Regulating the electronic properties of MoSe2 to improve its CO2 electrocatalytic reduction performance via atomic doping

2021 ◽  
Vol 45 (12) ◽  
pp. 5350-5356
Author(s):  
Jingjing Ye ◽  
Dewei Rao ◽  
Xiaohong Yan
Keyword(s):  
Electronic Properties ◽  
Electronic Property ◽  
Catalytic Performance ◽  
Reaction Intermediates ◽  
Electrocatalytic Reduction

The atomic environment should heavily influence the performance of CO2 reduction, and the regulated electronic property of reaction intermediates and metals (Cu) is responsible for the high catalytic performance of CH4 production.

Material property prediction and structural inverse design with modern deep learning techniques

2020 ◽  
Author(s):  
◽  
Dawei Li
Keyword(s):  
Mechanical Properties ◽  
Deep Learning ◽  
Electronic Properties ◽  
Material Property ◽  
Electronic Property ◽  
Inverse Design ◽  
Structural Defects ◽  
Property Prediction ◽  
Methane Uptake ◽  
Doped Graphene

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] To manipulate the mechanical and physical properties of bulk materials, like metals, ceramics, and semiconductors, the introduction of structural defects on the atomic or nano-level scales to the material has been widely adopted [1]. Properties that can be altered using this strategy include mechanical [2-4], magnetic [5-7] and electronic [8-11] properties. Recent development in the area of machine learning (ML) and deep learning (DL) generated new insights in the area of material research. ML models have been applied to the prediction of material properties of stoichiometric inorganic crystalline materials [12]. With the motivation to resolve the challenge of applying ML and DL in problems to correlate the predicted properties to its corresponding material structures, our lab previously proposed a brand new Concatenate Convolutional Network (CCN) [1] for predicting electronic properties, i.e. bandgaps, for doped graphene, a 2D material with widely tunable properties by doping different atoms. The proposed DL network provided a very promising performance in the prediction of electronic properties of graphene and boron-nitride (BN) hybrids, a well-known 2D bulk material. To take one step further in the performance of the prediction, as well as providing more insights into the structure-property relationships, we recently have applied a modified version of Google Inception V2 [13] network to the previously proposed problem and achieved much more improvements on predicting the electronic properties. The success of a highly accurate prediction of electronic properties led to the possibility of inverse design of the material using an even newer DL structure, Generative Adversarial Network (GAN). Since the possibility of hybridized graphene is enormous due to the dopant atom species, concentrations, and configurations, searching through such a vast material dimensional space in a high throughput manner would largely prompt the usage of doped graphene in the field of electronics, photo-electronics and robotics. More prominently, direct inverse design based on a desiring target functionality is highly anticipated. We recently have proposed a brand-new GAN structure solving the problem of inverse material design provided with the desired properties [14]. The new GAN structure we proposed can generate material data conditioned on a given electronic property, which is a continuous quantitative label. To the best of our knowledge, current existing GAN structures cannot generate data with the functionalities of regressional and conditional, some of the previous trials have ended up in either poor performance or non-fully autonomous generation. With the modified Google Inception Network to predict the electronic property of graphene-BN hybrids (h-BN) and the new regressional and conditional GAN (RCGAN) to design h-BN upon a desired electronic property, we wondered if the strategies and neural networks can be extended further into other material property related problems. Similar strategies were applied for mechanical property related problems of h-BN, however, instead of training a neural network from scratch, transfer learning has been adopted. The new network borrowed the prediction power from the network used for the electronic property prediction through sharing the same set of weights on the convolutional layers. The new network also achieved higher accuracy in predicting mechanical properties for graphene-BN hybrids, while required less resource in training the network and converged to a stable performance with a higher efficiency. After predicting mechanical properties of h-BN graphene successfully, inverse design based on desired mechanical properties has been achieved using RCGAN. To further explore applications of ML and DL in the world of material science, several ML and DL models have been applied to resolve the problem of predicting methane uptake based on material dimensions and environmental conditions. Two major questions raised are, what is the key factor affecting the methane uptake and how are they affecting it. They have been solved using the feature importance vectors output from the ML model with the highest predicting accuracy, along with the visualization of methane uptake through a contour plot created using the DL model. In all, throughout my latest year in research topics combining ML and DL with material problems, we have proposed several feasible strategies and mechanisms for gaining more insights into material and its correlated properties. We will focus more on the area of autonomous chemical structural design and discovery upon a desiring property using modified RCGAN and generate SMILES [15] representing chemical structures corresponding to the desired property in the future research.

scholarly journals The Electronic Property Differences between dA::dG and dA::dGoxo. A Theoretical Approach

Molecules ◽  
2020 ◽  
Vol 25 (17) ◽  
pp. 3828
Author(s):  
Boleslaw T. Karwowski
Keyword(s):  
Electronic Properties ◽  
Electron Affinity ◽  
Electronic Property ◽  
Theoretical Approach ◽  
Genetic Information ◽  
Solvent Relaxation ◽  
Structural And Electronic Properties ◽  
Lower Ionization Potential ◽  
Adiabatic Mode ◽  
Influence Of Solvent

The dA::dGoxo pair appearing in nucleic ds-DNA can lead to a mutation in the genetic information. Depending on the dGoxo source, an AT→GC and GC→AC transversion might be observed. As a result, glycosylases are developed during the evolution, i.e., OGG1 and MutY. While the former effectively removes Goxo from the genome, the second one removes adenine from the dA::dGoxo and dA:dG pair. However, dA::dGoxo is recognized by MutY as ~6–10 times faster than dA:dG. In this article, the structural and electronic properties of simple nucleoside pairs dA:dG, dC:::dGoxo, dC:::dG, dA::dGoxo in the aqueous phase have been taken into theoretical consideration. The influence of solvent relaxation on the above is also discussed. It can be concluded that the dA::dGoxo nucleoside pair shows a lower ionization potential and higher electron affinity than the dA:dG pair in both a vertical and adiabatic mode. Therefore, it could be predicted, under electronic properties, that the electron ejected, for instance by a MutY 4[Fe-S]2+ cluster, is predisposed to trapping by the ds-DNA part containing the dA::dGoxo pair rather than by dA::dG.

