Enhancing mechanical properties of hard ceramics, inorganic semiconductors, and Mg alloys

Understanding and optimizing the mechanical properties of materials is of paramount importance across diverse fields of scientific and technological advancements. Specifically, exploring the mechanical properties of crystalline systems in ceramics, semiconductors, and alloys stands as crucial pursuits with far-reaching implications. Ceramics, known for their hardness and brittleness, play pivotal roles in various industries, from cutting-edge electronics to biomedical applications. The mechanical integrity of ceramics is paramount in ensuring their reliability and durability in extreme conditions. On the other hand, semiconductors, with their unique electrical properties, form the backbone of modern electronics. Investigating their mechanical behaviors is essential for optimizing the performance and reliability of electronic devices. In the realm of alloys, encompassing a vast array of metallic compounds, understanding mechanical properties is fundamental for industries like aerospace and manufacturing, where materials must meet stringent criteria of strength, ductility, and resilience. The collective pursuit of comprehending and tailoring the mechanical characteristics of these materials fuels innovation across diverse technological applications, contributing to the development of materials with enhanced performance and versatility. In this work, we apply our multi-scale theoretical approaches to predict the mechanical behaviors of these crystalline materials at the atomic level and propose promising approaches that can help improve the mechanical properties and the design of novel materials. For superhard ceramics, we conduct molecular dynamics simulations to unveil a nuanced understanding of quasi-plastic deformation mechanisms in boron carbide. Firstly, utilizing machine learning force fields, molecular dynamics simulations, and transmission electron microscopy experiments, we identify an anomalous quasi-plastic deformation in B4C under shear deformation along specific slip systems. Furthermore, grain boundary engineering strategies propose the incorporation of Si to mitigate amorphization, showcasing the potential to increase the stress threshold for amorphization and failure for GB structures. These findings offer clues for the design of boron carbide with enhanced mechanical properties. For semiconductors, we focus on the electromechanical behaviors of II-VI ionic semiconductors. We show the control of dislocation motion through external electric fields in zinc sulfide in the experiment and apply Density Functional Theory simulations to uncover the mechanism of this phenomenon. Additionally, we explore the influence of electron and hole carriers on the deformation mechanisms of ZnS, ZnTe, and CdTe. Our results suggest the brittle tendency in ZnS while the ductile tendency in ZnTe and CdTe with excess carriers. The findings suggest potential applications in manipulating the mechanical properties of semiconductors through external stimuli, revealing insights into the intricate interplay between electric fields, carriers, and deformation mechanisms. For metallic systems, we propose a hierarchical high-throughput screening approach, based on quantum-mechanics-derived γ surface, for alloy design with improved mechanical properties. Specifically, we use this method to identify promising dopant elements in magnesium binary alloys with improved ductility. Ten elements, including Hg, Tl, Sn, Sb, Bi, Te, As, Pb, In, and Ca, are identified as promising alloying elements. This multi-faceted research not only contributes insights into the mechanical properties of superhard ceramics, semiconductors, and metallic systems but also proposes innovative avenues for materials design

Harnessing dislocation motion using an electric field

Dislocations, line defects in crystalline materials, play an essential role in the mechanical[1,2], electrical[3], optical[4], thermal[5], and phase transition[6] properties of these materials. Dislocation motion, an important mechanism underlying crystal plasticity, is critical for the hardening, processing, and application of a wide range of structural and functional materials[1,7,8]. For decades, the movement of dislocations has been widely observed in crystalline solids under mechanical loading[9-11]. However, the goal of manipulating dislocation motion via a non-mechanical field alone remains elusive. Here, we present real-time observations of dislocation motion controlled solely by an external electric field in single-crystalline zinc sulfide (ZnS). We find that 30{\deg} partial dislocations can move back and forth depending on the direction of the electric field, while 90{\deg} partial dislocations are motionless. We reveal the nonstoichiometric nature of dislocation cores using atomistic imaging and determine their charge characteristics by density functional theory calculations. The glide barriers of charged 30{\deg} partial dislocations, which are lower than those of 90{\deg} partial dislocations, further decrease under an electric field, explaining the experimental observations. This study provides direct evidence of dislocation dynamics under a non-mechanical stimulus and opens up the possibility of modulating dislocation-related properties

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.Comment: 13 page

QM-Mechanism-Based Hierarchical High-Throughput in silico Screening Catalyst Design for Ammonia Synthesis

We propose and test a hierarchical high-throughput screening (HHTS) approach to catalyst design for complex catalytic reaction systems that is based on quantum mechanics (QM) derived full reaction networks with QM rate constants but simplified to examine only the reaction steps likely to be rate determining. We illustrate this approach by applying it to determine the optimum dopants (our of 35 candidates) to improve the turnover frequency (TOF) for the Fe-based Haber–Bosch ammonia synthesis process. We start from the QM-based free-energy reaction network for this reaction over Fe(111), which contains the 26 most important surface configurations and 17 transition states at operating conditions of temperature and pressure, from which we select the key reaction steps that might become rate determining for the alloy. These are arranged hierarchically by decreasing free-energy reaction barriers. We then extract from the full reaction network, a reduced set of reaction rates required to quickly predict the effect of the catalyst changes on each barrier. This allows us to test new candidates with only 1% of the effort for a full calculation. Thus, we were able to quickly screen 34 candidate dopants to select a small subset (Rh, Pt, Pd, Cu) that satisfy all criteria, including stability. Then from these four candidates expected to increase the TOF for NH3 production, we selected the best candidate (Rh) for a more complete free-energy and kinetic analysis (10 times the effort for HHTS but still 10% of the effort for a complete analysis of the full reaction network). We predict that Rh doping of Fe will increase the TOF for NH_3 synthesis by a factor of ∼3.3 times compared to Fe(111), in excellent agreement with our HHTS predictions, validating this approach

