Random poly(ε‐caprolactone‐L‐alanine) by direct melt copolymerization

A series of random polyesteramides (PEAs) with a range of molar composition from 90/10 to 50/50 were synthesized by direct melt polycondensation of ε‐caprolactone and l ‐alanine. Their structure was fully characterized by Fourier transform IR and NMR spectroscopy. The resulting copolymers are completely amorphous with the exception of PEA‐90/10 which possesses a semicrystalline structure. These PEAs present increasing glass transition temperatures at increasing l ‐alanine contents and exhibit fairly good thermal stability with 10% mass loss temperatures reaching 315 °C. © 2020 Society of Industrial Chemistry     

Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

Density related properties of bark insulation boards bonded with tannin hexamine resin

Building owners are increasingly interested in a healthy and sustainable living environment, which is a trend favoring ecological building materials with outstanding structural physical parameters. Insulation boards from particles of larch bark (Larix decidua Mill.) bonded with a formaldehyde-free tannin resin were pressed and evaluated for their mechanical and physical properties. It could be shown that light (target density 250 kg/m³) boards can be pressed, and their thermal conductivity is low (0.065–0.09 W/(m*K)). With regard to mechanical characteristics, the influence of panel density was studied, and it was found that a certain compaction (ρ ≥ 400 kg/m³) is necessary to meet the requirements of the relevant standard. Interestingly, the resin amount did not influence the mechanical board properties as strongly as expected, and panel density is the most important variable in this respect. The study proved that tree bark cannot only be used for substantially upgraded insulation panels but can also be bonded with a formaldehyde free tannin resin.     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).     

Synthesis,structural and morphological characteristics,magnetic and optical properties of Co doped ZnO nanoparticles

Zn_1−xCo_xO (x==0.05, 0.10, 0.15) nanoparticles have been synthesized by an alternative wet-chemical synthesis route using the SimAdd technique. The as-obtained powders were investigated by FT-IR spectroscopy, X-ray diffraction and thermal analysis correlated with evolved gas analysis (TG–DTA–FT-IR) in order to determine their chemical nature, crystalline structure and to establish the decomposition sequences. The precipitates are generally amorphous, but low-intensity reflection peaks assigned both to the zinc oxalate dihydrate, and zinc hydroxide can be observed in the recorded patterns, indicating that hydroxy-oxalate precipitates were obtained. The structure, morphology and magnetic properties of the thermally treated samples have been investigated by X-ray diffraction, FT-IR, HRTEM, SAED, UV–vis and EPR. XRD studies reveal a hexagonal wurtzite-type structure for all Zn_1−xCo_xO samples. TEM investigations show particle size between 28 and 37 nm, with spherical and polyhedral shapes and with tendency to form aggregates. The presence of a Co₃O₄ secondary phase was evidenced by XRD, UV–vis and EPR for the Zn_0.85Co_0.15O sample. The ferromagnetic behavior of the samples was revealed. The paper highlights that by varying the cobalt concentration it is possible to modulate the structural, morphological, optical and magnetic properties.     

Superhydrophobic properties of cotton fabrics functionalized with ZnO by electroless deposition

Cotton fabrics were coated with arrays of ZnO hexagonal prisms using an electroless (catalytic/autocatalytic) deposition process. A typical three step method, similar to those used for electroless deposition of metals on insulating substrates, consisting of pre-activation, activation and deposition steps was employed. The low-dimensional ZnO particles were grown from an aqueous solution containing zinc nitrate as source of zinc ions and dimethylamineborane as reducing agent. The as-obtained ZnO-coated cotton fabrics were characterized from the point of view of structure by X-ray diffraction (XRD) and morphology by scanning electron microscopy (SEM). The XRD studies demonstrate that the ZnO particles have a hexagonal wurtzite crystalline structure. The SEM observations prove that the cotton fibers are homogeneously covered by hexagonal prisms which have uniform base size of approximately 500 nm and height of 1 μm. Optical spectroscopy measurements show that the functionalization with ZnO strongly decreases the transmittance in the UV–vis region of the cotton fabrics. An important characteristic is that the ZnO-functionalized cotton fabrics exhibit superhydrophobicity, with water contact angles exceeding 150°. The technique described is highly reproducible, easy scalable and cheap, allowing a wide range of applications.     

Secondary data — the Poor Relative of Business Continuity

Cable & Wireless Plc acquired Exodus on the 1st February 2002.You think you’ve got BCP covered because you take regular backups and store them safely off site and perhaps even encrypt them … Think again. There is a whole plethora of devices and programs that facilitate your users accessing that data after you have dutifully restored it from your backup medium of choice. These facilitators contain secondary data and this data needs backing up and security measures as well, otherwise your primary data will resemble a newly restored vintage car: great looks, but no leaded gasoline available to run on.     

Increase of regression speed of combustion through oxidizer doping

This paper presents an innovative method to increase the regression speed of a solid fuel grain by using oxidizer doping. The results obtained on a propulsion application showing the performance parameters as an output of a numerical model are provided. Two models are given that predict the regression speed increase for embedding wires and for oxidizer doping with a factor of ～2 to 3. Also, it is shown that this increase of the regression speed by using a low oxidizer doping percentage, which is based on the assumption that at a higher doping rate the combustion instabilities, can lead to detonations. Next, a thermochemical model for the combustion within the oxidizer doping model is afforded. For this model, the burn is assumed to take place around the surface of each of the oxidizer particles, being composed of two different burning processes: one is the burning on the surface of the solid fuel grain and the other is the burning between the solid fuel and the solid oxidizer. For a non-doped fuel grain, the burning takes place only on the surface of the fuel grain. Finally, a potential application of such propulsion units to small satellite launchers is presented, and a case study of such a vehicle intended to place a 50 kg payload on a low earth orbit (300 to 500 km altitude) is described. The modelling proposed is based on the assumption that the use of solid methane makes this launcher environmentally friendly.