Search

Polymer electrolyte membrane water electrolysis (PEMWE) is regarded as one of the most important cornerstone technologies in the upcoming hydrogen society. However, one of the major problems it encounters is its slow oxygen evolution kinetics, which necessitates the use of large amounts of precious metal catalysts to ensure a satisfactory reaction rate. Herein, we have prepared a series of RuO

A novel photonic crystal aptamer biosensor SiO

Tri-n-butyl phosphate (TnBP) is a widely used organophosphate ester, but its effects on the regenerative process under damaging circumstances remain unknown. In the present study, zebrafish larvae were exposed to 0, 50, 100, 200 and 1000 μg/L TnBP, and the caudal fins were cut at 72 hours post fertilization (hpf). First, after exposure to TnBP, the number of total neutrophils decreased together with decreased neutrophils in the tail, and TnBP inhibited chemotaxis. Second, reactive oxygen species (ROS) levels in the zebrafish decreased greatly. Following exposure to TnBP, transcription levels of many genes regulating fin regeneration, such as fgf20a, fgfr1a, bmp2a and bmp4, were significantly downregulated, while inflammatory factors such as cxcl8a, cxcl18b, il-6, and tnfa were abnormally upregulated. In addition, TnBP inhibited the regenerative area after caudal fin amputation. The inflammatory state was adverse during the regenerative process. In summary, TnBP exposure is immunotoxic and decreases oxidative stress in injured zebrafish larvae.

A kind of novel multi-stimuli responsive molecularly imprinted polymers with bovine serum albumin (BSA) as a dummy template (MSR-BSA-MIPs) was fabricated for specific recognition of human serum albumin (HSA) with modulated affinity. The MSR-BSA-MIPs were prepared through free radical polymerization using vinyl modified magnetic nanoparticles as substrates, bovine serum albumin (BSA), with high amino acid sequence similarity but low price compared to HSA, as the dummy template

It is an important challenge to effectively extract and determine pesticides in complex samples. Covalent organic frameworks (COFs) are burgeoning porous crystalline organic materials with good environmental resistance, thus demonstrating great potential as adsorbents in contaminants detection. In this work, we design and synthesize a novel COF-TpDB via 1,3,5-triformylphloroglucinol (Tp) and 4,4'-diaminobenzoylanilide (DB) as well as its packed cartridge for solid phase extraction (SPE) of carbamate pesticides. Simulation calculations showed H-bonding facilitates the adsorption interactions between the carbamate pesticides and TpDB. A method was developed by coupling TpDB as SPE sorbents with high performance liquid chromatography-ultraviolet (HPLC-UV) detection to determine trace carbamate pesticides in vegetables. The established method showed a wide linear range of 0.1-200 ng mL

With the increasing complexity of the sample properties in analytical chemistry and the lower concentrations of the detected substances, it is imperative to develop accurate and efficient sample pretreatment processes before chromatography and mass spectrometry analyses. Magnetic solid-phase extraction (MSPE) can be regarded as an efficient sample pretreatment method due to the simple synthesis, convenient operation, and high extraction efficiency. In recent years, Fe

With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming; the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.

The effective adsorption and sensitive determination of microcystin-LR (MC-LR) are crucial for the environment and human health. In this work, a highly fluorinated magnetic covalent organic framework (denoted as Fe

The oxygen evolution reaction (OER) is the efficiency-determining half-reaction process of high-demand, electricity-driven water splitting due to its sluggish four-electron transfer reaction. Tremendous effects on developing OER catalysts with high activity and strong acid-tolerance at high oxidation potentials have been made for proton-conducting polymer electrolyte membrane water electrolysis (PEMWE), which is one of the most promising future hydrogen-fuel-generating technologies. This review presents recent progress in understanding OER mechanisms in PEMWE, including the adsorbate evolution mechanism (AEM) and the lattice-oxygen-mediated mechanism (LOM). We further summarize the latest strategies to improve catalytic performance, such as surface/interface modification, catalytic site coordination construction, and electronic structure regulation of catalytic centers. Finally, challenges and prospective solutions for improving OER performance are proposed.

In this work, the electrospun polyacrylonitrile/covalent organic frameworks Tp-BD nanofibers (PAN/Tp-BD) were synthesized and applied as an adsorbent for thin film microextraction (TFME) of seven sulfonamides in animal derived food samples. The morphology, structure, porosity, and stability of the prepared nanofibers were investigated. The PAN/Tp-BD nanofibers exhibited good chemical stability, high flexibility, porous fibrous structure, and excellent extraction efficiency. Based on the PAN/Tp-BD nanofibers as the adsorbent, a thin film microextraction-high performance liquid chromatography (TFME-HPLC) method for the determination of seven sulfonamides (SAs) in food samples was developed. Under the optimal conditions, the TFME-HPLC exhibited the low limit of detection (0.10-0.18 ng·mL

In this work, a novel strategy to prepare molecularly imprinted polymers (MIPs) functionalized magnetic carbon nanotubes (MCNTs) via a facile sol-gel polymerization by adopting Cu

Biomedical sciences, and in particular biomarker research, demand efficient glycoprotein enrichment platforms. In this paper, a facile and efficient approach combining distillation-precipitation polymerization (DPP) and click chemistry was developed to synthesize boronic acid ligand-modified magnetic nanoparticles for the enrichment of glycoproteins. Due to the relatively large amount of benzyl chloride groups introduced by DPP on the magnetic core, which easily can be transferred into azide groups, the alkyne-phenylboronic acid ligands were immobilized onto the surface of Fe

