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Jian Luo

Publications and source records attributed to Jian Luo.

At least 73 records · Page 4Linked to original sources

High-Entropy Rare Earth Tetraborides

Six high-entropy rare earth tetraborides of the tetragonal UB4-prototyped structure have been successfully synthesized for the first time. The specimens are prepared from elemental precursors via high-energy ball mill and in-situ reactive spark plasma sintering. The sintered specimens are >98% in relative densities without detectable oxide impurities (albeit the presence of minor hexaborides in some compositions). No detectable secondary phase is observed in the composition (Y$_{0.2}$Nd$_{0.2}$Sm$_{0.2}$Gd$_{0.2}$Tb$_{0.2}$)B$_{4}$, which is proven homogeneous at both microscale and nanoscale. The Vickers microhardness are determined to be ~13-15 GPa at a standard indentation load of 9.8 N. A scientifically interesting observation is represented by the anisotropic lattice distortion from the rule-of-mixture averages. This work expands the family of high-entropy ceramics via fabricating a new class of high-entropy borides with a unique tetragonal quasi-layered crystal structure.

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Single-Phase Duodenary High-Entropy Fluorite/Pyrochlore Oxides with an Order-Disorder Transition

Improved thermomechanical properties have been reported for various high-entropy oxides containing typically five metal cations. This study further investigates a series of duodenary (11 metals + oxygen) high-entropy oxides by mixing different fractions of a five-cation fluorite-structured niobate and a seven-cation pyrochlore (both containing Yb) with matching lattice constants. Nine compositions of duodenary high-entropy oxides have been examined. All of them exhibit single high-entropy phases of either disordered fluorite or ordered pyrochlore structure. An order-disorder transition (ODT) is evident with changing composition, accompanied by a reduction in thermal conductivity (k). In comparison with the ODT criteria developed from ternary oxides, these duodenary oxides are more prone to disorder, but the ODT is still controlled by similar factors (but at different thresholds). Interestingly, there are abrupt increases in Young's modulus (E) at low mixing concentrations near both endmembers. The E/k ratios are increased, in comparison with both endmembers. This study suggests a new route to tailor high-entropy ceramics via controlling cation ordering vs. disordering.

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Multi-QuartzNet: Multi-Resolution Convolution for Speech Recognition with Multi-Layer Feature Fusion

In this paper, we propose an end-to-end speech recognition network based on Nvidia's previous QuartzNet model. We try to promote the model performance, and design three components: (1) Multi-Resolution Convolution Module, replaces the original 1D time-channel separable convolution with multi-stream convolutions. Each stream has a unique dilated stride on convolutional operations. (2) Channel-Wise Attention Module, calculates the attention weight of each convolutional stream by spatial channel-wise pooling. (3) Multi-Layer Feature Fusion Module, reweights each convolutional block by global multi-layer feature maps. Our experiments demonstrate that Multi-QuartzNet model achieves CER 6.77% on AISHELL-1 data set, which outperforms original QuartzNet and is close to state-of-art result.

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End-to-end Silent Speech Recognition with Acoustic Sensing

Silent speech interfaces (SSI) has been an exciting area of recent interest. In this paper, we present a non-invasive silent speech interface that uses inaudible acoustic signals to capture people's lip movements when they speak. We exploit the speaker and microphone of the smartphone to emit signals and listen to their reflections, respectively. The extracted phase features of these reflections are fed into the deep learning networks to recognize speech. And we also propose an end-to-end recognition framework, which combines the CNN and attention-based encoder-decoder network. Evaluation results on a limited vocabulary (54 sentences) yield word error rates of 8.4% in speaker-independent and environment-independent settings, and 8.1% for unseen sentence testing.

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MLNET: An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection

Voice activity detection (VAD) makes a distinction between speech and non-speech and its performance is of crucial importance for speech based services. Recently, deep neural network (DNN)-based VADs have achieved better performance than conventional signal processing methods. The existed DNNbased models always handcrafted a fixed window to make use of the contextual speech information to improve the performance of VAD. However, the fixed window of contextual speech information can't handle various unpredicatable noise environments and highlight the critical speech information to VAD task. In order to solve this problem, this paper proposed an adaptive multiple receptive-field attention neural network, called MLNET, to finish VAD task. The MLNET leveraged multi-branches to extract multiple contextual speech information and investigated an effective attention block to weight the most crucial parts of the context for final classification. Experiments in real-world scenarios demonstrated that the proposed MLNET-based model outperformed other baselines.

