Engineering microfluidic papers: effect of fiber source and paper sheet properties on capillary-driven fluid flow

In the present study, we introduce a novel approach to control and modulate fluid transport inside microfluidic papers using lab-engineered paper sheets. Lab-sheets consisting of different fiber sources (eucalyptus sulfate and cotton linters pulp) and varying porosities were designed and further modified with small millimeter-scaled channels using hydrophobic barriers consisting of fiber-attached, hydrophobic polymers. The capillary-driven transport of an aqueous solution was monitored visually, and the influence of parameters such as fiber source, paper grammage, and channel width on the flow rates through the channel was investigated. The experimental results were compared with those obtained with commercially available filter papers. Our findings suggest that accurate control of fluid transport processes with standard filter papers is complex. Additionally, if the channel width is smaller than the mean fiber length, flow rates become dependent on the geometric parameters of the channel because of the formation of dead-end pores at the hydrophobic barriers. Finally, control of the paper sheets porosity, by varying the fiber density of the lab-made paper, affords the fabrication of chemically identical sheets whereby capillary flow is largely influenced and can be modulated accordingly by simple papermaking processes.     

Application of the percolation model to gelation of an epoxy resin

The percolation model has been applied to the study of gelation of the TGDDM-DDS system (tetraglycidyldiaminodiphenylmethane–diaminodiphenylsulfone) at a mass concentration of 100–30. For each temperature the experimental viscosity curves are satisfactorily described by a percolation law. Using the degree of chemical reactions, X, as a variable, a very clear change in the reaction mechanism with temperature can be shown. Then a rate of advancement of effective reactions, Y, is defined. This value only takes intermolecular-type reactions into account, and is probably the only variable on which viscosity depends in a percolation law: η = B(1 ? Y/Y_c)^?p. We obtain Y_c= 0.45 and p= 2.0. Comparing X_c and Y_c at the gel point, we obtain information on the proportion of intramolecular reactions with temperature. It is also demonstrated that the critical percolation threshold agrees closely with the gel point determined experimentally on log G″= f(t) curves.     

Well-defined polymer precursors synthesized by RAFT polymerization of N,N-dimethylacrylamide/N-acryloxysuccinimide: random and block copolymers

N,N-dimethylacrylamide (DMA) and N-acryloxysuccinimide (NAS) were copolymerized by the reversible addition–fragmentation chain transfer (RAFT) polymerization technique, to obtain random and block copolymer precursors onto which different side-groups may be statistically grafted via the reactive NAS units. These reactive copolymers have interesting applications in various fields such as coatings and paints, water purification and biology. Random poly(DMA-co-NAS) copolymer chains were synthesized with a 75/25 molar ratio, high conversion, an excellent molecular weight (MW) control from 5000 to 130 000 g mol⁻¹, and low polydispersity index (M_w/M_n<1.1). Poly(DMA-b-NAS) block copolymers were synthesized by a two step method, in which a poly(DMA) homopolymer was prepared first and then used as macro-chain transfer agent to polymerize NAS. For example, a poly(DMA-b-NAS) sample was obtained with an average molecular weight of 44 300/7400 g mol⁻¹ corresponding to 447 DMA and 44 NAS units. Such block copolymers had not yet been synthesized by any controlled polymerization technique. They can be used to prepare polymers with exactly the same backbone and an increasing number of different side groups (e.g. hydrophobic, ionic or fluorescent).     

Effect of Step Size in Incremental Loading Tests

Using the crack-growth model, the deviation of real fracture stress from the stressing level measured by stepwise incremental loading with three starting modes is calculated. For both static and dynamic fatigue, the relative error depends only on the relative increment size and the crack-growth parameter, n. The usefulness of stepwise incremental loading for strength testing can be confirmed.     

Automated bank cheque verification using image processing and deep learning methods

Multimedia Tools and Applications - Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate...     

Giving too much social support: social overload on social networking sites

As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn.     

Product fit uncertainty and its effects on vendor choice: an experimental study

Recommender systems and other Internet-enabled technologies have changed the surrounding conditions of pre-purchase evaluations on the Internet. In some cases consumers can now sample entire products prior to a purchase – hereby removing all uncertainty about whether a product fits their taste. While previous research has mainly focused on vendor and product quality uncertainty, it is still not clear how declining product fit uncertainty affects consumers. To close this gap, we conducted a laboratory experiment to analyze the effects on consumers’ vendor selection. We find that full elimination of product fit uncertainty is beneficial for vendors, as it increases both the number of purchases and consumer loyalty. Interestingly, if product fit uncertainty is only partially eliminated, consumers do not necessarily show differential behavior for different levels of remaining product fit uncertainty. This has important implications for online vendors that consider the implementation of additional means to reduce product fit uncertainty.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.