Multi-Material and Multi-Functional 3D Printing: External Field Assisted Stereolithography

By combining various materials that serve mechanical, electrical, and thermal functions with controlled local distributions, smart devices and machines with multiple functionalities can be fabricated. The traditional manufacturing processes such as machining and molding limit the full functionality of particle–polymer composites owing to the lack of control on particle distribution. Multi-material additive manufacturing technology allows a higher degree of design by controlling orientations/alignments and local compositions of particles in the polymer matrix. In this work, the multi-material and multi-functional 3D printing process, external field assisted stereolithography, is developed and investigated. First, an additive manufacturing process, named Magnetic Field-assisted Projection Stereolithography (M-PSL), is presented for 3D printing of smart polymer composites. The magnetic alignment, curing mechanisms, and manufacturing process planning are discussed. Test cases have been successfully fabricated for remote control under external magnetic fields, showing the capability of printed smart polymer composites on performing desired functions. The printed magnetic field-responsive smart polymer composite creates a wide range of motions, opening up possibilities for various new applications, like sensing and actuation in soft robotics, biomedical devices, and autonomous systems. Besides magnetic alignment, this work reports another new particle patterning approach during additive manufacturing to fabricate multi-functional smart composite objects. An acoustic field is integrated into the projection based stereolithography system to pattern different micro- and nano- particles into dense parallel curves or networks in the liquid resin. Effects of acoustic field settings and manufacturing process parameters on patterning are modeled and experimentally characterized. Various particle patterning results are presented. The feasibility of the Acoustic-field-assisted Projection Stereolithography (A-PSL) process for multi-functional particle-polymer composite fabrication has been verified. In order to design and fabricate functional polymer composites with desired properties and functions, the correlation between micro-scale material distribution and macroscopic composite performance is investigated. Micro-scale particle distribution parameters, including particle loading fraction, particle assembly, microstructure orientation, and particle distribution patterns, are investigated. Magnetic functions, thermal functions and mechanical functions are tested. Test cases of remote control devices and thermal management application are demonstrated to verify the enhanced material properties and functionality of the printed polymer composites

Leveraging what you know: Versatile space-filling designs

Space-filling designs continue to gain popularity for computer experiments. Uniformity of space-filling characteristics has been broadly sought after to provide good estimation and prediction abilities for a variety of complex models. This article presents case studies when additional information on the features of the underlying relationship may be leveraged for selecting alternative space-filling designs that offer improvements to meet specific experimental goals. Three types of nontraditional space-filling designs are illustrated to achieve different objectives to (1) allow varied density of design points across the input space, (2) obtain balanced performance on covering the input space and the range of the response values, and (3) effectively augment existing runs to achieve certain space-filling characteristic in a sequential experiment. The mechanics for implementing these design choices are described and their flexibility to adapt to other experimental scenarios is illustrated.</p

Bioreactor Performance and Quantitative Analysis of Methanogenic and Bacterial Community Dynamics in Microbial Electrolysis Cells during Large Temperature Fluctuations

The use of microbial electrolysis cells (MECs) for H₂ production generally finds H₂ sink by undesirable methanogenesis at mesophilic temperatures. Previously reported approaches failed to effectively inhibit methanogenesis without the addition of nongreen chemical inhibitors. Here, we demonstrated that the CH₄ production and the number of methanogens in single-chamber MECs could be restricted steadily to a negligible level by continuously operating reactors at the relatively low temperature of 15 °C. This resulted in a H₂ yield and production rate comparable to those obtained at 30 °C with less CH₄ production (CH₄% < 1%). However, this operation at 15 °C should be taken from the initial stage of anodic biofilm formation, when the methanogenic community has not yet been established sufficiently. Maintaining MECs operating at 20 °C was not effective for controlling methanogenesis. The varying degrees of methanogenesis observed in MECs at 30 °C could be completely inhibited at 4 and 9 °C, and the total number of methanogens (mainly hydrogenotrophic methanogens) could be reduced by 68–91% during 32–55 days of operation at the low temperatures. However, methanogens cannot be eliminated completely at these temperatures. After the temperature is returned to 30 °C, the CH₄ production and the number of total methanogens can rapidly rise to the prior levels. Analysis of bacterial communities using 454 pyrosequencing showed that changes in temperature had no a substantial impact on composition of dominant electricity-producing bacteria (Geobacter). The results of our study provide more information toward understanding the temperature-dependent control of methanogenesis in MECs

Maximum likelihood and Bayesian phylogenetic trees

Tree files named as “RAxML_PB2.tre” et al. are maximum likelihood (ML) trees of six internal segments (PB2, PB1, PA, NP, M and NS) composed of the same 2343 North America AIV strains, which were reconstructed using RAxML v7.04. In each ML tree, the Mexico H7N3 strains are colored in red, and the lineage in which H7N3 Mexico fell is colored in blue; Tree files named as “HA.mcc.tre” et al. are temporally structured maximum clade credibility (mcc) time-scaled phylogenetic trees of all eight segments, which were generated using Beast V 1.7.3 and annotated with ancestral state changes (A: host order; B: host species; C: flyway; D: state/province; E: subtype) recovered from the discrete trait analyses. In each mcc tree, the branches are colored according to the trait state in the tree nodes. All tree files can be visualized in FigTree V 1.4.0

