Tuning thermal expansion from strong negative to zero to positive in Cu2-Zn P2O7 solid solutions

Intrinsic thermal expansion and tunability of thermal expansion coefficient in Ni-substituted Co2V2O7

“…205), substituting P for V can tune the CTE and percent relative expansion [21]. Similar tunability is observed when substituting Zn for Cu [19] or V for P [22] in Cu 2 P 2 O 7 (space groups C2/c, No. 15 and C2/m, No.…”

“…However, the present VTXRD data are likely unsuitable to elucidate intricacies in oxygen occupancies and atomic positions atoms given the low number of electrons. Furthermore, results from diffraction techniques can disagree with 'true' bond lengths yielded by local structure techniques such as EXAFS or pair distribution function measurements since diffraction yields an 'average' atomic position that can be clouded by anisotropic vibration [19]. Thus, additional techniques that understand the temperature dependence of the oxygen positions and occupancy (such as variable temperature neutron diffraction) and the local structure to reveal true bond lengths (such as temperature dependent EXAFS or pair distribution function measurements) are required to understand which bonds' rigidity are most affected by the inclusion of Ni.…”

“…Though robust for many materials, including ZrW 2 O 8 mentioned above, some framework oxides with AAV below 16 Å 3 exhibit NTE, thus they would not be detected by this proxy. Specifically, materials in the A 2 Q 2 O 7 (A = divalent metal; Q = P, V) family such as α -Cu 2 P 2 O 7 (AAV = 10.97 Å 3 ) [18], β -Cu 2 P 2 O 7 (AAV = 10.91 Å 3 ), [19] and α -Zn 2 V 2 O 7 (AAV = 13.24 Å 3 ) [20] which have all shown NTE. Thus, we find this family of materials worthy of further study to explore whether or not NTE is exhibited and, if so, how it manifests.…”

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.…”