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The conjunction of (bio-)chemical recognition elements with nanoscale biological building blocks such as virus particles is considered as a very promising strategy for the creation of biohybrids opening novel opportunities for label-free biosensing. This work presents a new approach for the development of biosensors using tobacco mosaic virus (TMV) nanotubes or coat proteins (CPs) as enzyme nanocarriers. Sensor chips combining an array of Pt electrodes loaded with glucose oxidase (GOD)-modified TMV nanotubes or CP aggregates were used for amperometric detection of glucose as a model system for the first time. The presence of TMV nanotubes or CPs on the sensor surface allows binding of a high amount of precisely positioned enzymes without substantial loss of their activity, and may also ensure accessibility of their active centers for analyte molecules. Specific and efficient immobilization of streptavidin-conjugated GOD ([SA]-GOD) complexes on biotinylated TMV nanotubes or CPs was achieved via bioaffinity binding. These layouts were tested in parallel with glucose sensors with adsorptively immobilized [SA]-GOD, as well as [SA]-GOD crosslinked with glutardialdehyde, and came out to exhibit superior sensor performance. The achieved results underline a great potential of an integration of virus/biomolecule hybrids with electronic transducers for future applications in biosensorics and biochips.
Silos generally work as storage structures between supply and demand for various goods, and their structural safety has long been of interest to the civil engineering profession. This is especially true for dynamically loaded silos, e.g., in case of seismic excitation. Particularly thin-walled cylindrical silos are highly vulnerable to seismic induced pressures, which can cause critical buckling phenomena of the silo shell. The analysis of silos can be carried out in two different ways. In the first, the seismic loading is modeled through statically equivalent loads acting on the shell. Alternatively, a time history analysis might be carried out, in which nonlinear phenomena due to the filling as well as the interaction between the shell and the granular material are taken into account. The paper presents a comparison of these approaches. The model used for the nonlinear time history analysis considers the granular material by means of the intergranular strain approach for hypoplasticity theory. The interaction effects between the granular material and the shell is represented by contact elements. Additionally, soil–structure interaction effects are taken into account.
The behaviour of infilled reinforced concrete frames under horizontal load has been widely investigated, both experimentally and numerically. Since experimental tests represent large investments, numerical simulations offer an efficient approach for a more comprehensive analysis. When RC frames with masonry infill walls are subjected to horizontal loading, their behaviour is highly non-linear after a certain limit, which makes their analysis quite difficult. The non-linear behaviour results from the complex inelastic material properties of the concrete, infill wall and conditions at the wall-frame interface. In order to investigate this non-linear behaviour in detail, a finite element model using a micro modelling approach is developed, which is able to predict the complex non-linear behaviour resulting from the different materials and their interaction. Concrete and bricks are represented by a non-linear material model, while each reinforcement bar is represented as an individual part installed in the concrete part and behaving elasto-plastically. Each brick is modelled individually and connected taking into account the non-linearity of a brick mortar interface. The same approach is followed using two finite element software packages and the results are compared with the experimental results. The numerical models show a good agreement with the experiments in predicting the overall behaviour, but also very good matching for strength capacity and drift. The results emphasize the quality and the valuable contribution of the numerical models for use in parametric studies, which are needed for the derivation of design recommendations for infilled frame structures.
Past earthquakes demonstrated the high vulnerability of industrial facilities equipped with complex process technologies leading to serious damage of process equipment and multiple and simultaneous release of hazardous substances. Nonetheless, current standards for seismic design of industrial facilities are considered inadequate to guarantee proper safety conditions against exceptional events entailing loss of containment and related consequences. On these premises, the SPIF project -Seismic Performance of Multi-Component Systems in Special Risk Industrial Facilities- was proposed within the framework of the European H2020 SERA funding scheme. In detail, the objective of the SPIF project is the investigation of the seismic behaviour of a representative industrial multi-storey frame structure equipped with complex process components by means of shaking table tests. Along this main vein and in a performance-based design perspective, the issues investigated in depth are the interaction between a primary moment resisting frame (MRF) steel structure and secondary process components that influence the performance of the whole system; and a proper check of floor spectra predictions. The evaluation of experimental data clearly shows a favourable performance of the MRF structure, some weaknesses of local details due to the interaction between floor crossbeams and process components and, finally, the overconservatism of current design standards w.r.t. floor spectra predictions.
