Multiscale insight into the Cd1-xZnxTe vibrational-mechanical properties -- High-pressure experiments and ab initio calculations
Toni Alhaddad, Mohamad Shoker, Olivier Pages, Alain Polian, Vitor, Torres, Yann Le Godec, Jean-Paul Itie, Christophe Bellin, Keevin Beneut,, Sebastien Diliberto, Stephanie Michel, Agnieszka Marasek, Karol, Strzalkowski

TL;DR
This study combines high-pressure experiments and ab initio calculations to explore the multiscale vibrational-mechanical properties of Cd1-xZnxTe alloys, revealing pressure-tunable phonon coupling processes and insights into bond-scale interactions.
Contribution
It provides a detailed multiscale analysis of phonon coupling in Cd1-xZnxTe, linking experimental and theoretical approaches to understand pressure effects on vibrational and mechanical properties.
Findings
Bulk modulus varies linearly with composition, matching ab initio predictions.
Pressure induces coupling/decoupling of phonons depending on alloy composition.
Pressure-tunable phonon switches can be achieved based on percolation mechanisms.
Abstract
The Cd1-xZnxTe semiconductor alloy is a regular system regarding its macroscopic mechanic properties in that its experimental bulk modulus exhibits a linear x-dependence, in line with ab initio predictions. Complexity arises at the bond scale, referring to the intricate Cd1-xZnxTe percolation-type Raman pattern [T. Alhaddad et al., Journal of Applied Physics 133, 065701 (2023)]. This offers an appealing benchmark to test various phonon coupling processes at diverse length scales in a compact multi-oscillator assembly, presently tuned by pressure. At x around 0, an inter-bond long-range/macro electric coupling between the matrix and impurity polar phonons is detuned under pressure. Inversely, at x around 1, an intra-bond short-range/nano mechanic coupling is enforced between the two Zn Te apolar sub-phonons stemming from same and alien percolation-type environments. The pressure-induced…
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Taxonomy
TopicsChalcogenide Semiconductor Thin Films · Advanced Semiconductor Detectors and Materials · Machine Learning in Materials Science
