Advances in rapid and effective break-in/conditioning/recovery of automotive PEMFC stacks

Automotive proton exchange membrane fuel cell stacks need to meet manufacturer specified rated beginning-of-life (BOL) performance before being assembled into vehicles and shipped off to customers. The process of “breaking-in” of a freshly assembled stack is often referred to as “conditioning.” It has become an intensely researched area especially in automotive companies, where imminent commercialization of fuel cell electric vehicles (FCEVs) demands a short, energy- and cost-efficient, and practical conditioning protocol. Significant advances in reducing the conditioning time from 1 to 2 days to as low as 4h or less, in some cases without the use of additional inert gases such as nitrogen, and with minimal use of hydrogen, and specialized test stations will be discussed.     

A novel low power hybrid cache using GC-EDRAM cells

In a typical embedded CPU, large on-chip storage is critical to meet high performance requirements. However, the fast increasing size of the on-chip storage based on traditional SRAM cells makes the area cost and energy consumption unsustainable for future embedded applications. Replacing SRAM with DRAM on the CPU’s chip is generally considered not worthwhile because DRAM is not compatible with the common CMOS logic and requires additional processing steps beyond what is required for CMOS. However a special DRAM technology, Gain-Cell embedded-DRAM (GC-eDRAM)  [1], [2], [3] is logic compatible and retains some of the good properties of DRAM (small and low power). In this paper we evaluate the performance of a novel hybrid cache memory where the data array, generally populated with SRAM cells, is replaced with GC-eDRAM cells while the tag array continues to use SRAM cells. Our evaluation of this cache demonstrates that, compared to the conventional SRAM-based designs, our novel architecture exhibits comparable performance with less energy consumption and smaller silicon area, enabling the sustainable on-chip storage scaling for future embedded CPUs.     

Nanovaccine-Mediated Cell Selective Delivery of Neoantigens Potentiating Adoptive Dendritic Cell Transfer for Personalized Immunization

Neoantigen vaccines and adoptive dendritic cell (DC) transfer are major clinical approaches to initiate personalized immunity in cancer patients. However, the immunization efficacy is largely limited by the in vivo trajectory including neoantigens’ access to resident DCs and DCs’ access to lymph nodes (LNs). Herein, an innovative strategy is proposed to improve personalized immunization through neoantigen-loaded nanovaccines synergized with adoptive DC transfer. It is found that it enables selective delivery of neoantigens to resident DCs and macrophages by coating cancer cell membranes onto neoantigen-loaded nanoparticles. In addition, the nanovaccines promote the secretion of chemokine C-C motif ligand 2 (CCL2), CCL3, and C-X-C motif ligand 10 from macrophages, thus potentiating the access of transferred DCs to LNs. This immunization strategy enables coordinated delivery of identified neoantigens and autologous tumor lysate-derived undefined antigens, leading to initiation of antitumor T cell immunity in a personalized manner. It significantly inhibits tumor growth in prophylactic and established mouse tumor models. The findings provide a new vision for potentiating adoptive cell transfer by nanovaccines, which may open the door to a transformative possibility for improving personalized immunization.     

Enhanced Power Density of Alcohol Biofuel Cell by Polymer-assisted Crosslinks of 3D Graphene on Carbon Paper as the Bioanode

Herein, we successfully construct the 3D biocompatible graphene through crosslinking 2D graphene nanosheet onto carbon fiber paper with poly(diallyldimethylammonium chloride) (PDDA) as anode of the alcohol biofuel cell. Compared with the bioanode without 3D graphene, the current density and output power of PDDA-graphene-ADH bioanode is increased by 23 % and 41 % at a high concentration of ethanol at pH 8.9, suggesting the stabilization role of graphene in enzyme loading. The study provides us a deep analysis on structures and performances of the bioanode incl. electrochemistry, X-ray photoelectron spectra, and atomic force microscopy images, which is significant to develop the new methods to construct 3D porous electrodes in energy conversion device.     

Simultaneous enhancement of the efficiency and stability of organic solar cells using PEDOT:PSS grafted with a PEGME buffer layer

We report a simple processing method to simultaneously improve the efficiency and stability of organic solar cells (OSCs). Poly(4-styrene sulfonate)-doped poly(3,4-ethylenedioxy-thiophene (PEDOT:PSS), widely used as hole transport layer (HTL) in OSCs, tends to accelerate the degradation of devices because of its hygroscopic and acidic properties. In this regard, we have modified PEDOT:PSS to reduce its hygroscopic and acidic properties through a condensation reaction between PEDOT:PSS and poly(ethylene glycol) methyl ether (PEGME) in order to improve the efficiency and stability of OSCs. As a result, the power conversion efficiency (PCE) increased by 21%, from 2.57% up to 3.11%. A better energy level alignment by the reduced work function of the modified PEDOT:PSS with a highest occupied molecular orbital (HOMO) level of poly(3-hexylthiophene-2,5-diyl) (P3HT) is considered the origin of the improved the efficiency. The half-life of OSCs with PEDOT:PSS modified with PEGME buffer layer also increased up to 3.5 times compared to that of devices with pristine PEDOT:PSS buffer layer.     