The investigation of the possibility to determine various characteristics of powder heparin (n = 115) was carried out with infrared spectroscopy. The evaluation of heparin samples included several parameters such as purity grade, distributing company, animal source as well as heparin species (i.e. Na-heparin, Ca-heparin, and heparinoids). Multivariate analysis using principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and partial least squares – discriminant analysis (PLS-DA) were applied for the modelling of spectral data. Different pre-processing methods were applied to IR spectral data; multiplicative scatter correction (MSC) was chosen as the most relevant.
Obtained results were confirmed by nuclear magnetic resonance (NMR) spectroscopy. Good predictive ability of this approach demonstrates the potential of IR spectroscopy and chemometrics for screening of heparin quality. This approach, however, is designed as a screening tool and is not considered as a replacement for either of the methods required by USP and FDA.
The molecular weight properties of lignins are one of the key elements that need to be analyzed for a successful industrial application of these promising biopolymers. In this study, the use of 1H NMR as well as diffusion-ordered spectroscopy (DOSY NMR), combined with multivariate regression methods, was investigated for the determination of the molecular weight (Mw and Mn) and the polydispersity of organosolv lignins (n = 53, Miscanthus x giganteus, Paulownia tomentosa, and Silphium perfoliatum). The suitability of the models was demonstrated by cross validation (CV) as well as by an independent validation set of samples from different biomass origins (beech wood and wheat straw). CV errors of ca. 7–9 and 14–16% were achieved for all parameters with the models from the 1H NMR spectra and the DOSY NMR data, respectively. The prediction errors for the validation samples were in a similar range for the partial least squares model from the 1H NMR data and for a multiple linear regression using the DOSY NMR data. The results indicate the usefulness of NMR measurements combined with multivariate regression methods as a potential alternative to more time-consuming methods such as gel permeation chromatography.
Lignin is a promising renewable biopolymer being investigated worldwide as an environmentally benign substitute of fossil-based aromatic compounds, e.g. for the use as an excipient with antioxidant and antimicrobial properties in drug delivery or even as active compound. For its successful implementation into process streams, a quick, easy, and reliable method is needed for its molecular weight determination. Here we present a method using 1H spectra of benchtop as well as conventional NMR systems in combination with multivariate data analysis, to determine lignin’s molecular weight (Mw and Mn) and polydispersity index (PDI). A set of 36 organosolv lignin samples (from Miscanthus x giganteus, Paulownia tomentosa and Silphium perfoliatum) was used for the calibration and cross validation, and 17 samples were used as external validation set. Validation errors between 5.6% and 12.9% were achieved for all parameters on all NMR devices (43, 60, 500 and 600 MHz). Surprisingly, no significant difference in the performance of the benchtop and high-field devices was found. This facilitates the application of this method for determining lignin’s molecular weight in an industrial environment because of the low maintenance expenditure, small footprint, ruggedness, and low cost of permanent magnet benchtop NMR systems.
As with most high-velocity free-surface flows, stepped spillway flows become self-aerated when the drop height exceeds a critical value. Due to the step-induced macro-roughness, the flow field becomes more turbulent than on a similar smooth-invert chute. For this reason, cascades are oftentimes used as re-aeration structures in wastewater treatment. However, for stepped spillways as flood release structures downstream of deoxygenated reservoirs, gas transfer is also of crucial significance to meet ecological requirements. Prediction of mass transfer velocities becomes challenging, as the flow regime differs from typical previously studied flow conditions. In this paper, detailed air-water flow measurements are conducted on stepped spillway models with different geometry, with the aim to estimate the specific air-water interface. Re-aeration performances are determined by applying the absorption method. In contrast to earlier studies, the aerated water body is considered a continuous mixture up to a level where 75% air concentration is reached. Above this level, a homogenous surface wave field is considered, which is found to significantly affect the total air-water interface available for mass transfer. Geometrical characteristics of these surface waves are obtained from high-speed camera investigations. The results show that both the mean air concentration and the mean flow velocity have influence on the mass transfer. Finally, an empirical relationship for the mass transfer on stepped spillway models is proposed.
Optical flow estimation is known from Computer Vision where it is used to determine obstacle movements through a sequence of images following an assumption of brightness conservation. This paper presents the first study on application of the optical flow method to aerated stepped spillway flows. For this purpose, the flow is captured with a high-speed camera and illuminated with a synchronized LED light source. The flow velocities, obtained using a basic Horn–Schunck method for estimation of the optical flow coupled with an image pyramid multi-resolution approach for image filtering, compare well with data from intrusive conductivity probe measurements. Application of the Horn–Schunck method yields densely populated flow field data sets with velocity information for every pixel. It is found that the image pyramid approach has the most significant effect on the accuracy compared to other image processing techniques. However, the final results show some dependency on the pixel intensity distribution, with better accuracy found for grey values between 100 and 150.