Preparation, structure and dielectric property of barium stannate titanate ceramics

Wei, Xiaoyong; Yao, Xi

doi:10.1016/j.mseb.2006.11.012

Cited by 94 publications

(44 citation statements)

References 26 publications

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“…Features. Because the "verticality" of the MPB is closely related to the choice of two end-member compositions, we used features that couple the atomic, crystal chemistry and electronic structure properties of T and R ends for regression (30)(31)(32)(33)(34)(35). Because polarization and strain are the primary and secondary order parameters, respectively, of the electromechanically coupled piezoelectric materials, we chose the following features: (i) the unit cell volume difference between the T and R ends, ∆Vol = VT − VR, where VT and VR are the unit cell volumes of the T and R ends; (ii) t f , defined by t f = is the weighted average ionic radii of the B site ion at the R end; (iii) the ionic displacements for the T (δDT ) and R (δDR) ends, respectively, taken from the work of Balachandran et al (33); (iv) the ratio of the effective nuclear charge between the A site ion at the T end and B-site ion at the R end, renc, defined by renc = Aenc T Benc R ; and (v) the ratio of electronegativities between the A site ion at the T end and B-site ion at the R end, ren , defined by ren = Aen T Ben R .…”

Section: Datamentioning

confidence: 99%

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Xue

Balachandran

Yuan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

137

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An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau-Devonshire theory, we demonstrate our approach for BaTiO 3 -based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba 0.5 Ca 0.5 )TiO 3 -Ba(Ti 0.7 Zr 0.3 )O 3 , with piezoelectric properties that show better temperature reliability than other BaTiO 3 -based piezoelectrics in our initial training data.piezoelectric materials | materials informatics | Bayesian learning | morphotropic phase boundary | Pb-free materials A ccelerating the process of materials design and discovery is an emerging theme in materials science (1). The emphasis has so far largely been on screening databases or using datadriven approaches that infer predictions directly from the data, be it from high-throughput calculations or experimental measurements (2-6). However, a distinguishing aspect of materials science is that in addition to data there exists a substantial body of knowledge in the form of phenomenological models and physical theories that could be used to constrain the inference models. Hence, a key challenge in materials informatics is to incorporate knowledge to make predictions that are more robust than would be possible by using data alone. Although such knowledge is used in choosing features or descriptors for materials informatics (7-9), it has seldom been used to encode prior information in the form of probability distributions and uncertainties for predicting novel materials with desired properties. Bayesian inference, which permits integration of prior knowledge or beliefs with the observed data, has shown considerable promise in cancer genomics (10) using metabolic pathway information, and in systems biology (11), but has been little explored in materials science. Our objective is to combine empirical data and materials knowledge within a Bayesian approach coupled to the results of Landau-Devonshire theory (12, 13) to design better BaTiO3-based lead-free piezoelectrics.Piezoelectric materials, such as the solid solutions of BaTiO3, are best suited for exploring Bayesian inference methods because historically they are well modeled by Landau-Devonshire theory (12-14) and equations exist for describing some of the key characteristics that determine the functional response, such as the morphotropic phase boundary (MPB) (15, 16). These equations serve as "constraints" that encode prior knowledge within our Bayesian formalism. Furthermore, BaTiO3-based solid solutions represent an important class of potential substitutes for Pb-based materials, which suffer from environmental concerns. Akin to the Pb-based piezoelectrics, MPBs can be established in BaTiO3-based solid solutions that enable polarization and structural inst...

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Section: Datamentioning

confidence: 99%

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Xue

Balachandran

Yuan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

137

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“…The Á( ) characteristic clearly shows the existences of two wavelength ranges (Fig. 4a) which allow the determination of interband optical transitions [25]:…”

Section: Photoelectrochemistrymentioning

confidence: 99%

Electrical, optical and photoelectrochemical properties of BaSnO3−δ

Omeiri¹,

Hadjarab²,

Bouguelia³

et al. 2010

Journal of Alloys and Compounds

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“…Povećanje poroznosti se objašnjava niskom temperaturom sinterovanja. Svi BTS uzorci su sinterovani na istoj temperaturi (1420 o C) bez obzira na sadržaj kalaja, međutim da bi uzorci postigli veliku gustinu potrebno je sa povećanjem sadržaja Sn postepeno povećavati i temperaturu sinterovanja [266]. Teorijska gustina (ρ T ) BTS materijala se povećava sa povećanjem sadržaja kalaja: ρ T barijum titanata je 6.02 g/cm 3 , BTS5 6.11 g/cm 3 , BTS10…”

Section: Specifična Površina Sinterovanih Bts Keramikaunclassified

“…6.18 g/cm 3 a za BTS15 6.23 g/cm 3 [266]. Prema tome, da bi se postigla ρ T BTS materijala, sa povećanjem sadržaja kalaja potrebno je povećati i količinu unete energije.…”

Section: Specifična Površina Sinterovanih Bts Keramikaunclassified

“…U sinterovanim keramikama pripremljenim od BTS prahova, sa povećanjem sadržaja kalaja smanjuje se gustina kao i prosečna veličina zrna. Smanjenje gustine se objašnjava neadekvatnom temperaturom sinterovanja, odnosno, sa povećanjem sadržaja kalaja trebalo bi povećavati i temperaturu sinterovanja na kojoj bi se dobila što veća gustina [266].…”

Section: Mikrostruktura Sinterovanih Keramika (Sem)unclassified

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Synthesis and characterization of BaTi1-xSnxO3 powders and multilayered ceramic materials

Marković¹

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Preparation, structure and dielectric property of barium stannate titanate ceramics

Cited by 94 publications

References 26 publications

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Electrical, optical and photoelectrochemical properties of BaSnO3−δ

Synthesis and characterization of BaTi1-xSnxO3 powders and multilayered ceramic materials

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Preparation, structure and dielectric property of barium stannate titanate ceramics

Cited by 94 publications

References 26 publications

Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Electrical, optical and photoelectrochemical properties of BaSnO3−δ

Synthesis and characterization of BaTi1-xSnxO3 powders and multilayered ceramic materials

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Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

Accelerated search for BaTiO ₃ -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning