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Sun Z, Bai Y, Jing H, Hu T, Du K, Guo Q, Gao P, Tian Y, Ma C, Liu M, Pu Y. A polarization double-enhancement strategy to achieve super low energy consumption with ultra-high energy storage capacity in BCZT-based relaxor ferroelectrics. MATERIALS HORIZONS 2024; 11:3330-3344. [PMID: 38682657 DOI: 10.1039/d4mh00322e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
Due to dielectric capacitors' already-obtained fast charge-discharge speed, research has been focused on improving their Wrec. Increasing the polarization and enhancing the voltage endurance are efficient ways to reach higher Wrec, however simultaneous modification still seems a paradox. For example, in the ferroelectric-to-relaxor ferroelectric (FE-to-RFE) phase transition strategy, which has been widely used in the latest decade, electric breakdown strength (Eb) and energy storage efficiency (η) always increase, while at the same time, the maximum polarization (Pmax) inevitably decreases. The solution to this problem can be obtained from another degree of freedom, like defect engineering. By incorporating Bi(Zn2/3Ta1/3)O3 (BZT) into the Ba0.15Ca0.85Zr0.1Ti0.9O3 (BCZT) lattice to form (1 - x)Ba0.15Ca0.85Zr0.1Ti0.9O3-xBi(Zn2/3Ta1/3)O3 (BCZT-xBZT) solid-solution ceramics, in this work, ultrahigh ferroelectric polarization was achieved in BCZT-0.15BZT, which is caused by the polarization double-enhancement, comprising the contribution of interfacial and dipole polarization. In addition, due to the electron compensation, a Schottky contact formed at the interface between the electrode and the ceramic, which in the meantime, enhanced its Eb. A Wrec of 8.03 J cm-3, which is the highest among the BCZT-based ceramics reported so far, with an extremely low energy consumption, was finally achieved. BCZT-0.15BZT also has relatively good polarization fatigue after long-term use, good energy storage frequency stability and thermal stability, as well as excellent discharge properties.
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Affiliation(s)
- Zixiong Sun
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
- Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices, Hubei University, Wuhan 430062, China
- Shaanxi Collaborative Innovation Center of Industrial Auxiliary Chemistry and Technology, Shaanxi University of Science and Technology, Xi'an 710021, China
- MESA+ Institute for Nanotechnology, University of Twente, PO Box 217, Enschede 7500 AE, The Netherlands
| | - Yuhan Bai
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
| | - Hongmei Jing
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710119, P. R. China.
| | - Tianyi Hu
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China
| | - Kang Du
- School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, China
| | - Qing Guo
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
| | - Pan Gao
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
| | - Ye Tian
- School of Materials Science and Engineering, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
| | - Chunrui Ma
- State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, China
| | - Ming Liu
- School of Microelectronics, Xi'an Jiaotong University, Xi'an, China.
| | - Yongping Pu
- School of Materials Science and Engineering, Shaanxi University of Science and Technology, Xi'an 710021, P. R. China.
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Chen RS, Lu Y. Negative Capacitance Field Effect Transistors based on Van der Waals 2D Materials. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2304445. [PMID: 37899295 DOI: 10.1002/smll.202304445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 09/20/2023] [Indexed: 10/31/2023]
Abstract
Steep subthreshold swing (SS) is a decisive index for low energy consumption devices. However, the SS of conventional field effect transistors (FETs) has suffered from Boltzmann Tyranny, which limits the scaling of SS to sub-60 mV dec-1 at room temperature. Ferroelectric gate stack with negative capacitance (NC) is proved to reduce the SS effectively by the amplification of the gate voltage. With the application of 2D ferroelectric materials, the NC FETs can be further improved in performance and downscaled to a smaller dimension as well. This review introduces some related concepts for in-depth understanding of NC FETs, including the NC, internal gate voltage, SS, negative drain-induced barrier lowering, negative differential resistance, single-domain state, and multi-domain state. Meanwhile, this work summarizes the recent advances of the 2D NC FETs. Moreover, the electrical characteristics of some high-performance NC FETs are expressed as well. The factors which affect the performance of the 2D NC FETs are also presented in this paper. Finally, this work gives a brief summary and outlook for the 2D NC FETs.
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Affiliation(s)
- Ruo-Si Chen
- School of Engineering, College of Engineering, Computing & Cybernetics, Australian National University, Canberra, ACT, 2602, Australia
| | - Yuerui Lu
- School of Engineering, College of Engineering, Computing & Cybernetics, Australian National University, Canberra, ACT, 2602, Australia
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Wang J, Xu P, Ji X, Li M, Lu W. Feature Selection in Machine Learning for Perovskite Materials Design and Discovery. MATERIALS (BASEL, SWITZERLAND) 2023; 16:3134. [PMID: 37109971 PMCID: PMC10146176 DOI: 10.3390/ma16083134] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
Perovskite materials have been one of the most important research objects in materials science due to their excellent photoelectric properties as well as correspondingly complex structures. Machine learning (ML) methods have been playing an important role in the design and discovery of perovskite materials, while feature selection as a dimensionality reduction method has occupied a crucial position in the ML workflow. In this review, we introduced the recent advances in the applications of feature selection in perovskite materials. First, the development tendency of publications about ML in perovskite materials was analyzed, and the ML workflow for materials was summarized. Then the commonly used feature selection methods were briefly introduced, and the applications of feature selection in inorganic perovskites, hybrid organic-inorganic perovskites (HOIPs), and double perovskites (DPs) were reviewed. Finally, we put forward some directions for the future development of feature selection in machine learning for perovskite material design.