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High-Entropy Monoborides: Towards Superhard Materials

Single-phase high-entropy monoborides (HEMBs) of the CrB prototype structure have been synthesized for the first time. Reactive spark plasma sintering of ball milled mixtures of elemental precursor powders produced bulk (V0.2Cr0.2Nb0.2Mo0.2Ta0.2)B, (V0.2Cr0.2Nb0.2Mo0.2W0.2)B, and (V0.2Cr0.2Nb0.2Ta0.2W0.2)B HEMB specimens of ~98.3-99.5% relative densities. Vickers hardness was measured to be ~22-26 GPa at an indentation load of 9.8 N and ~32-37 GPa at 0.98 N. In particular, the load-dependent hardness of (V0.2Cr0.2Nb0.2Ta0.2W0.2)B is higher than those of ternary (Ta0.5W0.5)B (already considered as superhard) and hardest reported high-entropy metal diborides, and on a par with the classical superhard boride WB4.

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Part I: Theoretical Predictions of Preferential Oxidation in Refractory High Entropy Materials

High entropy materials, which include high entropy alloys, carbides, and borides, are a topic of substantial research interest due to the possibility of a large number of new material compositions that could fill gaps in application needs. There is a current need for materials exhibiting high temperature stability, particularly oxidation resistance. A systematic understanding of the oxidation behavior in high entropy materials is therefore required. Prior work notes large differences in the thermodynamic favorability between oxides formed upon oxidation of high entropy materials. This work uses both analytical and computational thermodynamic approaches to investigate and quantify the effects of this large variation and the resulting potential for preferential component oxidation in refractory high entropy materials including group IV-, V- and VI-element based alloys and ceramics. Thermodynamic calculations show that a large tendency towards preferential oxidation is expected in these materials, even for elements whose oxides exhibit a small difference in thermodynamic favorability. The effect is reduced in carbides, compared to their alloy counterparts. Further, preferential oxidation in high entropy refractory materials could result in possible destabilization of the solid solution or formation of other, competing phases, with corresponding changes in bulk material properties.

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Dual-Phase High-Entropy Ultra-High Temperature Ceramics

A series of dual-phase high-entropy ultrahigh temperature ceramics (DPHE-UHTCs) are fabricated starting from N binary borides and (5-N) binary carbides powders. >~99% relative densities have been achieved with virtually no native oxides. These DPHE-UHTCs consist of a hexagonal high-entropy boride (HEB) phase and a cubic high-entropy carbide (HEC) phase. A thermodynamic relation that governs the compositions of the HEB and HEC phases in equilibrium is discovered and a thermodynamic model is proposed. These DPHE-UHTCs exhibit tunable grain size, Vickers microhardness, Young' and shear moduli, and thermal conductivity. The DPHE-UHTCs have higher hardness than the weighted linear average of the two single-phase HEB and HEC, which are already harder than the rule-of-mixture averages of individual binary borides and carbides. This study extends the state of the art by introducing dual-phase high-entropy ceramics (DPHECs), which provide a new platform to tailor various properties via changing the phase fraction and microstructure.

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Dissolving and Stabilizing Soft WB2 and MoB2 Phases into High-Entropy Borides via Boron-Metals Reactive Sintering to Attain Higher Hardness

Five single-phase WB2- and MoB2-containing high-entropy borides (HEBs) have been made via reactive spark plasma sintering of elemental boron and metals. A large reactive driving force enables the full dissolution of 10-20 mol. % WB2 to form dense, single-phase HEBs, including (Ti0.2Zr0.2Hf0.2Mo0.2W0.2)B2, (Ti0.2Ta0.2Cr0.2Mo0.2W0.2)B2, (Zr0.2Hf0.2Nb0.2Ta0.2W0.2)B2, and (Zr0.225Hf0.225Ta0.225Mo0.225W0.1)B2; the successful fabrication of such single-phase WB2-containing HEBs has not been reported before. In the processing science, this result serves perhaps the best example demonstrating that the phase formation in high-entropy ceramics can strongly depend on the kinetic route. A scientifically interesting finding is that HEBs containing softer WB2 and/or MoB2 components are significantly harder than (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2 (with harder binary boride components). This exemplifies that high-entropy ceramics can achieve unexpected properties.