A rational study on the geometric and electronic properties of single-atom catalysts for enhanced catalytic performance

Nanoscale ◽  
2020 ◽  
Vol 12 (45) ◽  
pp. 23206-23212
Author(s):  
Qi Xue ◽  
Yi Xie ◽  
Simson Wu ◽  
Tai-Sing Wu ◽  
Yun-Liang Soo ◽  
...  
Keyword(s):  
Electronic Properties ◽  
Catalytic Performance ◽  
Reduction Reaction ◽  
Single Atom ◽  
Geometric And Electronic Properties

We investigate the geometric and electronic properties of single-atom catalysts (SACs) for electrocatalytic CO2 reduction reaction (eCO2RR).

Novel Fe–Ce–Ti catalyst with remarkable performance for the selective catalytic reduction of NOx by NH3

2016 ◽  
Vol 6 (17) ◽  
pp. 6688-6696 ◽  
Author(s):  
Zhiming Liu ◽  
Yuxian Liu ◽  
Biaohua Chen ◽  
Tianle Zhu ◽  
Lingling Ma
Keyword(s):  
Selective Catalytic Reduction ◽  
Catalytic Reduction ◽  
Catalytic Performance ◽  
Redox Cycle ◽  
Reaction Intermediates ◽  
Scr Of No ◽  
Reduction Of Nox

The redox cycle (Ce4+ + Fe2+ ↔ Ce3+ + Fe3+) over the Fe–Ce–Ti catalyst contributes to the activation of NOx and NH3 and thus the formation of reaction intermediates, leading to the high catalytic performance for the NH3-SCR of NOx.

Electrocatalytic reduction of CO2 by two-dimensional transition metal porphyrin sheets

2019 ◽  
Vol 7 (19) ◽  
pp. 11944-11952 ◽  
Author(s):  
Jin-Hang Liu ◽  
Li-Ming Yang ◽  
Eric Ganz
Keyword(s):  
Transition Metal ◽  
Catalytic Performance ◽  
Two Dimensional ◽  
Electrocatalytic Reduction ◽  
Metal Porphyrin ◽  
Reduction Of Co2 ◽  
Excellent Catalytic Performance ◽  
Cu And Zn ◽  
Electrocatalytic Reduction Of Co2 ◽  
Monolayer Catalysts

The first transition metal series TM–PP monolayer catalysts exhibit excellent catalytic performance during the process of electroreduction of CO2. The products have 2e− CO (Sc, Mn and Ni), HCOOH (Cr, Fe, Co, Cu and Zn), 8e− CH4 (Ti and V), and the overpotential of the reaction can be as low as 0.127 V.

Effect of the coordination environment of Cu in Cu2O on the electroreduction of CO2 to ethylene

2020 ◽  
Vol 22 (19) ◽  
pp. 6340-6344
Author(s):  
Yahui Wu ◽  
Chunjun Chen ◽  
Xupeng Yan ◽  
Shoujie Liu ◽  
Mengen Chu ◽  
...  
Keyword(s):  
Coordination Number ◽  
Crucial Role ◽  
Crystal Surface ◽  
Catalytic Performance ◽  
Electrocatalytic Reduction ◽  
Coordination Environment

The coordination number (CN) of Cu–Cu and Cu–O in Cu2O play crucial role on the catalytic performance of CO2 electrocatalytic reduction to C2H4. And the CN of Cu–Cu and Cu–O could be tuned by changing the crystal surface and size of Cu2O.

Taming electronic properties of boron nitride nanosheets as metal-free catalysts for aerobic oxidative desulfurization of fuels

2018 ◽  
Vol 20 (19) ◽  
pp. 4453-4460 ◽  
Author(s):  
Linjie Lu ◽  
Jing He ◽  
Peiwen Wu ◽  
Yingcheng Wu ◽  
Yanhong Chao ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Electronic Properties ◽  
Oxidative Desulfurization ◽  
Catalytic Performance ◽  
Metal Free ◽  
Boron Nitride Nanosheets ◽  
The Matrix

Electronic properties of boron nitride nanosheets were tamed by doping carbon atoms into the matrix for boosted aerobic catalytic performance.

scholarly journals Boron-doped onion-like carbon with enriched substitutional boron: the relationship between electronic properties and catalytic performance

2015 ◽  
Vol 3 (43) ◽  
pp. 21805-21814 ◽  
Author(s):  
Yangming Lin ◽  
Yansong Zhu ◽  
Bingsen Zhang ◽  
Yoong Ahm Kim ◽  
Morinobu Endo ◽  
...  
Keyword(s):  
Electronic Properties ◽  
Catalytic Performance ◽  
Boron Doped ◽  
The Relationship

Boron-doped onion-like carbon is developed as a novel electrocatalyst. The detailed relationship between electronic properties and catalytic performance is explored.

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