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Affiliation(s)
- Junya Wang
- Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Pengcheng Xu
- Materials Genome Institute, Shanghai University, Shanghai 200444, China
| | - Xiaobo Ji
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Minjie Li
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
- Zhejiang Laboratory, Hangzhou 311100, China
- Key Laboratory of Silicate Cultural Relics Conservation (Shanghai University), Ministry of Education, Shanghai 200444, China
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Wei GW, Soares TA, Wahab H, Zhu F. Computational Chemistry in Asia. J Chem Inf Model 2022; 62:5035-5037. [DOI: 10.1021/acs.jcim.2c01050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Xuan X, Guo W, Zhang Z. Ferroelasticity in Two-Dimensional Tetragonal Materials. PHYSICAL REVIEW LETTERS 2022; 129:047602. [PMID: 35939029 DOI: 10.1103/physrevlett.129.047602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 09/30/2021] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
Ferroelasticity is a prominent material property analogous to ferroelectricity and ferromagnetism, but its characteristic spontaneous structural polarization has remained less studied and poorly understood. Here, we use a high-throughput computation approach in conjunction with first-principles calculations to identify 65 (M=transition metal, X=nonmetal) monolayers exhibiting in-plane ferroelasticity out of 166 stable tetragonal monolayers. Molecular orbital theory analysis reveals that ferroelastic distortion arises when M-d/X-p and M-d/M-d couplings are both sufficiently weak. We have developed a physically interpretable one-dimensional descriptor that correctly predicts 89% of ferroelastics or nonferroelastics among the examined MX monolayers. Moreover, we find eleven MX compounds that exhibit strongly coupled ferroelasticity and magnetism driven by strain-controlled magnetocrystalline anisotropy, raising the prospects of developing 2D ferroelasticity-based multiferroics.
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Affiliation(s)
- Xiaoyu Xuan
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Wanlin Guo
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zhuhua Zhang
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, and Institute for Frontier Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Xu P, Chen C, Chen S, Lu W, Qian Q, Zeng Y. Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength. ACS OMEGA 2022; 7:21052-21061. [PMID: 35755382 PMCID: PMC9219529 DOI: 10.1021/acsomega.2c01839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H2 flow of 3 L min-1, and a thickness of 200 μm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM-SVR model can accurately predict the bonding strength of YSZ coating.
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Affiliation(s)
- Pengcheng Xu
- Materials
Genome Institute, Shanghai University, Shanghai 200444, China
| | - Can Chen
- The
State Key Lab of High Performance Ceramics and Superfine Micro-structure,
Shanghai Institute of Ceramics, Chinese
Academy of Sciences, Shanghai 200050, China
| | - Shuizhou Chen
- School
of Computer Engineering and Science, Shanghai
University, Shanghai 200444, China
| | - Wencong Lu
- Materials
Genome Institute, Shanghai University, Shanghai 200444, China
- Department
of Chemistry, College of Sciences, Shanghai
University, Shanghai 200444, China
| | - Quan Qian
- School
of Computer Engineering and Science, Shanghai
University, Shanghai 200444, China
| | - Yi Zeng
- The
State Key Lab of High Performance Ceramics and Superfine Micro-structure,
Shanghai Institute of Ceramics, Chinese
Academy of Sciences, Shanghai 200050, China
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Kim E, Kim J, Min K. Prediction of dielectric constants of ABO 3-type perovskites using machine learning and first-principles calculations. Phys Chem Chem Phys 2022; 24:7050-7059. [PMID: 35258051 DOI: 10.1039/d1cp04702g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this study, the machine-learning method, combined with density functional perturbation theory (DFPT) calculations, was implemented to predict and validate the dielectric constants of ABO3-type perovskites. For the construction of the training database, the dielectric constants of 7113 inorganic materials were extracted from the Materials Project. The chemical, structural, and physical descriptors were generated and trained using the gradient-boosting-based regressor after feature engineering. The prediction accuracies were 0.83 and 0.67 (R2) and 0.12 and 0.26 (root mean square error) for the electronic and ionic contributions to the dielectric constant, respectively. The constructed surrogate model was then employed to predict the dielectric constants of the ABO3-type perovskites (216 structures), whose thermodynamic stabilities were satisfactory. The predicted values were validated using DFPT calculations. The constructed database was further used to develop a surrogate model for the prediction of dielectric constants. The final R2 prediction accuracies reached 0.79 (electronic) and 0.67 (ionic).
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Affiliation(s)
- Eunsong Kim
- School of Mechanical Engineering, Soongsil University, Dongjak-gu, 369 Sangdo-ro, Seoul, 06978, Republic of Korea.
| | - Joonchul Kim
- School of Mechanical Engineering, Soongsil University, Dongjak-gu, 369 Sangdo-ro, Seoul, 06978, Republic of Korea.
| | - Kyoungmin Min
- School of Mechanical Engineering, Soongsil University, Dongjak-gu, 369 Sangdo-ro, Seoul, 06978, Republic of Korea.
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