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Journal of Physics: Condensed Matter - latest papers

Latest articles for Journal of Physics: Condensed Matter

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  • Chemical pressure induced spin reorientation in the magnetic structure of Ca3Mn2−xSnxO7 ( x=0,0.03,0.05 )
    The effect of partially substituting Tin (Sn) at the Manganese (Mn) site of , viz. with , on its structural and magnetic properties have been investigated using synchrotron diffraction, neutron diffraction, and bulk magnetization measurements. It is observed that with a substitution of , the minor (≈8%) tetragonal ( ) structural phase that is present in the predominantly orthorhombic ( ) undoped , completely disappears. The compounds order antiferromagnetically, the ordering temperature decreases with increasing Sn-content, indicating a weakening of the antiferromagnetic exchange interactions. Interestingly, in the ordered state, the spin magnetic moments which were aligned along the a-axis of the unit cell in the undoped compound, are observed to have reoriented with their major components lying in the b − c plane in the Sn-doped compounds. The above influence of Sn-doping is seen to be stemming from a significant modification of the octahedral rotation and tilt mode geometry in the unit cell, that is known to be responsible for driving ferroelectricity in these compounds.

  • Advancements and challenges in strained group-IV-based optoelectronic materials stressed by ion beam treatment
    In this perspective article, we discuss the application of ion implantation to manipulate strain (by either neutralizing or inducing compressive or tensile states) in suspended thin films. Emphasizing the pressing need for a high-mobility silicon-compatible transistor or a direct bandgap group-IV semiconductor that is compatible with complementary metal–oxide–semiconductor technology, we underscore the distinctive features of different methods of ion beam-induced alteration of material morphology. The article examines the precautions needed during experimental procedures and data analysis and explores routes for potential scalable adoption by the semiconductor industry. Finally, we briefly discuss how this highly controllable strain-inducing technique can facilitate enhanced manipulation of impurity-based spin quantum bits (qubits).

  • Integrating machine learning and the finite element method for assessing stiffness degradation in photovoltaic modules
    This study introduces a novel machine learning (ML) method utilizing a stacked auto-encoder network to predict stiffness degradation in photovoltaic (PV) modules with pre-existing cracks. The input data for the training process was derived from numerical simulations, ensuring a comprehensive representation of module behavior under various conditions. The findings highlight the robust predictive capability of the model, as evidenced by its impressive R2 value of 0.961 and notably low root mean square error (RMSE) of 4.02%. These metrics significantly outperform those of other conventional methods, including the artificial neural network with R2 of 0.905 and RMSE of 9.43%, the space vector machine with R2 of 0.827 and RMSE of 17.93%, and the random forest (RF) with R2 of 0.899 and RMSE of 11.02%. Moreover, the findings suggest that the predictive dynamics of degradation are affected by the varying weight functions of different input parameters, such as climate temperature (CT), grain size (GS), material effort, and pre-crack size, as the degradation level changes. Furthermore, a geometric analysis reveals model deficiencies where significant overestimations correlate with thicker glass components, while pronounced underestimations are predominantly associated with thinner layers of polycrystalline silicon wafer and Ethylene Vinyl Acetate in the module. As a case study, it demonstrated that to maintain a constant degradation level between 1.30 and 1.32 in a PV module with components featuring consistent geometric attributes, the input parameters must be kept within specific ranges: CT ranging from 33 °C to 57 °C, GS ranging from 36 to 81 μm, material effort ranging from 0.74 to 0.81, and pre-crack size ranging from 24 to 32 μm. Therefore, this underscores that the ML model not only predicts degradation but also delineates the parameter space required to achieve a consistent output value.

  • Structure and scaling of Kitaev chain across a quantum critical point in real space
    The spatial Kibble–Zurek mechanism is applied to the Kitaev chain with inhomogeneous pairing interactions that vanish in half of the lattice and result in a quantum critical point separating the superfluid and normal-gas phases in real space. The weakly-interacting BCS theory predicts scaling behavior of the penetration of the pair wavefunction into the normal-gas region different from conventional power-law results due to the non-analytic dependence of the BCS order parameter on the interaction. The Bogoliubov–de Gennes (BdG) equation produces numerical results confirming the scaling behavior and hints complications in the strong-interaction regime. The limiting case of the step-function quench reveals the dominance of the BCS coherence length in absence of additional length scale. Furthermore, the energy spectrum and wavefunctions from the BdG equation show abundant in-gap states from the normal-gas region in addition to the topological edge states.

  • Particle-assisted formation of oil-in-liquid metal emulsions
    Gallium-based liquid metals (LMs) have surface tension an order of magnitude higher than water and break up into micro-droplets when mixed with other liquids. In contrast, silicone oil readily mixes into LM foams to create oil-in-LM emulsions with oil inclusions. Previously, the LM was foamed through rapid mixing in air for an extended duration (over 2 h). This process first results in the internalization of oxide flakes that form at the air-liquid interface. Once a critical fraction of these randomly shaped solid flakes is reached, air bubbles internalize into the LM to create foams that can internalize secondary liquids. Here, we introduce an alternative oil-in-LM emulsion fabrication method that relies on the prior addition of SiO2 micro-particles into the LM before mixing it with the silicone oil. This particle-assisted emulsion formation process provides a higher control over the composition of the LM-particle mixture before oil addition, which we employ to systematically study the impact of particle characteristics and content on the emulsions’ composition and properties. We demonstrate that the solid particle size (0.8 μm to 5 μm) and volume fraction (1%–10%) have a negligible impact on the internalization of the oil inclusions. The inclusions are mostly spherical with diameters of 20–100 μm diameter and are internalized by forming new, rather than filling old, geometrical features. We also study the impact of the particle characteristics on the two key properties related to the functional application of the LM emulsions in the thermal management of microelectronics. In particular, we measure the impact of particles and silicone oil on the emulsion’s thermal conductivity and its ability to prevent deleterious gallium-induced corrosion and embrittlement of contacting metal substrates.