Relative Contributions of Inelastic Phonon Scattering and Elastic Phonon Scattering to Thermal Boundary Conductance across Solid Interfaces

The knowledge of inelastic phonon scattering is crucial for the understanding of thermal boundary conductance across solid interfaces. Several traditional theoretical models such as the acoustic mismatch model (AMM) and the diffuse mismatch model (DMM) assume that the elastic phonon scattering drives the thermal transport across the interface. But there are experiments indicating that the inelastic phonon scattering plays an important part in the interfacial thermal energy conduction as well. We use nonequilibrium molecular dynamics (NEMD) to predict the inelastic phonon conductance across Cu/Si interface. Temperature distribution across Cu/Si interface has been obtained from the simulation results, and a temperature drop across the interface is observed. The inelastic phonon scattering is compared to the elastic phonon scattering to demonstrate their relative contributions to the interfacial thermal conductance. The results show that at relatively high temperature, the inelastic phonon scattering can be comparable to elastic phonon scattering, providing an additional energy dissipation channel

FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning

Personalized federated learning (pFL) enables collaborative training among multiple clients to enhance the capability of customized local models. In pFL, clients may have heterogeneous (also known as non-IID) data, which poses a key challenge in how to decouple the data knowledge into generic knowledge for global sharing and personalized knowledge for preserving local personalization. A typical way of pFL focuses on label distribution skew, and they adopt a decoupling scheme where the model is split into a common feature extractor and two prediction heads (generic and personalized). However, such a decoupling scheme cannot solve the essential problem of feature skew heterogeneity, because a common feature extractor cannot decouple the generic and personalized features. Therefore, in this paper, we rethink the architecture decoupling design for feature-skew pFL and propose an effective pFL method called FediOS. In FediOS, we reformulate the decoupling into two feature extractors (generic and personalized) and one shared prediction head. Orthogonal projections are used for clients to map the generic features into one common subspace and scatter the personalized features into different subspaces to achieve decoupling for them. In addition, a shared prediction head is trained to balance the importance of generic and personalized features during inference. Extensive experiments on four vision datasets demonstrate our method reaches state-of-the-art pFL performances under feature skew heterogeneity

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information

Enantiomeric Discrimination by Surface- Enhanced Raman Scattering- Chiral Anisotropy of Chiral Nanostructured Gold Films

A surface- enhanced Raman scattering- chiral anisotropy (SERS- ChA) effect is reported that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films (CNAFs) equipped in the normal Raman scattering Spectrometer. The CNAFs provided remarkably higher enhancement factors of Raman scattering (EFs) for particular enantiomers, and the SERS intensity was proportional to the enantiomeric excesses (ee) values. Except for molecules with mesomeric species, all of the tested enantiomers exhibited high SERS- ChA asymmetry factors (g), ranging between 1.34 and 1.99 regardless of polarities, sizes, chromophores, concentrations and ee. The effect might be attributed to selective resonance coupling between the induced electric and magnetic dipoles associated with enantiomers and chiral plasmonic modes of CNAFs.Absolution by SERS: A surface- enhanced Raman scattering chiral anisotropy effect is presented that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films. It is applied in the normal Raman scattering system to identify the absolute configuration and composition of enantiomers, overcoming disadvantages of polarimeter systems and chromatography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156417/3/anie202006486-sup-0001-misc_information.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156417/2/anie202006486_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156417/1/anie202006486.pd

Enantiomeric Discrimination by Surface- Enhanced Raman Scattering- Chiral Anisotropy of Chiral Nanostructured Gold Films

A surface- enhanced Raman scattering- chiral anisotropy (SERS- ChA) effect is reported that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films (CNAFs) equipped in the normal Raman scattering Spectrometer. The CNAFs provided remarkably higher enhancement factors of Raman scattering (EFs) for particular enantiomers, and the SERS intensity was proportional to the enantiomeric excesses (ee) values. Except for molecules with mesomeric species, all of the tested enantiomers exhibited high SERS- ChA asymmetry factors (g), ranging between 1.34 and 1.99 regardless of polarities, sizes, chromophores, concentrations and ee. The effect might be attributed to selective resonance coupling between the induced electric and magnetic dipoles associated with enantiomers and chiral plasmonic modes of CNAFs.Absolution by SERS: A surface- enhanced Raman scattering chiral anisotropy effect is presented that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films. It is applied in the normal Raman scattering system to identify the absolute configuration and composition of enantiomers, overcoming disadvantages of polarimeter systems and chromatography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156470/3/ange202006486_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156470/2/ange202006486.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156470/1/ange202006486-sup-0001-misc_information.pd

