Ab initio study on electronic structure and magnetic properties of AlN and BP monolayers with Ti doping

Wang, Min; Li, Hui; Ren, Jie; Gao, Leyuan; Feng, Tianlong; Zhi, Hao; Yue, Yunliang; Zhou, Tiege; Hou, Denglu

doi:10.1016/j.spmi.2021.107010

Cited by 11 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This value is in good agreement with previous studies [10][11][12][13][14][15][16][17][18][19][20]. Additionally, the bond distance between the N and B atoms was calculated to be 1.45 Å, within the previous data range [10][11][12][13][14][15][16][17][18][19][20]. Next, the size of 1x1 P-h-BN expanded to 5x5, as shown in Figure 1(a), to investigate electronic and other properties.…”

Section: The Structural and Electronic Properties Of P-h-bn And Bv-h-bnsupporting

confidence: 90%

“…First of all, the lattice constants of 1x1 P-h-BN (a, b) were calculated, and the obtained data reveal that the values of a, b for 1x1 P-h-BN have been found to be 2.51 Å. This value is in good agreement with previous studies [10][11][12][13][14][15][16][17][18][19][20]. Additionally, the bond distance between the N and B atoms was calculated to be 1.45 Å, within the previous data range [10][11][12][13][14][15][16][17][18][19][20].…”

Section: The Structural and Electronic Properties Of P-h-bn And Bv-h-bnsupporting

confidence: 88%

“…The formation of a highly regular selfassembled BN nanostructure with a periodicity of 3.22 nm on Rh (111) by CVD of borazine ("NanoMesh") was first obtained by Corso et al, and the BN monolayer structure was modeled by Laskowski et al [8][9]. From then until now, many studies related to h-BN monolayer have been performed [10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introduction *mentioning

confidence: 99%

See 2 more Smart Citations

First-Principles Study of Titanium and Lithium Adsorption on Perfect and Defective Hexagonal Boron Nitride Monolayer Under Effects of Charging

SALMANKURT

2023

Kocaeli Journal of Science and Engineering

View full text Add to dashboard Cite

Single Titanium (Ti) and Lithium (Li) atoms adsorbtion on Pristine and defective hexagonal boron nitride (P-h-BN and BV-h-BN) monolayer were employed using Density Functional Theory (DFT) under effect of charging. Obtained data reveal that Li adsorbtion on P-h-BN is weak, while Ti adsorbtion on P-h-BN is strong. When Ti and Li atoms interact with P-h-BN surface, Ti and Li generate 4 µB/cell and 1 µB/cell magnetic moments, respectively. The extraction of an electron from the systems leads to a considerable rise in the adsorption energy, notably in the case of Li-P-h-BN. There is a notable decrease in the band gap of Ti-P-h-BN in both the charged states, especially in the electron-added state. Removing an electron from the Li-P-h-BN system results in a non-magnetic state and a significant increase of the band gap to 4.07 eV. Ti-BV-h-BN system shows significantly stronger adsorption energy due to d-orbitals of Ti atom. When an electron is added to the systems, the interaction energy between Ti and BV-h-BN decreases, while the interaction energy between Li and BV-h-BN increases. Moreover, removing an electron from Ti-BN-h-BN results in an increase in the band gap to 2.29 eV and disappearance of magnetic moment.

show abstract

Section: The Structural and Electronic Properties Of P-h-bn And Bv-h-bnsupporting

confidence: 90%

Section: The Structural and Electronic Properties Of P-h-bn And Bv-h-bnsupporting

confidence: 88%

Section: Introduction *mentioning

confidence: 99%

See 1 more Smart Citation

First-Principles Study of Titanium and Lithium Adsorption on Perfect and Defective Hexagonal Boron Nitride Monolayer Under Effects of Charging

SALMANKURT

2023

Kocaeli Journal of Science and Engineering

View full text Add to dashboard Cite

show abstract

“…A combined model is used to achieve more accurate forecasting results. In this study, four AI models are selected as the component models for our proposed combined model: limit learning machine (ELM) [30], support vector machine (SVM) [31], back-propagation neuron network (BP) [32], and wavelet neural network (WAVENN) [33]. To improve its accuracy, the four groups of prediction results from the four AI models are weighted again using the multi-objective locust optimization algorithm (MOGOA).…”

Section: Theory and Construction Of Combined Modelmentioning

confidence: 99%

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Tian

Wang

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

show abstract

“…Recently, 2D boron phosphide (BP) which is a group III-V compound, has received a great deal of attention [20][21][22][23][24][25]. Experimentally, BP thin lms were successfully synthesized on aluminum nitride(0001)/sapphire substrate by chemical vapor deposition [26].…”

Section: Introductionmentioning

confidence: 99%

Monolayer and bilayer BP as efficeiant optoelectronic materials in visible and ultraviolet regions

Behzad

Chegel

2023

Preprint

View full text Add to dashboard Cite

Utilizing first-principles calculations, the effects of interlayer interaction and biaxial strain on the phonon dispersion and optical properties of BP sheet are investigated. The calculated phonon spectra shows that the monolayer and bilayer structures are dynamically stable. The phonon bandgap of monolayer BP decreases under tensile strain and increases under the compressive strain. The LO and TO modes harden under compressive strain and soften under tensile strain. The interlayer coupling in bilayer BP causes the splitting of out-of-plane ZA and ZO modes at the Γ point and the phonon band gap decreases with increasing the tensile strain. The absorption edge of bilayer BP is located around 0.6 eV, and it is at a lower energy with respect to the monolayer BP. The results indicate that the optical absorption may be enhanced by applying the compressive strain. These results demonstrate that the phonon dispersion and optical properties of BP sheet could as well be tuned with both interlayer interaction and biaxial strain that are promising for optoelectronic and thermoelectric applications.

show abstract

Ab initio study on electronic structure and magnetic properties of AlN and BP monolayers with Ti doping

Cited by 11 publications

References 29 publications

First-Principles Study of Titanium and Lithium Adsorption on Perfect and Defective Hexagonal Boron Nitride Monolayer Under Effects of Charging

First-Principles Study of Titanium and Lithium Adsorption on Perfect and Defective Hexagonal Boron Nitride Monolayer Under Effects of Charging

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

Monolayer and bilayer BP as efficeiant optoelectronic materials in visible and ultraviolet regions

Contact Info

Product

Resources

About