspects of Na-ion batteries. Here, the gap is filled by conducting a comprehensive review of these nonelectrode external factors, especially by looking into their correlation with electrochemical properties, such as cycle life, and first cycle coulombic efficiency. Not only are the representative reports reviewed, but also quantitative analyses on the database that are constructed are provided. With such analyses, some new data-driven perspectives are postulated, which are of great value to the community.

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“…Similarly, charge density redistribution is often used to understand trends in catalytic activity [12][13][14][15] . In liquid electrolytes, the analysis of gradients and other features in charge density helps us understand intermolecular interactions [16][17][18][19] . Charge density also gives us access to functional properties (through surrogate models) that are costly to calculate directly.…”

Section: Introductionmentioning

confidence: 99%

Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids

Jørgensen

Bhowmik

2022

npj Comput Mater

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Electron density $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ ρ ( r → ) is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ ρ ( r → ) distributions are often used to capture critical physicochemical phenomena in functional materials. We present a machine learning framework for the prediction of $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ ρ ( r → ) . The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message-passing graph, but only receive messages. The model is tested across multiple datasets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in $$\rho (\overrightarrow{{{{\bf{r}}}}})$$ ρ ( r → ) obtained from DFT done with different exchange-correlation functionals. The accuracy on all three datasets is beyond state of the art and the computation time is orders of magnitude faster than DFT.

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Reversibility of imido-based ionic liquids: a theoretical and experimental study

Abstract: Theoretical and experimental methods were used to study the reversibility of a series of imido-based ionic liquids.

Cited by 6 publications

References 59 publications

Photoinduced Annulation of N‐Phenylbenzamides for the Synthesis of Phenanthridin‐6(5H)‐Ones

Photoinduced Annulation of N‐Phenylbenzamides for the Synthesis of Phenanthridin‐6(5H)‐Ones

Electrolytes, SEI Formation, and Binders: A Review of Nonelectrode Factors for Sodium‐Ion Battery Anodes

Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids

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