Multi-Scale Simulations of Nonequilibrium and Non-Local Thermal Transport

Metallic components and metal-dielectric interfaces appear widely in modern electronics and the thermal management is an important issue. A very important feature that has been overlooked in the conventional Fourier’s equations analyses is the nonequilibrium thermal transport induced by selective electron-phonon (e-p) coupling and phonon-phonon (p-p) coupling. It significantly affects many processes such as laser heating and ignoring this phenomenon can lead to wrong or misleading predictions. On the other hand, as devices shrink into nano-scale, heat generation and dissipation at the interfaces between different components start to dominate the thermal process and present a challenge for thermal mitigations. Many unresolved issues also arise from interfaces, such as the unexpected high interfacial thermal conductance (ITC) at metal-diamond interfaces. Both of these require a deep understanding of the physics at interfaces. Therefore in this work, I present multi-scale simulations in metals/dielectrics and interfaces based on two-temperature model (TTM) and establish the new multitemperature model (MTM). The methods are combined with Fourier’s Law, molecular dynamics (MD), Boltzmann transport equations (BTE) and implemented to predict the thermal transport in several materials and interfaces where e-p coupling and p-p coupling are important. First-principles studies based on density functional theory (DFT) are also presented as predictive approaches to acquire the properties, as well as investigating the new physical phenomenon of non-local e-p coupling in metals. This research seeks to provide general, sophisticated but also simple simulation approaches which can help people accurately predict the thermal transport process. It also seeks to explore new physics which cannot be captured and predicted by conventional analyses based on Fourier’s Law and can advance our understanding as well as providing new insights in the current thermal analysis paradigm. The first part of this thesis focuses on the non-equilibrium thermal transport in metals and across metal-dielectric interfaces based on TTM. First of all, nonequilibrium thermal transport in metal matrix composites (MMC) is investigated. Metal particle is usually added to polymer matrix for enhanced thermal performance. Here we apply TTM calculations and manifest a “critical particle size” above which the thermal conductivity of the composite material can be enhanced. MD simulations are performed to predict the thermal properties. TTM-Fourier and TTM-BTE calculations are conducted as comparisons. The widely used Au-SAM (self-assembly-monolayers) material pair is chosen to demonstrate our models. For a 1-D SAMAu-SAM sandwich system, the two calculation approaches present almost identical results, and the critical particle size is 10.7 nm. A general interpretation of thermal transport in sandwiched metal thin films between two dielectric materials is also presented. It is found that when the film thickness is on the order of several nanometers, due to strong e-p non-equilibrium the thermal transport is dominated by phonons and electrons hardly contribute. Then the e-p non-equilibrium thermal transport across metal-dielectric interfaces is investigated using TTM-MD. One possible explanation to the unexpected ITC at metal-diamond interfaces is the cross-interface e-p coupling mechanism, which is based on the hypothesis that electrons can couple to phonons within a certain distance rather than just those at the same location. Therefore we extend TTM-MD by modifying its governing equation to a non-local integral form. Two models are proposed to describe the coupling distance: the “joint-phonon-modes” model and the “phonon-wavelength” model

An open database of computed bulk ternary transition metal dichalcogenides

Abstract We present a dataset of structural relaxations of bulk ternary transition metal dichalcogenides (TMDs) computed via plane-wave density functional theory (DFT). We examined combinations of up to two chalcogenides with seven transition metals from groups 4–6 in octahedral (1T) or trigonal prismatic (2H) coordination. The full dataset consists of 672 unique stoichiometries, with a total of 50,337 individual configurations generated during structural relaxation. Our motivations for building this dataset are (1) to develop a training set for the generation of machine and deep learning models and (2) to obtain structural minima over a range of stoichiometries to support future electronic analyses. We provide the dataset as individual VASP xml files as well as all configurations encountered during relaxations collated into an ASE database with the corresponding total energy and atomic forces. In this report, we discuss the dataset in more detail and highlight interesting structural and electronic features of the relaxed structures

Defect Self-Elimination in Nanocube Superlattices through the Interplay of Brownian, van der Waals, and Ligand-Based Forces and Torques

Understanding defect healing is necessary to predict the response of devices based on nanoparticle-superlattices with controlled electronic and optoelectronic performance. Key questions remain regarding the process of nanoparticle (NP) interactions and resulting assembly dynamics and defect self-elimination. In particular, for anisotropic particles, additional degrees of freedom beyond those of spherical particles, associated with rotational dynamics and torques, significantly impact phenomena. Here we investigate nanocube (NC) superlattices by employing liquid phase transmission electron microscopy, continuum theories, and molecular dynamics (MD) simulations. Analyzing interparticle forces and torques due to van der Waals, Brownian, and ligand interactions, we find that the latter dominates and the anisotropic NC morphology introduces significant torques. In inhomogeneous regions, unbalanced forces and torques induce NC translations and rotations that are transmitted to neighboring NCs, prompting “chain interactions” in a two-dimensional (2D) network, leading to defect self-elimination. The development of this fundamental understanding will further enable design and fabrication of defect-free superlattices, as well as those with tailored defects via assembly of anisotropic particles