The effect of heteroatoms on the formation of magnetic ceramic nanocomposites in pyrolysis of organometallic precursors: similar molecular structure, but totally different morphology, composition and properties

2020 ◽  
Author(s):  
Zhijun Ruan ◽  
Jingwen Ran ◽  
Shanshan Liu ◽  
Yanmei Chen ◽  
Xichao Wang ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Magnetic Properties ◽  
Cobalt Catalyst ◽  
Organometallic Compounds ◽  
Structure Property ◽  
Structure Property Relationship ◽  
Crystal Phases ◽  
Organometallic Precursors ◽  
Saturated Carbon Atom ◽  
Ceramic Nanocomposites

Abstract Four organometallic compounds were synthesized for solid-state pyrolysis (SSP) to research the structure-property relationship between the precursors and the as-generated magnetic ceramic nanocomposites (MCNs). In which, the only saturated carbon atom in MC was replaced by N or O or S atom, to produce MN, MO and MS, respectively. It was found that, the crystal phase of the cobalt catalyst could be regulated by introducing different heteroatoms during the pyrolysis: fcc-Co for MC/MN, while fcc/hcp-Co hybrid for MO/MS. Metal cobalt with different crystal phases has their special catalytic and magnetic properties. Thus, MCNs with totally different morphology, composition and properties could be prepared by just changing one heteroatom in the precursors upon SSP. Uniform nanotubes were generated from pyrolysis of MC/MN, while nanospheres were generated from MO/MS. The obtained MCNs all show excellent magnetic properties with Ms ranged from 47.6 to 54.2 emu g-1. Analyzing carefully, due to the magnetic difference between fcc-Co and hcp-Co, the Ms of the MCNs obtained from MO/MS were slightly lower than those of MC/MN, but, their Mr and Hc were 2 to 5 times higher than the latter.

Controllable preparation of magnetic carbon nanocomposites by pyrolysis of organometallic precursors, similar molecular structure but very different morphology, composition and properties

2021 ◽  
Author(s):  
Zhijun Ruan ◽  
Jingwen Ran ◽  
Shanshan Liu ◽  
Yanmei Chen ◽  
Xichao Wang ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Solid State ◽  
Organometallic Compounds ◽  
Structure Property ◽  
Carbon Nanocomposites ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Magnetic Carbon ◽  
Organometallic Precursors ◽  
Controllable Preparation

Four cobalt-containing organometallic compounds were synthesized for solid-state pyrolysis (SSP) to study the structure-property relationship between precursors and as-generated magnetic carbon nanocomposites (MCNs). In the research, the only saturated carbon...

Predicting Gas Chromatographic Retention Time of Polychlorinated Dibenzo-p-Dioxins from Molecular Structure

2002 ◽  
Vol 216 (4) ◽  
Author(s):  
Eduardo J. Delgado ◽  
Adelio Matamala ◽  
Joel B. Alderete
Keyword(s):  
Molecular Structure ◽  
Retention Time ◽  
Chromatographic Retention ◽  
Quantitative Structure ◽  
Structure Property ◽  
Qspr Model ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship

A quantitative structure-property relationship (QSPR) model is developed to correlate the gas chromatographic retention time of polychlorinated dibenzo-

scholarly journals Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

2018 ◽  
Author(s):  
Shuangjia Zheng ◽  
Xin Yan ◽  
Yuedong Yang ◽  
Jun Xu
Keyword(s):  
Molecular Structure ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Data Sets ◽  
Structure Property ◽  
Structure Representation ◽  
Syntax Analysis ◽  
Structure Property Relationships ◽  
Connection Table ◽  
Structure Property Relationship

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

Structure-property relationship in organometallic compounds regarding SHG

2007 ◽  
Author(s):  
Jürgen Heck ◽  
Marc H. Prosenc ◽  
Timo Meyer-Friedrichsen ◽  
Jan Holtmann ◽  
Edyta Walczuk ◽  
...  
Keyword(s):  
Organometallic Compounds ◽  
Structure Property ◽  
Property Relationship ◽  
Structure Property Relationship

Quantitative Relationship between Organic Molecular Structure and Infinite Dilution Activity Coefficients in 1-Ethyl-3-Methylimidazolium Tetrafluoroborate

2012 ◽  
Vol 524-527 ◽  
pp. 1848-1851
Author(s):  
Jie Ming Xiong ◽  
Chen Chen ◽  
Ming Lan Ge
Keyword(s):  
Molecular Structure ◽  
Activity Coefficients ◽  
Infinite Dilution ◽  
Energy Gap ◽  
Dipole Moments ◽  
Quantitative Relationship ◽  
Structure Property ◽  
Qspr Model ◽  
Structure Property Relationship ◽  
Correlation Parameters

Base on structural descriptors including dipole moments (μ), Energy gap (∆ε), hydration energy (∆H), and hydrophobic parameter lg P of 25 organic solutes, the quantitative structure-property relationship (QSPR) method was used to correlate the values of activity coefficients at infinite dilution, , for the solutes in ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) at 323.15 K. The result showed that the QSPR model had a good correlation and could successfully describe . The quantitative relationship between organic molecular structure and in [EMIM][BF4] was obtained and the correlation parameters were analyzed to understand the interactions that affect activity coefficients at infinite dilution.

scholarly journals Diverse Metal Coordination Center in M-N-C Enabling Different Type of Enzyme-Mimic Reactions

2020 ◽  
Author(s):  
Xinghua Chen ◽  
Lufang Zhao ◽  
Qing Zhou ◽  
Yuan Xu ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Enzyme Mimic ◽  
Metal Coordination ◽  
Enzymatic Reactions ◽  
Structure Property ◽  
Coordination Center ◽  
Structure Property Relationship ◽  
Different Types ◽  
New Perspective ◽  
New Generation

<a></a><a></a><a>The epoch-making breakthrough of nanoscience has brought new perspective to empolder new generation of nanozymes with enzyme-like structure and further to propel the comprehending of the structure-property relationship. Here, we report that the regulation of metal coordination center in M-N-C nanozymes (M = Fe, Co, Mn, Ni, and Cu) greatly altered their biocatalytic activities so as to selectively drive different types of enzymatic reactions. It was revealed that the intrinsic selectivity in interaction and activation of ROS by different M-N<sub>x</sub> was the origin to promote disparate types of enzyme-like reactions. This work would open a new vista of nanozymes to selectively catalyze different types of reactions, enabled by mimicking the molecular structure of natural enzymes and a further modulation.</a>

scholarly journals Identifying Structure-Property Relationships through SMILES Syntax Analysis With Self-Attention Mechanism

2018 ◽  
Author(s):  
Shuangjia Zheng ◽  
Xin Yan ◽  
Yuedong Yang ◽  
Jun Xu
Keyword(s):  
Molecular Structure ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Data Sets ◽  
Structure Property ◽  
Structure Representation ◽  
Syntax Analysis ◽  
Structure Property Relationships ◽  
Connection Table ◽  
Structure Property Relationship

<p>Recognizing substructures and their relations embedded in a molecular structure representation is a key process for <a></a><a>structure-activity</a> or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-the-art methods. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.</p>

ChemInform Abstract: Structure-Property Relationship in Organometallic Compounds Regarding SHG

ChemInform ◽  
2008 ◽  
Vol 39 (44) ◽  
Author(s):  
Juergen Heck ◽  
Marc H. Prosenc ◽  
Timo Meyer-Friedrichsen ◽  
Jan Holtmann ◽  
Edyta Walczuk ◽  
...  
Keyword(s):  
Organometallic Compounds ◽  
Structure Property ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Abstract Structure
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