Additional file 3: Figure S9. of Quantifying predictors for the spatial diffusion of avian influenza virus in China

(A to F) Bayesian MCC phylogenies of 6 internal segments of 320 Chinese AIV sequences labelled with sequence names on tips and Bayesian posterior probability on nodes. (A) PB2; (B) PB1; (C) PA; (D) NP; (E) M; (F) NS. (ZIP 523 kb

Additional file 2: Figure S1. of Quantifying predictors for the spatial diffusion of avian influenza virus in China

Host distribution of 320 Chinese AIV sequences in Traditional Region, Economic Region, Economic Divided zone, and China Agro-Ecological Region types. Figure S2. Distributions of 320 Chinese AIV sequences in the sampled time, host order, subtype and sampled provinces. Figure S3. Influence of the sampling scheme on the phylogeographic reconstruction. Figure S4. PB1 tree mapping with region traits. Figure S5. PA tree mapping with region traits. Figure S6. NP tree mapping with region traits. Figure S7. M tree mapping with region traits. Figure S8. NS tree mapping with region traits. (PDF 864 kb

<i>I</i>-optimal or <i>G</i>-optimal: Do we have to choose?

When optimizing an experimental design for good prediction performance based on an assumed second order response surface model, it is common to focus on a single optimality criterion, either G-optimality, for best worst-case prediction precision, or I-optimality, for best average prediction precision. In this article, we illustrate how using particle swarm optimization to construct a Pareto front of non-dominated designs that balance these two criteria yields some highly desirable results. In most scenarios, there are designs that simultaneously perform well for both criteria. Seeing alternative designs that vary how they balance the performance of G- and I-efficiency provides experimenters with choices that allow selection of a better match for their study objectives. We provide an extensive repository of Pareto fronts with designs for 17 common experimental scenarios for 2 (design size N = 6 to 12), 3 (N = 10 to 16) and 4 (N = 15, 17, 20) experimental factors. These, when combined with a detailed strategy for how to efficiently analyze, assess, and select between alternatives, provide the reader with the tools to select the ideal design with a tailored balance between G- and I-optimality for their own experimental situations.</p

Time of the most recent common ancestors for the Mexico H7N3 virus.

a<p>Time of the most recent common ancestors (TMRCA) of each segment of the novel Mexico H7N3 virus are represented in the order of date/month/year. The values in parentheses represent the 95% HPD intervals.</p>b<p>The strains are identified are those most closely related to the outbreak strains in each tree phylogeny in this study.</p><p>Time of the most recent common ancestors for the Mexico H7N3 virus.</p

AICM estimates for the fit of different discrete trait models.

a<p>The names of the 8 models (Mod1-8) in the comparison test.</p>b<p>The estimated AICM score of the posterior: lower values of marginal likelihood indicate a better fit to the data. The model with the best performance is indicated in bold.</p>c<p>The standard error of the AICM estimated using 1000 bootstrap replicates.</p>d<p>The AICM comparisons are shown in the matrix composed of columns 4 to 11. In each row of the matrix, the positive value in a cell represents the support for one model (in column 1) over the other (indicated in the column titles). A difference of AICM = 10 is considered to indicate a strong preference for one model over another.</p><p>AICM estimates for the fit of different discrete trait models.</p

Maximum clade credibility (MCC) phylogenies for the HA segment.

<p>Branches are coloured according to the 4 discrete traits (host order, host species, flyway and location) on internal nodes. Mexican outbreak strains are highlighted with pink. A: Host order. Five host orders are labelled on HA tree: wild birds of the order Anseriformes (ans-wild); wild birds of the order Charadriiformes (cha-wild); wild birds of the order Passeriformes (pas-wild); domestic birds of the order Galliformes and Mexico H7N3 outbreak in the order Galliformes (gal-domestic-Mexico). B: Host species. Wild Anseriformes are classified into the five main species and a group comprising the other rarer species of Anseriformes in this study: mallard (Anas platyrhynchos), northern pintail (Anasacuta), northern shoveller, blue-winged teal, green-winged teal and other Anseriformes (other ans); The order Galliformes are shown as “outbreak” (the H7N3 Mexico outbreak) and “other_gal”; The other orders are shown as: Charadriiformes (cha) and Galliformes (gal), Gruiformes (gru) and Passeriformes (pas). C: Flyway. Four specific North American flyways are labelled on the HA tree: the Atlantic, Mississippi, Central, and Pacific. D: State. 22 states and provinces of the viral sample locations are labelled on the HA tree. The original MCC tree files with all taxa names are deposited in Dryad (doi:10.5061/dryad.j5bf8), and trees for the other 7 segments without taxa names can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s002" target="_blank">Figure S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s003" target="_blank">S3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s004" target="_blank">S4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s005" target="_blank">S5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s006" target="_blank">S6</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s007" target="_blank">S7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107330#pone.0107330.s008" target="_blank">S8</a>.</p