arXiv subjects

Cristina H. Amon

Publications and source records attributed to Cristina H. Amon.

4 recordsLinked to original sources

arXiv2026

High-magnitude, spatially and directionally programmable, and sustained strain engineering of 2D semiconductors

Crystalline two-dimensional (2D) semiconductors often combine high elasticity and in-plane strength, making them ideal for strain-induced tuning of electronic characteristics, akin to strategies used in silicon electronics. However, existing techniques have not achieved strain in 2D materials that is simultaneously high in magnitude (>1%), stable over long periods, and spatially and directionally programmable. In this context, programmable strain means that the strain level and direction can be deterministically engineered across a single layer. Here, we apply spatially programmable biaxial strain (e_b) up to ~2.2% with a spatial gradient of ~0.1 %e_b um-1 in monolayer MoS2 via conformal transfer onto patterned substrates fabricated using two-photon lithography. The induced e_b is stable for months and enables local band gap tuning of ~0.4 eV in monolayer MoS2, ~25% of its intrinsic band gap. Further, by tailoring substrate topography, we introduce uniaxial (anisotropic) strain, demonstrating control over both strain magnitude and direction. We also extend this strain engineering framework to MoS2-WS2 bilayer heterostructures. Overall, we introduce a distinct regime of strain-enabled control in 2D semiconductors to support the development of optoelectronics and nanoelectronics with engineered optical and electronic landscapes.

cond-mat.mtrl-sci↗

arXiv2024

Deep encoder-decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling

Conjugate heat transfer (CHT) analyses are vital for the design of many energy systems. However, high-fidelity CHT numerical simulations are computationally intensive, which limits their applications such as design optimization, where hundreds to thousands of evaluations are required. In this work, we develop a modular deep encoder-decoder hierarchical (DeepEDH) convolutional neural network, a novel deep-learning-based surrogate modeling methodology for computationally intensive CHT analyses. Leveraging convective temperature dependencies, we propose a two-stage temperature prediction architecture that couples velocity and temperature fields. The proposed DeepEDH methodology is demonstrated by modeling the pressure, velocity, and temperature fields for a liquid-cooled cold-plate-based battery thermal management system with variable channel geometry. A computational mesh and CHT formulation of the cold plate is created and solved using the finite element method (FEM), generating a dataset of 1,500 simulations. Our performance analysis covers the impact of the novel architecture, separate DeepEDH models for each field, output geometry masks, multi-stage temperature field predictions, and optimizations of the hyperparameters and architecture. Furthermore, we quantify the influence of the CHT analysis' thermal boundary conditions on surrogate model performance, highlighting improved temperature model performance with higher heat fluxes. Compared to other deep learning neural network surrogate models, such as U-Net and DenseED, the proposed DeepEDH architecture for CHT analyses exhibits up to a 65% enhancement in the coefficient of determination $R^{2}$. (*Due to the notification of arXiv "The Abstract field cannot be longer than 1,920 characters", the appeared Abstract is shortened. For the full Abstract, please download the Article.)

cs.CE↗

arXiv2021

Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability

Successful development of wind farms relies on the optimal siting of wind turbines to maximize the power capacity under stochastic wind conditions and wake losses caused by neighboring turbines. This paper presents a novel method to quickly generate approximate optimal layouts to support infrastructure design decisions. We model the quadratic integer formulation of the discretized layout design problem with an undirected graph that succinctly captures the spatial dependencies of the design parameters caused by wake interactions. On the undirected graph, we apply probabilistic inference using sequential tree-reweighted message passing to approximate turbine siting. We assess the effectiveness of our method by benchmarking against a state-of-the-art branch and cut algorithm under varying wind regime complexities and wind farm discretization resolutions. For low resolutions, probabilistic inference can produce optimal or nearly optimal turbine layouts that are within 3% of the power capacity of the optimal layouts achieved by state-of-the-art formulations, at a fraction of the computational cost. As the discretization resolution (and thus the problem size) increases, probabilistic inference produces optimal layouts with up to 9% more power capacity than the best state-of-the-art solutions at a much lower computational cost.

math.OC↗

arXiv2019

Phonon thermal transport in \b{eta}-NX (X=P, As, Sb) monolayers: a first-principles study of the interplay between harmonic and anharmonic phonon properties

The investigation of thermal properties of recently emerged two-dimensional (2D) materials is a necessary step towards fulfilling their potential applications in nano-electronics devices. In this study, the thermal conductivity of novel \b{eta}-NX (X=P, As, Sb) monolayers are investigated using a first-principles density functional theory (DFT) study based on the full solution of the linearized Peierls-Boltzmann transport equation (PBTE). The results show that the room temperature thermal conductivities of \b{eta}-NP, \b{eta}-NAs, and \b{eta}-NSb are about 1.1, 5.5, and 34.0 times higher than those of single-element \b{eta}-P, \b{eta}-As, and \b{eta}-Sb monolayers, respectively. The phonon transport analysis reveals that higher phonon group velocities as well as phonon lifetimes are responsible for such an enhancement in the lattice thermal conductivities of \b{eta}-NX (X=P, As, Sb) binary compounds compared to single-element group-VA monolayers. We found that \b{eta}-NP has the minimum thermal conductivity among \b{eta}-NX (X=P, As, Sb) monolayers, while it has the minimum average atomic mass, which is in contrast with the common assumption that lower mass systems exhibit higher thermal conductivities. This work demonstrates the trade-off between harmonic and anharmonic phonon properties in determining the variation of the thermal conductivity among \b{eta}-NX (X=P, As, Sb) monolayers. The higher anharmonicity in \b{eta}-NP is found to be responsible for the lower thermal conductivity of this monolayer.

cond-mat.mtrl-sci↗