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A Step Forward from High-Entropy Ceramics to Compositionally Complex Ceramics: A New Perspective

High-entropy ceramics (HECs) have quickly gained attention since 2015. To date, nearly all work has focused on five-component, equimolar compositions. This perspective article briefly reviews different families of HECs and selected properties. Following a couple of our most recent studies, we propose a step forward to expand HECs to Compositionally Complex ceramics (CCCs) to include medium-entropy and non-equimolar compositions. Using defective fluorite and ordered pyrochlore oxides as two primary examples, we further consider the complexities of aliovalent cations and anion vacancies as well as ordered structures with two cation sublattices. Better thermally-insulating yet stiff CCCs have been found in non-equimolar compositions with optimal amounts of oxygen vacancies and in ordered pyrochlores with substantial size disorder. It is demonstrated that medium-entropy ceramics (MECs) can prevail over their high-entropy counterparts. The diversifying classes of CCCs provide even more possibilities than HECs to tailor the composition, defects, disorder/order, and, consequently, various properties.

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Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom

Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, a potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called "complexion diagrams," as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a "mission impossible" to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~108 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.

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From High-Entropy Ceramics to Compositionally-Complex Ceramics: A Case Study of Fluorite Oxides

Using fluorite oxides as an example, this study broadens high-entropy ceramics (HECs) to compositionally-complex ceramics (CCCs) or multi-principal cation ceramics (MPCCs) to include medium-entropy and/or non-equimolar compositions. Nine compositions of compositionally-complex fluorite oxides (CCFOs) with the general formula of (Hf1/3Zr1/3Ce1/3)1-x(Y1/2X1/2)xO2-delta (X = Yb, Ca, and Gd; x = 0.4, 0.148, and 0.058) are fabricated. The phase stability, mechanical properties, and thermal conductivities are measured. Compared with yttria-stabilized zirconia, these CCFOs exhibit increased cubic phase stability and reduced thermal conductivity, while retaining high Young's modulus (~210 GPa) and nanohardness (~18 GPa). Moreover, the temperature-dependent thermal conductivity in the non-equimolar CCFOs shows an amorphous-like behavior. In comparison with their equimolar high-entropy counterparts, the medium-entropy non-equimolar CCFOs exhibit even lower thermal conductivity (k) while maintaining high modulus (E), thereby achieving higher E/k ratios. These results suggest a new direction to achieve thermally-insulative yet stiff CCCs (MPCCs) via exploring non-equimolar and/or medium-entropy compositions.

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Size Disorder as a Descriptor for Predicting Reduced Thermal Conductivity in Medium- and High-Entropy Pyrochlores

High-entropy ceramics generally exhibit reduced thermal conductivity, but little is known about what controls this suppression and which descriptor can predict it. Herein, 18 medium- and high-entropy pyrochlores were synthesized to measure their thermal conductivity and Young's modulus. Up to 35% reductions in thermal conductivity were achieved with retained moduli, thereby attaining insulative yet stiff properties for potential thermal barrier coating applications. Notably, the measured thermal conductivity correlates well with a modified size disorder parameter. Thus, this modified size disorder parameter is suggested as a useful descriptor for designing thermally-insulative medium- and high-entropy ceramics (broadly defined as "compositionally-complex ceramics").

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A High-Entropy Silicide: (Mo0.2Nb0.2Ta0.2Ti0.2W0.2)Si2

A high-entropy metal disilicide, (Mo0.2Nb0.2Ta0.2Ti0.2W0.2)Si2, has been successfully synthesized. X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), and electron backscatter diffraction (EBSD) collectively show the formation of a single high-entropy silicide phase. This high-entropy (Mo0.2Nb0.2Ta0.2Ti0.2W0.2)Si2 possesses a hexagonal C40 crystal structure with ABC stacking sequence and a point group of P6222. This discovery expands the known families of high-entropy materials from metals, oxides, borides, carbides, and nitrides to a silicide, for the first time to our knowledge, as well as demonstrating that a new, non-cubic, crystal structure (with lower symmetry) can be made into high-entropy. This (Mo0.2Nb0.2Ta0.2Ti0.2W0.2)Si2 exhibits high nanohardness of 16.7 +- 1.9 GPa and Vickers hardness of 11.6 +- 0.5 GPa. Moreover, it has a low thermal conductivity of 6.9 +- 1.1 W m-1 K-1, which is approximately one order of magnitude lower than that of the widely-used tetragonal MoSi2 and ~1/3 of those reported values for the hexagonal NbSi2 and TaSi2 with the same crystal structure.

