Thermal Expansion, Response to Pressure, and Reversible Pressure-Induced Amorphization in Anion Excess ReO₃-Type Cubic LuZrF₇

Abstract

A variety of ReO3-type and double-ReO3-type metal fluorides have been examined for their potential as negative and low-thermal-expansion materials. However, they are susceptible to unwanted phase transitions on cooling and modest compression. The incorporation of excess fluoride into the ReO3 structure, to form materials such as [Mg1–x Zr x ]­ZrF6+2x and cubic LuZrF7, can be used to eliminate unwanted phase transitions and control thermal expansion. Variable-temperature powder diffraction measurements on cubic LuZrF7 show close to zero thermal expansion between 100 and 200 K. On compression, cubic LuZrF7 does not undergo a long-range cooperative polyhedral titling phase transition typical of ReO3-type fluorides. Instead, it begins to amorphize on compression at >0.7 GPa. This amorphization is reversible at ambient temperature over the course of 24 h. Cubic LuZrF7 has a bulk modulus, K 0, of 44.8(8) GPa and displays pronounced pressure-induced softening (K 0′ = 29(1)), prior to the amorphization. In situ high-pressure X-ray total scattering data indicate that the local structure of cubic LuZrF7 is largely preserved out to ∼7 Å on amorphization, suggesting that there are no major changes in bonding associated with the amorphization and providing an explanation for why the amorphization is reversible. The amorphization is likely associated with the reorientation and distortion of coordination polyhedra in an uncorrelated manner.

Efficient and thermally stable broadband near-infrared emission from near zero thermal expansion AlP3O9:Cr3+phosphors

“…This phenomenon has also been observed in many other NTE/ZTE materials. 43,44 To demonstrate the practical application of the as-synthesized AlP 3 O 9 :Cr 3+ NIR phosphors, a NIR pc-LED was packaged by assembling the AlP 3 O 9 :6%Cr 3+ phosphor with commercial 450 nm InGaN chips (the inset in Fig. 5a).…”

Exploring negative thermal expansion materials with bulk framework structures and their relevant scaling relationships through multi-step machine learning

“…The training data contains nearly 200 NTE materials from the published literature 16–127 and about 230 positive thermal expansion (PTE) data with the same structures or elements as NTE materials. To improve the reliability of the prediction capability, we employed the three-step ML method with data augmentation and cross-validation.…”