Microscale flow-through electroporation at DC voltage has advantages in delivering small molecules. Yet, electroporation based on constant voltage are liable to generate electrolysis products which limits the voltage-operating window. Parallel on-chip 3D electrodes with close and uniform spacing are required to cut down voltage as well as provide enough electric field for electroporation. Here we present a simple electrode fabrication method based on capillary restriction valves in Z-axis to achieve parallel 3D electrodes with controllable electrode spacing in PDMS chips. With electrodes accurately placed in close range, a low voltage of only 1.5 V can generate enough electric field (>400 V/cm) to make cell membrane permeable. Squeeze flow is introduced to produce higher electric field (>800 V/cm) at a fixed voltage for more efficient electroporation. Benefit from the electrode fabrication method and application of squeeze flow, we develop a smartphone controlled microfluidic electroporation system which integrate functions of sample injection, pressure regulating, real-time observation and constant DC power supply. The system is used to electroporate two cell lines, showing a permeabilization percentage of 63% for HEK-293 cells and 58% for CHO-K1 cells with optimal parameters. Thus, the portable microfluidic system provides a cost-effective and user-friendly flow-through cell electroporation platform.

We present a facile strategy to prepare the molecularly imprinted polymers layer on the surface of Fe

AI benchmarking provides yardsticks for benchmarking, measuring and evaluating innovative AI algorithms, architecture, and systems. Coordinated by BenchCouncil, this paper presents our joint research and engineering efforts with several academic and industrial partners on the datacenter AI benchmarks—AIBench. The benchmarks are publicly available from http://www.benchcouncil.org/AIBench/index.html. Presently, AIBench covers 16 problem domains, including image classification, image generation, text-to-text translation, image-to-text, image-to-image, speech-to-text, face embedding, 3D face recognition, object detection, video prediction, image compression, recommendation, 3D object reconstruction, text summarization, spatial transformer, and learning to rank, and two end-to-end application AI benchmarks. Meanwhile, the AI benchmark suites for high performance computing (HPC), IoT, Edge are also released on the BenchCouncil web site. This is by far the most comprehensive AI benchmarking research and engineering effort.

The complexity and diversity of big data and AI workloads make understanding them difficult and challenging. This paper proposes a new approach to modelling and characterizing big data and AI workloads. We consider each big data and AI workload as a pipeline of one or more classes of units of computation performed on different initial or intermediate data inputs. Each class of unit of computation captures the common requirements while being reasonably divorced from individual implementations, and hence we call it a data motif. For the first time, among a wide variety of big data and AI workloads, we identify eight data motifs that take up most of the run time of those workloads, including Matrix, Sampling, Logic, Transform, Set, Graph, Sort and Statistic. We implement the eight data motifs on different software stacks as the micro benchmarks of an open-source big data and AI benchmark suite ---BigDataBench 4.0 (publicly available from http://prof.ict.ac.cn/BigDataBench), and perform comprehensive characterization of those data motifs from perspective of data sizes, types, sources, and patterns as a lens towards fully understanding big data and AI workloads. We believe the eight data motifs are promising abstractions and tools for not only big data and AI benchmarking, but also domain-specific hardware and software co-design., Comment: The paper will be published on The 27th International Conference on Parallel Architectures and Compilation Techniques (PACT18)

For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too huge at binary size level and prohibitively expensive to run on cycle-accurate simulators. The concept of data motif, which is identified as a class of units of computation performed on initial or intermediate data, is the first step towards building proxy benchmark to mimic the real-world big data and AI workloads. However, there is no practical way to construct a proxy benchmark based on the data motifs to help simulation-based research. In this paper, we embark on a study to bridge the gap between data motif and a practical proxy benchmark. We propose a data motif-based proxy benchmark generating methodology by means of machine learning method, which combine data motifs with different weights to mimic the big data and AI workloads. Furthermore, we implement various data motifs using light-weight stacks and apply the methodology to five real-world workloads to construct a suite of proxy benchmarks, considering the data types, patterns, and distributions. The evaluation results show that our proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures. To facilitate the community, we will release the proxy benchmarks on the project homepage http://prof.ict.ac.cn/BigDataBench., Comment: The paper will be published on 2018 IEEE International Symposium on Workload Characterization (IISWC 2018). arXiv admin note: text overlap with arXiv:1802.00699, arXiv:1711.03229

Sparse Matrix-vector Multiplication (SpMV) is an important computation kernel widely used in HPC and data centers. The irregularity of SpMV is a well-known challenge that limits SpMV’s parallelism with vectorization operations. Existing work achieves limited locality and vectorization efficiency with large preprocessing overheads. To address this issue, we present the Compressed Vectorization-oriented sparse Row (CVR), a novel SpMV representation targeting efficient vectorization. The CVR simultaneously processes multiple rows within the input matrix to increase cache efficiency and separates them into multiple SIMD lanes so as to take the advantage of vector processing units in modern processors. Our method is insensitive to the sparsity and irregularity of SpMV, and thus able to deal with various scale-free and HPC matrices. We implement and evaluate CVR on an Intel Knights Landing processor and compare it with five state-of-the-art approaches through using 58 scale-free and HPC sparse matrices. Experimental results show that CVR can achieve a speedup up to 1.70 × (1.33× on average) and a speedup up to 1.57× (1.10× on average) over the best existing approaches for scale-free and HPC sparse matrices, respectively. Moreover, CVR typically incurs the lowest preprocessing overhead compared with state-of-the-art approaches.