Mechanical Properties of Cellulose Nanofibril-Filled Polypropylene Composites

Cellulose nanofiber (CNF), microfibrillated cellulose (MFC), and microcrystalline cellulose (MCC) filled-polypropylene (PP) composite samples were manufactured using a melt mixing technique. Mechanical testing was conducted to investigate tensile and flexural properties of the composites at different filler loading levels. Test results showed that in the case of cellulose nanofibril fillers, the composites sustained considerable tensile strength up to 10% (w/w) filler loading whereas the tensile strength of the MCC-filled composites decreased continuously. Moreover, tensile modulus increased as filler loading increased for all cellulose fillers. CNF and MCC-filled composites demonstrate plastic deformation and longer elongation at break than MFC-filled composites while MFC-filled composites exhibited a quasi-brittle behavior under tensile deformation. Flexural strength of cellulose nanofibril-filled composites decreased slightly as a function of filler loading up to 6% (w/w) and increased beyond 6% (w/w). The 10% (w/w) cellulose nanofibril-filled composite samples exhibited sustained flexural strength as compared with neat PP. The trend of increased flexural modulus of elasticity behavior was identical to the tensile modulus of elasticity behavior

Determining the Mechanical Properties of Microcrystalline Cellulose (MCC)-Filled PET-PTT Blend Composites

Polymer composite materials consisting of poly(ethylene terephthalate) (PET)-poly(trimethylene terephthalate) (PTT) blends and microcrystalline cellulose (MCC) were prepared by injection molding. The composites were analyzed for tensile, flexural, and impact strength as well as density determinations. There was no statistical difference in terms of mechanical properties between the control PET-PTT blend and 2.5 wt% MCC-filled composites. Because of better compatibility as well as better stress-transfer properties, the tensile strength of the composites was larger (reaching values from 24.8-36.3 MPa with the addition of 20 wt% MCC). Elongation at break of the composites was greater (reaching values from 2.3-3.3% with the addition of 20 wt% MCC). The tensile modulus of MCC-filled composites systemically increased with increasing MCC loading (reaching values from 1.11-1.68 GPa with the addition of 30 wt% MCC). The flexural modulus of composites was higher than the control PET-PTT blend. The modulus also increased with increasing MCC loading (reaching values from 2.10-3.37 GPa with the addition of 30 wt% MCC). The Izod impact strength of the composites decreased as the MCC loading increased and this observation was in good agreement with commonly observed filled polymer systems

GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6 pages, 8 figure

Prediction of Advanced Fibrosis in Nonalcoholic Fatty Liver Disease: An Enhanced Model of BARD Score.

UNLABELLED: BACKGROUNDAIMS: The BARD score is a model to detect advanced liver fibrosis in nonalcoholic fatty liver disease (NAFLD) patients. The aims of this study were to identify additional factors and then to build an enhanced version of the BARD score. METHODS: One hundred seven patients with biopsy-proven NAFLD were enrolled retrospectively. Logistic regressions were performed to identify independent risk factors for advanced liver fibrosis (stage 3 or 4). An enhanced model of the BARD score (BARDI score) was built and evaluated with a receiver operating characteristic (ROC) curve. RESULTS: In multivariate analysis, age (odds ratio [OR], 0.89; p=0.04), aspartate aminotransferase/alanine aminotransferase ratio (OR, 1.73; p CONCLUSIONS: The BARDI score had an improved PPV over the BARD score and maintained an excellent NPV. Further study is warranted for its external validation and comparison with other models

Heterologous gene expression using self-assembled supra-molecules with high affinity for HSP70 chaperone

Contrary to the results of direct expression, various human proteins (ferritin light-chain, epithermal growth factor, interleukin-2, prepro-ghrelin, deletion mutants of glutamate decarboxylase and arginine deiminase, and mini-proinsulin) were all soluble in Escherichia coli cytoplasm when expressed with the N-terminus fusion of ferritin heavy-chain (FTN-H). Through systematic investigations, we have found that a specific peptide motif within FTN-H has a high affinity to HSP70 chaperone DnaK, and that the peptide motif was composed of a hydrophobic core of three residues (Ile, Phe and Leu) and two flanking regions enriched with polar residues (Gly, Gln and Arg). It was also observed that all the recombinant proteins expressed with the fusion of FTN-H formed spherical nanoparticles with diameters of 10–15 nm, as confirmed by the transmission electron microscopy image. The protein nanoparticles are non-covalently cross-linked supra-molecules formed by the self-assembly function of FTN-H. Upon the formation of the supra-molecule, its size is likely to be limited by the assembly properties of FTN-H, thereby keeping the self-assembled particles soluble. This study reports on the dual function of FTN-H for fusion expression and solubility enhancement of heterologous proteins: (i) high-affinity interaction with DnaK and (ii) formation of self-assembled supra-molecules with limited and constant sizes, thereby avoiding the undesirable formation of insoluble macro-aggregates of heterologous proteins

Observation of superabsorption by correlated atoms

Emission and absorption of light lie at the heart of light-matter interaction. Although the emission and absorption rates are regarded as intrinsic properties of atoms and molecules, various ways to modify these rates have been sought in critical applications such as quantum information processing, metrology and light-energy harvesting. One of the promising approaches is to utilize collective behavior of emitters as in superradiance. Although superradiance has been observed in diverse systems, its conceptual counterpart in absorption has never been realized. Here, we demonstrate superabsorption, enhanced cooperative absorption, by correlated atoms of phase-matched superposition state. By implementing an opposite-phase-interference idea on a superradiant state or equivalently a time-reversal process of superradiance, we realized the superabsorption with its absorption rate much faster than that of the ordinary ground-state absorption. The number of photons completely absorbed for a given time interval was measured to be proportional to the square of the number of atoms. Our approach, breaking the limitation of the conventional absorption, can help weak-signal sensing and advance efficient light-energy harvesting as well as light-matter quantum interfaces.Comment: 7 pages, 5 figure