Alternative green biocatalysts to induce carbonate precipitation for applications on building materials

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Coulombic surface-ion interactions induce non-linear and chemistry-specific charging kinetics

While important for many industrial applications, chemical reactions responsible for charging of solids in water are often poorly understood. We theoretically investigate the charging kinetics of solid-liquid interfaces, and find that the time-dependent equilibration of surface charge contains key information not only on the reaction mechanism, but also on the valency of the reacting ions. We construct a non-linear differential equation describing surface charging by combining chemical Langmuir kinetics and electrostatic Poisson-Boltzmann theory. Our results reveal a clear distinction between late-time (near-equilibrium) and short-time (far-from-equilibrium) relaxation rates, the ratio of which contains information on the charge valency and ad- or desorption mechanism of the charging process. Similarly, we find that single-ion reactions can be distinguished from two-ion reactions as the latter show an inflection point during equilibration. Interestingly, such inflection points are characteristic of autocatalytic reactions, and we conclude that the Coulombic ion-surface interaction is an autocatalytic feedback mechanism.Comment: 7 pages, 2 figures, Supplementary Information: 3 pages, 1 figur

Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Single cells can be characterized in terms of their phenotypic properties using flow cytometry. However, up to our knowledge there has not yet been a thorough survey concerning the classification of bacterial species based on flow cytometric data. This paper aims to perform a thorough investigation concerning the identification of bacterial communities of various complexities in species richness. We do this by creating so-called in silico communities, communities created by aggregating the data coming from individual cultures; moreover we show that it is possible to use in silico communities to identify in vitro created communities as well, proving the biological relevance and usability of bacterial in silico communities

Surface consolidation of natural stones by use of bio-agents and chemical consolidants

Surface treatment is a frequently used method for conservation and restoration of building materials. . In this study, a novel and environment friendly strategy, bacterially induced calcium carbonate precipitation was applied to strengthen the surface of limestone. The treatment procedure for bio-deposition was first optimized regarding the aspects of treatment frequency and treatment time. Ultrasonic velocity was used to characterize the surface properties. It turned out that two subsequent applications of a one-step bio-deposition treatment had the best effect, where the transmitting velocity of the ultrasonic wave was increased with around 10~20%. The improvement mainly occurred from the surface till the depth of 4 cm and the largest increase was at the depth around 2 cm. Meanwhile, a commercial chemical ethyl silicate based consolidant, was applied under the same condition. Yet the efficiency measured by the increase in ultrasonic velocity was not significant

Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Flow cytometry is able measure up to 50.000 cells in various dimensions in seconds of time. This large amount of data gives rise to the possibility of making predictions at the single-cell level, however, applied to bacterial populations a systemic investigation lacks. In order to combat this deficiency, we cultivated twenty individual bacterial populations and measured them through flow cytometry. By creating in silico communities we are able to use supervised machine learning techniques in order to examine to what extent single-cell predictions can be made; this can be used to identify the community composition. We show that for more than half of the communities consisting out of two bacterial populations we can identify single cells with an accuracy >90%. Furthermore we prove that in silico communities can be used to identify their in vitro counterpart communities. This result leads to the conclusion that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of bacterial flow cytometric data and for the experimental study of low-complexity communities

Electrokinetics in reactive and conical channels: Not-so-linear transport of charge, fluid, and salt

Transport of charge, fluid, and salt in electrolytes is critical for biology, where it nurtures cells, and also for industry, where it is used to purify our drinking water. Not only is transport in electrolytes important, but it also exhibits a rich variety of transport phenomena due to the intricate connection between ionic and fluidic transport. Striking examples are electro-osmotic flow, where a voltage difference drives flow, and streaming current, where a pressure difference drives charge transfer. In straight, micrometer, channels this transport usually exhibits a linear relation between driving force and transport rate. However, in this thesis we investigate transport in reactive and conical channels for which surprisingly transport is nonlinear. We show that flow alters the surface chemistry of a dissolving channel and that electrostatic surface-ion interactions induce nonlinear reaction kinetics. Finally we consider the influence of pressure and geometry on current rectification by conical pores. These nonlinear transport phenomena not only open up new signal-processing and chemical analysis methods, but also affect mineral transport by groundwater

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R-2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. IMPORTANCE A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production

Clustering environmental flow cytometry data by searching density peaks

Microbial single cells can be characterized by their phenotypic properties using flow cytometry. Therefore flow cytometry can be used to analyze various aspects of environmental microbial communities. In recent years, researchers have focused on fully exploiting the multivariate data that such analyses generate. As they are interested in the diversity of an environmental sample, we need a proper estimation of the number of species and their abundances. We modified a recently published algorithm to estimate the microbial diversity based on flow cytometry data. After giving a brief sketch of the problem setup, we will review this algorithm alongside its various implementations. Moreover we will present our current implementation combined with future challenges we foresee