Mixed metal-organic framework mixed-matrix membranes : insights into simultaneous moisture-triggered and catalytic delivery of nitric oxide using cryo-scanning electron microscopy

Funding: This work was supported by the European Research Council grant ADOR (Advanced Grant 787073). The authors acknowledge the EPSRC Light Element Analysis Facility Grant (EP/T019298/1) and the EPSRC Strategic Equipment Resource Grant (EP/R023751/1).The fundamental chemical and structural diversity of metal–organic frameworks (MOFs) is vast, but there is a lack of industrial adoption of these extremely versatile compounds. To bridge the gap between basic research and industry, MOF powders must be formulated into more application-relevant shapes and/or composites. Successful incorporation of varying ratios of two different MOFs, CPO-27-Ni and CuBTTri, in a thin polymer film represents an important step toward the development of mixed MOF mixed-matrix membranes. To gain insight into the distribution of the two different MOFs in the polymer, we report their investigation by Cryo-scanning electron microscopy (Cryo-SEM) tomography, which minimizes surface charging and electron beam-induced damage. Because the MOFs are based on two different metal ions, Ni and Cu, the elemental maps of the MOF composite cross sections clearly identify the size and location of each MOF in the reconstructed 3D model. The tomography run was about six times faster than conventional focused ion beam (FIB)-SEM and the first insights to image segmentation combined with machine learning could be achieved. To verify that the MOF composites combined the benefits of rapid moisture-triggered release of nitric oxide (NO) from CPO-27-Ni with the continuous catalytic generation of NO from CuBTTri, we characterized their ability to deliver NO individually and simultaneously. These MOF composites show great promise to achieve optimal dual NO delivery in real-world medical applications.Publisher PDFPeer reviewe

KSU Chamber Singers, A Litany for Courage and the Seasons

Kennesaw State University Chamber Singers present David Maslanka\u27s A Litany for Courage and the Seasons, six songs for chorus, clarinet and vibraphone on poems of Richard Beale at the 2013 National Collegiate Choral Organization 5th National Conference in Charleston, South Carolina, October 31 - November 2.https://digitalcommons.kennesaw.edu/musicprograms/1268/thumbnail.jp

KSU Chamber Singers and Men\u27s Ensemble

KSU School of Music presents KSU Chamber Singers and Men\u27s Ensemble.https://digitalcommons.kennesaw.edu/musicprograms/1294/thumbnail.jp

Choral Ensembles Holiday Concert

KSU School of Music presents Choral Ensembles Holiday Concert.https://digitalcommons.kennesaw.edu/musicprograms/1323/thumbnail.jp

Holiday Concert, Home for the Holidays

Kennesaw State University School of Music presents Holiday Concert, Home for the Holidays.https://digitalcommons.kennesaw.edu/musicprograms/1433/thumbnail.jp

American Choral Directors Association Preview Concert with Guest Artist Ola Gjeilo, composer

Kennesaw State University School of Music presents the ACDA National Conference American Choral Directors Association Preview Concert with guest artist Ola Gjeilo, composer.https://digitalcommons.kennesaw.edu/musicprograms/1337/thumbnail.jp

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.Comment: 17 pages, 11 figures, 8 tables. To appear at the 44th International Symposium on Computer Architecture (ISCA), Toronto, Canada, June 24-28, 201

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Mixed metal-organic framework mixed-matrix membranes:insights into simultaneous moisture-triggered and catalytic delivery of nitric oxide using cryo-scanning electron microscopy

The fundamental chemical and structural diversity of metal–organic frameworks (MOFs) is vast, but there is a lack of industrial adoption of these extremely versatile compounds. To bridge the gap between basic research and industry, MOF powders must be formulated into more application-relevant shapes and/or composites. Successful incorporation of varying ratios of two different MOFs, CPO-27-Ni and CuBTTri, in a thin polymer film represents an important step toward the development of mixed MOF mixed-matrix membranes. To gain insight into the distribution of the two different MOFs in the polymer, we report their investigation by Cryo-scanning electron microscopy (Cryo-SEM) tomography, which minimizes surface charging and electron beam-induced damage. Because the MOFs are based on two different metal ions, Ni and Cu, the elemental maps of the MOF composite cross sections clearly identify the size and location of each MOF in the reconstructed 3D model. The tomography run was about six times faster than conventional focused ion beam (FIB)-SEM and the first insights to image segmentation combined with machine learning could be achieved. To verify that the MOF composites combined the benefits of rapid moisture-triggered release of nitric oxide (NO) from CPO-27-Ni with the continuous catalytic generation of NO from CuBTTri, we characterized their ability to deliver NO individually and simultaneously. These MOF composites show great promise to achieve optimal dual NO delivery in real-world medical applications