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020). arXiv admin note: substantial text overlap with arXiv:2003.1398

Urban Flood Extent Segmentation and Evaluation from Real-World Surveillance Camera Images Using Deep Convolutional Neural Network

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. High-definition web cameras can be an alternative tool with the models trained on the data it collected. In conclusion, DCNN-based models can extract flood extent from camera images of urban flooding. The challenges with using these models on real-world data identified through this research present opportunities for future research

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Disruption of splicing-regulatory elements using CRISPR/Cas9 to rescue spinal muscular atrophy in human iPSCs and mice

We here report a genome-editing strategy to correct spinal muscular atrophy (SMA). Rather than directly targeting the pathogenic exonic mutations, our strategy employed Cas9 and guide-sgRNA for the targeted disruption of intronic splicing-regulatory elements. We disrupted intronic splicing silencers (ISSs, including ISS-N1 and ISS + 100) of survival motor neuron (SMN) 2, a key modifier gene of SMA, to enhance exon 7 inclusion and full-length SMN expression in SMA iPSCs. Survival of splicing-corrected iPSC-derived motor neurons was rescued with SMN restoration. Furthermore, co-injection of Cas9 mRNA from Streptococcus pyogenes (SpCas9) or Cas9 from Staphylococcus aureus (SaCas9) alongside their corresponding sgRNAs targeting ISS-N1 into zygotes rescued 56% and 100% of severe SMA transgenic mice (Smn , SMN2 ). The median survival of the resulting mice was extended to >400 days. Collectively, our study provides proof-of-principle for a new strategy to therapeutically intervene in SMA and other RNA-splicing-related diseases. -/- tg/

Active Power Decoupling (APD) Converter for PV Microinverter Applications

Under global challenges in climate change, the demand for renewable energy is continuously growing. Photovoltaic (PV) power and its integration into the utility grid are gaining increasing traction. To lower the levelized cost of energy (LCOE) of PV systems, enhance the adoption of PV applications, and ensure the delivery of high-quality power to the utility grid, there is a growing need for reliable, cost-effective, efficient, and compact PV inverters. One key challenge in single-phase PV systems is the short lifetime and poor reliability of electrolytic capacitors used for decoupling the double line frequency (DLF) power. To eliminate the less reliable electrolytic capacitor, the active power decoupling (APD) technique is widely adopted. Various topologies can be used for APD, but the selection of proper topology, modulation scheme, and circuit components, along with the control strategy, will enhance the efficiency, power density, reliability, and cost of the overall PV microinverter. This Ph.D. dissertation proposes an APD converter circuit suitable for PV microinverters, designed for optimized efficiency, power density, and cost. The proposed APD converter is controlled to achieve good power decoupling performance and to optimize the system's maximum power point tracking (MPPT) efficiency. The proposed APD converter circuit is analyzed in the low-frequency domain for power flow and in the high-frequency domain for modulation strategy, where different topologies are considered, taking into account the voltage and current ratings of active devices and decoupling capacitors. Two modulation approaches, continuous conduction mode (CCM) and critical conduction mode (CRM), are compared, considering detailed zero voltage switching (ZVS) operation and different loss mechanisms. Parametric design and multi-objective optimization are performed for CCM and CRM to select circuit components and switching frequency for each modulation strategy to minimize power loss, volume, and costs. With the results of multi-objective optimization, Pareto-optimal designs for CCM and CRM are analyzed in terms of the impact of various circuit elements, namely: switching device output capacitance and on-state resistance, inductor winding turns and core geometries, as well as capacitor dimensions and capacitance. With the optimal CCM- and CRM-operated APD realizations, closed-loop control algorithms are designed, and the corresponding system characteristics are compared. A simple pulse width modulation (PWM) based control strategy that does not rely on zero-crossing detection (ZCD) is proposed to implement closed-loop CRM modulation. In addition, advanced control technologies, including double sampling-based average current control, current observer-based reduced sensor control, and sensorless predictive control, are proposed to improve APD converter performance, reduce system complexity, and lower circuit cost. The proposed APD converter operation is extended to different application scenarios, including burst-mode operation and non-sinusoidal power delivery, including systems with non-linear circuit components, non-linear local loads, or non-ideal grids. A feed-forward control solution is proposed to enable power decoupling for non-sinusoidal power with improved control accuracy and reduced closed-loop design burden. The circuit design, associated analyses, and control approaches are validated by the design, development, and testing of 400 VA APD hardware prototypes