Towards a Better Prediction of Cell Settling on Nanostructure Arrays—Simple Means to Complicated Ends

Vertical arrays of nanostructures (NSs) are emerging as promising platforms for probing and manipulating live mammalian cells. The broad range of applications requires different types of interfaces, but cell settling on NS arrays is not yet fully controlled and understood. Cells are both seen to deform completely into NS arrays and to stay suspended like tiny fakirs, which have hitherto been explained with differences in NS spacing or density. Here, a better understanding of this phenomenon is provided by using a model that takes into account the extreme membrane deformation needed for a cell to settle into a NS array. It is shown that, in addition to the NS density, cell settling depends strongly on the dimensions of the single NS, and that the settling can be predicted for a given NS array geometry. The predictive power of the model is confirmed by experiments and good agreement with cases from the literature. Furthermore, the influence of cell‐related parameters is evaluated theoretically and a generic method of tuning cell settling through surface coating is demonstrated experimentally. These findings allow a more rational design of NS arrays for the numerous exciting biological applications where the mode of cell settling is crucial.     

An analytical model for optimizing the performance of graphene based silicon Schottky barrier solar cells

In this paper, a model taking into account the effects of carrier loss mechanisms has been developed. The model simulates the photovoltaic properties of the graphene/n-type silicon Schottky barrier solar cells (G/n-Si_SBSC), and it can reproduce the experimentally determined parameters of the G/n-Si_SBSC. To overcome the low efficiencies of G/n-Si_SBSC, their performances have been optimized by modifying the work function of graphene and Si properties, accounted for variation of its thickness and doping level. The obtained results show that the work function of graphene has the major impact on the device performance. Also, the temperature dependence of the G/n-Si_SBSC performance is investigated.     

Study of metal assisted anisotropic chemical etching of silicon for high aspect ratio in crystalline silicon solar cells

Textured surface is commonly used to enhance the efficiency of silicon solar cells by reducing the overall reflectance and improving the light scattering. In this study, a comparison between isotropic and anisotropic etching methods was investigated. The deep funnel shaped structures with high aspect ratio are proposed for better light trapping with low reflectance in crystalline silicon solar cells. The anisotropic metal assisted chemical etching (MACE) was used to form the funnel shaped structures with various aspect ratios. The funnel shaped structures showed an average reflectance of 14.75% while it was 15.77% for the pillar shaped structures. The average reflectance was further reduced to 9.49% using deep funnel shaped structures with an aspect ratio of 1:1.18. The deep funnel shaped structures with high aspect ratios can be employed for high performance of crystalline silicon solar cells.     

Specific emitter identification for satellite communication using probabilistic neural networks

Electromagnetic signal emitted by satellite communication (satcom) transmitters are used to identify specific individual uplink satcom terminals sharing the common transponder in real environment, which is known as specific emitter identification (SEI) that allows for early indications and warning (I&W) of the targets carrying satcom furnishment and furthermore the real time electromagnetic situation awareness in military operations. In this paper, the authors are the first to propose the identification of specific transmitters of satcom by using probabilistic neural networks (PNN) to reach the goal of target recognition. We have been devoted to the examination by exploring the feasibility of utilizing the Hilbert transform to signal preprocessing, applying the discrete wavelet transform to feature extraction, and employing the PNN to perform the classification of stationary signals. There are a total of 1000 sampling time series with binary phase shift keying (BPSK) modulation originated by five types of satcom transmitters in the test. The established PNNs classifier implements the data testing and finally yields satisfactory accuracy at 8 dB(±1 dB) carrier to noise ratio, which indicates the feasibility of our method, and even the keen insight of its application in military.     

基于电化学老化衰退模型的锂离子动力电池外特性

当前锂离子动力电池电化学模型存在模型复杂、建模难度大、计算效率低、老化评估效果差的问题,本文提出一种考虑电池衰退老化的机理模型(ADME).本文首先通过有限差分法对伪二维(P2D)电化学模型进行离散降阶处理,得到简化伪二维(SP2D)模型.在SP2D模型的基础上,基于阴阳两极发生的副反应导致的衰退老化现象,提出一种考虑电池衰退老化的机理模型.其次,使用多变量偏差补偿最小二乘法实现模型参数辨识.最后通过动力电池衰退老化性能循环实验,对比分析了恒流、脉冲工况下SP2D模型和ADME模型的终端电压输出.结果表明:ADME模型较为简单、计算效率和估算精度高,可以有效评估电池容量老化衰退,得到理想的锂离子动力电池外特性曲线.