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Single-Phase High-Entropy Intermetallic Compounds (HEICs): Bridging High-Entropy Alloys and Ceramics

High-entropy intermetallic compounds (HEICs) were fabricated by mechanical alloying and spark plasma sintering to fill a knowledge gap between the traditional high-entropy alloys (HEAs) and emerging high-entropy ceramics (HECs). Notably, several four- or five-component equimolar aluminides, such as the B2-phase (Fe1/5Co1/5Ni1/5Mn1/5Cu1/5)Al, have been made into single-phase HEICs for the first time. Thermodynamic modeling and a reversible, temperature-dependent, phase-stability experiment suggest that such B2-phase HEICs are entropy-stabilized phases. The structure of these HEICs resembles that of HECs with high-entropy mixing of four or five elements of nearly equal fractions in one and only one sublattice, but with significant (~10%) anti-site defects (differing from typical HECs). A new phase stability rule for forming single B2-phase HEICs is proposed. Five additional HEICs of predominantly D022 phases have also been made. This study broadens the families of equimolar, single-phase, high-entropy materials that have been successfully fabricated.

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Integrated Access and Backhaul Optimization for Millimeter Wave Heterogeneous Networks

By allowing a large number of links to be simultaneously transmitted, directional antenna arrays with beamforming have been envisioned as a promising candidate to reach unprecedented levels of spatial isolation. To achieve the high efficiency of spatial reuse in improving system performance, an optimization problem that maximizes the achievable data rate of a multihop heterogeneous network, which incorporates the concept of integrated access and backhaul and supports both downlink and uplink transmissions on access and backhaul links, is formulated. The optimization problem is then systematically decomposed and demonstrated as NP-hard, and a heuristic joint scheduling and resource allocation algorithm is proposed to maximize the achievable data rate. In addition, an efficient dynamic routing algorithm is proposed to further enhance the data rate. With extensive system-level simulations, it is demonstrated that the proposed algorithms achieve significant gain over benchmark schemes, in terms of data rate, and closely approach the theoretical optimum, yet with lower latency. Besides, the proposed algorithms enable a flexible adjustment of downlink and uplink transmission duration allocation and support both half- and full-duplex modes with considerable performance enhancement. In particular, the proposed algorithms are capable of fulfilling different performance requirements for both point-to-point and point-to-multipoint communications.

cs.IT↗

Multihop Routing for Data Delivery in V2X Networks

Data delivery relying on the carry-and-forward strategy of vehicle-to-vehicle (V2V) communications is of significant importance, however highly challenging due to frequent connection disruption. Fortunately, incorporating vehicle-to-infrastructure (V2I) communications, motivated by its availability in bridging long-range vehicular connectivity, dramatically improves delivery opportunity. Nevertheless, the cooperation of V2V and V2I communications, known as vehicular-to-everything (V2X) communications, necessitates a specific design of multihop routing for enhancing data delivery performance. To address this issue, this paper provides a mathematical framework to investigate the data delivery performance in V2X networks in terms of both delivery latency and data rate. With theoretical analysis, we formulate a global and a distributed optimization problem to maximize the weighted sum of delivery latency and data rate. The optimization problems are then solved by convex optimization theory and based on the solutions, we propose a global and a distributed multihop routing algorithm to select the optimal route for maximizing the weighted sum. The rigorousness of the proposed algorithms is validated by extensive simulation under a wide range of system parameters and simulation results shed insight on the design of multihop routing algorithm in V2X networks for minimizing latency and maximizing data rate.

cs.IT↗

A unified theory of grain growth in polycrystalline materials

Grain growth is a ubiquitous and fundamental phenomenon observed in the cellular structures with the grain assembly separated by a network of grain boundaries, including metals and ceramics. However, the underlying mechanism of grain growth has remained ambiguous for more than 60 years. The models for grain growth, based on the classically linear relationship between the grain boundary migration and capillary driving force, generally predict normal grain growth. Quantitative model for abnormal grain growth is lacking despite decades of efforts. Here, we present a unified model to reveal quantitatively how grain growth evolves, which predicts the normal, abnormal and stagnant behaviors of grain growth in polycrystalline materials. Our model indicates that the relationship between grain boundary migration and capillary driving force is generally nonlinear, but will switch to be the classically linear relationship in a specific case. Furthermore, the grain growth experiments observed in polycrystalline SrTiO3 demonstrates the validity of the unified model. Our study provides a unified, quantitative model to understand and predict grain growth in polycrystalline materials, and thus offers helpful guides for the microstructural design to optimize the properties of polycrystalline materials.

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