High-Performance Packaging Technology for Wide Bandgap Semiconductor Modules

The properties of wide band gap (WBG) semiconductors are beneficial to power electronics applications ranging from consumer electronics and renewable energy to electric vehicles and high-power traction applications like high-speed trains. WBG devices, properly integrated, will allow power electronics systems to be smaller, lighter, operate at higher temperatures, and at higher frequencies than previous generations of Si-based systems. These will contribute to higher efficiency, and therefore, lower lifecycle costs and lower CO2 emissions. Over 20 years have been spent developing WBG materials, low-defect-density wafers, epitaxy, and device fabrication and processing technology. In power electronics applications, devices are normally packaged into large integrated modules with electrical, mechanical and thermal connection to the system and control circuit. The first generations of WBG device have used conventional or existing module designs to allow drop-in replacement of Si devices; this approach limits the potential benefit. To realize the full potential of WBG devices, especially the higher operating temperatures and faster switching frequency, a new generation of packaging design and technology concepts must be widely implemented

Status and Trend of Power Semiconductor Module Packaging for Electric Vehicles

Power semiconductor modules are the core components in power-train system of hybrid and electric vehicles (HEV/EV). With the global interests and efforts to popularize HEV/EV, automotive module has become one of the fast growing sectors of power semiconductor industry. However, the comprehensive requirements in power, frequency, efficiency, robustness, reliability, weight, volume, and cost of automotive module are stringent than industrial products due to extremely high standards of vehicle safety and harsh environment. The development of automotive power module is facing comprehensive challenges in designing of structure, material, and assembly technology. In this chapter, the status and trend of power semiconductor module packaging for HEV/EV are investigated. Firstly, the functionality of power electronics and module in HEV/EV power-train system, as well as the performance requirements by automotive industry, is addressed. A general overview of HEV/EV module design and manufacturing is discussed. Then, the typical state-of-the-art commercial and custom HEV/EV power modules are reviewed and evaluated. Lastly, the packaging trends of automotive module are investigated. The advanced assembly concept and technology are beneficial to thermal management, minimized parasitic parameters, enhancement of thermal and mechanical reliability, and the reduction of weight, volume, and cost

An approach to quantifying 3D responses of cells to extreme strain

The tissues of hollow organs can routinely stretch up to 2.5 times their length. Although significant pathology can arise if relatively large stretches are sustained, the responses of cells are not known at these levels of sustained strain. A key challenge is presenting cells with a realistic and well-defined three-dimensional (3D) culture environment that can sustain such strains. Here, we describe an in vitro system called microscale, magnetically-actuated synthetic tissues (micro-MASTs) to quantify these responses for cells within a 3D hydrogel matrix. Cellular strain-threshold and saturation behaviors were observed in hydrogel matrix, including strain-dependent proliferation, spreading, polarization, and differentiation, and matrix adhesion retained at strains sufficient for apoptosis. More broadly, the system shows promise for defining and controlling the effects of mechanical environment upon a broad range of cells

Design of Moderator of a Compact Accelerator-driven Neutron Source for Coded Source Imaging

AbstractCoded source imaging (CSI) is a possible method to solve the contradiction between neutron flux and L/D ratio. Peking University neutron imaging facility (PKUNIFTY) is a RFQ accelerator based facility. The CSI experiments were carried out on PKUNFTY to test the benefits that this technique might bring. The CSI technique gets more restricts on the moderator, especially the neutron distribution in the inner collimator, where the coded mask sampling the source. The effect caused by the non-uniformity of neutron distribution on the mask plane was investigated. The slope type non-uniformity should less than 20% to keep the artifact in the reconstructed image insignificant. The PKUNIFTY moderator was modified according to the above limit. The preliminary experiments shown the moderator design for coded source imaging is acceptable

The Potential Antipyretic Mechanism of Gardeniae Fructus and Its Heat-Processed Products With Plasma Metabolomics Using Rats With Yeast-Induced Fever

