Controllable Internal Mixing in Coalescing Droplets Induced by The Solutal Marangoni Convection of Surfactants with Distinct Headgroup Architectures

Through several complementary experiments, an investigation of the bulk and interfacial flows that emerged during the coalescence of two water-in-oil droplets with asymmetric compositional properties was performed. By adding surfactant to one of the coalescing droplets and leaving the other surfactant-free, a strong interfacial tension gradient (i.e., solutal Marangoni) driving energy between the merging droplets generated pronounced internal mixing. The contributions of two distinct types of surfactant, anionic ammonium lauryl sulfate (ALS) and cationic cetyltrimethylammonium bromide (CTAB) on the rate of coalescence bridge expansion and on the generation of opposing flows during coalescence were investigated. All coalescence experiments supported the power law relation between the radius of the expanding connective liquid bridge and time, rb ∝ t1/2. However, the presence of surfactant decreased the magnitude of the prefactor in this relationship due to induced interfacial solutal Marangoni convection. Experiments showed that packing efficiency, diffusivity, and bulk concentration of the selected surfactant are vital in solutal Marangoni convection and thus the degree and timescale of internal mixing between merging droplets, which has yet to be adequately discussed within the literature. Denser interfacial packing efficiency and lower diffusivity of CTAB produced stronger opposing bulk and interfacial flow as well as greater bulk mixing. A discussion of how optimized surfactant selection and solutal Marangoni convection can be used for passively inducing convective mixing between coalescing drops in microfluidic channels when viscosity modulation is not feasible is provided

Microstructure and yielding of microfiber gels

Large aspect ratio cellulose nanofibers are able to a form poroelastic network at low volume fractions via aggregation and entanglement, forming a gel without significantly modifying viscosity[1]. The gels have a small but useful yield stress and a better ability to suspend particles than non-interacting higher volume fraction glasses[2] because the sparse fiber networks can significantly restructure at small strains. Yielding behavior can thus strongly depend on the fluid microstructure[3]. We study here deformation and yielding of aqueous cellulose fiber gels. Confocal imaging shows how gel yield stress relates to structural deformation rate because of localized network restructuring. Such response is advantageous to applications like surface coatings, nasal sprays, cosmetics, and foods. Understanding the mechanism of rate- and length-scale dependent yielding, and relating microstructure changes to bulk rheology[4], will enhance our ability to formulate, model, and design complex fluids with novel performance. References [1] - Solomon MJ, Spicer PT. Microstructural regimes of colloidal rod suspensions, gels, and glasses. Soft Matter, 6, 1391 (2010). [2] - Emady H, Caggioni M, Spicer P. Colloidal microstructure effects on particle sedimentation in yield stress fluids. J Rheol. 57, 1761 (2013). [3] - Joshi YM. Dynamics of colloidal glasses and gels. Annu Rev Chem Biomol Eng. 5, 181, (2014). [4] - Hsiao L, Newman RS, Glotzer SC, Solomon MJ. Role of isostaticity and load-bearing microstructure in the elasticity of yielded colloidal gels. Proc Natl Acad Sci, 109, 16029, (2012

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article