Gardeniae Fructus (GF), prepared GF (GFP), and carbonized GF (GFC) are widely used in China for the treatment of fever. However, the involved antipyretic mechanism has not been fully elucidated. In this study, rectal temperature and pyrogenic cytokines were used to evaluate the antipyretic effect of raw and processed GF in rats with dry-yeast-induced pyrexia. Reverse phase and hydrophilic interaction liquid chromatography and ultra-high-performance liquid chromatography/mass spectrometry were used to acquire the metabolomics profile of GF, GFP, and GFC in rats with pyrexia. The results showed that the rectal temperature of rats treated with GF, GFP, and GFC was suppressed after 6 h (P < 0.05), compared with that observed in pyrexia model rats. The enzyme-linked immunosorbent assay showed that the expression of tumor necrosis factor α and interleukin 6 were suppressed by GF, GFP, and GFC. Moreover, GFC suppressed the expression of interleukin 6 significantly (P < 0.01). Of note, 11, 15, and 25 feature metabolites were identified in the GF, GFP, and GFC groups. Pathway analysis showed that GF mainly regulated the biosynthesis of valine, leucine, and isoleucine. Notably, GFP was involved in glycerophospholipid metabolism, while GFC was linked to glycerophospholipid and sphingolipid metabolism. These results suggested that GF, GFP, and GFC maintained their antipyretic effect despite heat processing. However, heat processing altered endogenous feature metabolites and certain pathways of GF, GFP, and GFC in rats with yeast-induced pyrexia to exert an antipyretic effect

Risk Prediction of Second Primary Malignancies in Primary Early-Stage Ovarian Cancer Survivors: A SEER-Based National Population-Based Cohort Study

Purpose: This study aimed to characterize the clinical features of early-stage ovarian cancer (OC) survivors with second primary malignancies (SPMs) and provided a prediction tool for individualized risk of developing SPMs. Methods: Data were obtained from the Surveillance, Epidemiology and End Results (SEER) database during 1998–2013. Considering non-SPM death as a competing event, the Fine and Gray model and the corresponding nomogram were used to identify the risk factors for SPMs and predict the SPM probabilities after the initial OC diagnosis. The decision curve analysis (DCA) was performed to evaluate the clinical utility of our proposed model. Results: A total of 14,314 qualified patients were enrolled. The diagnosis rate and the cumulative incidence of SPMs were 7.9% and 13.6% [95% confidence interval (CI) = 13.5% to 13.6%], respectively, during the median follow-up of 8.6 years. The multivariable competing risk analysis suggested that older age at initial cancer diagnosis, white race, epithelial histologic subtypes of OC (serous, endometrioid, mucinous, and Brenner tumor), number of lymph nodes examined (<12), and radiotherapy were significantly associated with an elevated SPM risk. The DCA revealed that the net benefit obtained by our proposed model was higher than the all-screening or no-screening scenarios within a wide range of risk thresholds (1% to 23%). Conclusion: The competing risk nomogram can be potentially helpful for assisting physicians in identifying patients with different risks of SPMs and scheduling risk-adapted clinical management. More comprehensive data on treatment regimens and patient characteristics may help improve the predictability of the risk model for SPMs

Nanoscale integrin cluster dynamics controls cellular mechanosensing via FAKY397 phosphorylation

Transduction of extracellular matrix mechanics affects cell migration, proliferation, and differentiation. While this mechanotransduction is known to depend on the regulation of focal adhesion kinase phosphorylation on Y397 (FAKpY397), the mechanism remains elusive. To address this, we developed a mathematical model to test the hypothesis that FAKpY397-based mechanosensing arises from the dynamics of nanoscale integrin clustering, stiffness-dependent disassembly of integrin clusters, and FAKY397 phosphorylation within integrin clusters. Modeling results predicted that integrin clustering dynamics governs how cells convert substrate stiffness to FAKpY397, and hence governs how different cell types transduce mechanical signals. Existing experiments on MDCK cells and HT1080 cells, as well as our new experiments on 3T3 fibroblasts, confirmed our predictions and supported our model. Our results suggest a new pathway by which integrin clusters enable cells to calibrate responses to their mechanical microenvironment

Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China:the PLANS (platelet lymphocyte age neutrophil sex) model

Background Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. Methods Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). Results The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. Conclusions The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality