Impact of non‐solvent on regeneration of cellulose dissolved in 1‐(carboxymethyl)pyridinium chloride ionic liquid

Cellulose dissolved in ionic liquid (1‐(carboxymethyl)pyridinium chloride)/water (60/40 w/w) mixture is regenerated in various non‐solvents, namely water, ethanol, methanol and acetone, to gain more insight into the contribution of non‐solvent medium to the morphology of regenerated cellulose. To this end, the initial and regenerated celluloses were characterized with respect to crystallinity, thermal stability, chemical structure and surface morphology using wide‐angle X‐ray diffraction, thermogravimetric analysis, Fourier transform infrared spectroscopy and scanning electron microscopy. According to the results, regardless of non‐solvent type, all regenerated samples have the same chemical structure and lower crystallinity in comparison to the initial cellulose, making them a promising candidate for efficient biofuel production based on enzymatic hydrolysis of cellulose. The reduction in crystallinity of regenerated samples is explained based on the potential of the non‐solvent to break the hydrogen bonds between cellulose chains and ionic liquid molecules as well as the affinity of water and non‐solvent which can be evaluated based on Hansen solubility parameter. The latter also determines the phase‐separation mechanism during the regeneration process, which in turn affects surface morphology of the regenerated cellulose. The pivotal effect of regenerated cellulose crystallinity on its thermal stability is also demonstrated. Regenerated cellulose with lower crystallinity is more susceptible to molecular rearrangement during heating and hence exhibits enhanced thermal stability. © 2019 Society of Chemical Industry     

Quasi-static and dynamic crushing behaviors of aluminum and steel tubes with a cutout

Tubular members are commonly used as an energy absorber in engineering structures and many such members have a cutout. In this study, the crushing behaviors of tubes with a cutout are characterized and the effects of cutout on the energy absorption capabilities of these tubes are quantified. Systematic parametric studies were carried out to study the effect of material properties, including yield and ultimate strength of material, strain rate effect, location of cutout, tube length and impact speed on the crushing behaviors and energy absorption capacity of aluminum and steel tubes. First, a numerical model was constructed with a commercial explicit finite element code. It will be first proven that the numerical simulation can produce sufficiently accurate results in an economic manner. Subsequently, the crushing behavior of aluminum and steel tubes with a cutout was experimentally characterized and their energy absorption capacity was evaluated in terms of mean crushing force, peak crushing force and specific energy absorption (SEA). Tubes of various lengths with a cutout located at different locations, subject to both quasi-static and dynamic impact loadings were considered. For steel tubes, the numerical simulation investigated the influence of the strain rate effect and variation in strain hardening ratio of the material. Empirical equations describing the mean and peak crushing forces of aluminum and steel tubes with a cutout were developed using linear and nonlinear regression methods applied to the results obtained from the numerical and experimental studies.     

THEORETICAL STUDY OF NONUNIFORM DROPLETS CONCENTRATION DISTRIBUTION ON VENTURI SCRUBBER PERFORMANCE

A new three-dimensional dispersion model has been developed and was used to study particulate removal operations in a venturi type scrubber. The model takes into account the effect of nonuniform droplets concentration distribution on the particulate removal efficiency of the scrubber of Brink and Contant (1958). Experimental data was used to test the results of the mathematical model. The results from the model are in good agreement with the experimental data. After validating the model, it was used to predict the effect of parameters such as liquid to gas flow rate ratio, gas throat velocity, and angle of the divergent section and nozzle diameter on the extent of nonuniformity of drops. Removal efficiency will increase with increasing uniformity of droplets concentration distribution. Thus, any factor that increases removal efficiency increases uniformity and vice versa. The factors affecting uniformity are: droplet diameter, liquid jet penetration length and size of scrubber.     

Bayesian estimation based on vague lifetime data

In Classical Bayesian approach, estimation of lifetime data usually is dealing with precise information. However, in real world, some informations about an underlying system might be imprecise and represented in the form of vague quantities. In these situations, we need to generalize classical methods to vague environment for studying and analyzing the systems of interest. In this paper, we propose the Bayesian estimation of failure rate and mean time to failure based on vague set theory in the case of complete and censored data sets. To employ the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to induce the vague Bayes estimate of failure rate and mean time to failure by introducing and applying a theorem called “Resolution Identity” for vague sets. In order to evaluate the membership degrees of vague Bayesian estimate for these quantities, a computational procedure is investigated. In the proposed method, the original problem is transformed into a nonlinear programming problem which is then divided into eight subproblems to simplifying computations.     

Integration of Data from Liquid–Liquid Phase Separation Databases Highlights Concentration and Dosage Sensitivity of LLPS Drivers

Liquid–liquid phase separation (LLPS) is a molecular process that leads to the formation of membraneless organelles, representing functionally specialized liquid-like cellular condensates formed by proteins and nucleic acids. Integrating the data on LLPS-associated proteins from dedicated databases revealed only modest agreement between them and yielded a high-confidence dataset of 89 human LLPS drivers. Analysis of the supporting evidence for our dataset uncovered a systematic and potentially concerning difference between protein concentrations used in a good fraction of the in vitro LLPS experiments, a key parameter that governs the phase behavior, and the proteomics-derived cellular abundance levels of the corresponding proteins. Closer scrutiny of the underlying experimental data enabled us to offer a sound rationale for this systematic difference, which draws on our current understanding of the cellular organization of the proteome and the LLPS process. In support of this rationale, we find that genes coding for our human LLPS drivers tend to be dosage-sensitive, suggesting that their cellular availability is tightly regulated to preserve their functional role in direct or indirect relation to condensate formation. Our analysis offers guideposts for increasing agreement between in vitro and in vivo studies, probing the roles of proteins in LLPS.     

Long‐term water uptake behavior of natural fiber/polypropylene composites

Composites of different natural fibers and polypropylene were prepared and their long‐term water absorption behaviors were studied. Wood flour, rice hulls, newsprint fibers, and kenaf fibers (at 25 and 50% by weight contents) were mixed with polypropylene and 1 and 2% compatibilizer, respectively. Water absorption tests were carried out on injection‐molded specimens at room temperature for 5 weeks. Measurements were made every week and water absorption was calculated. Water diffusion coefficients were also calculated by evaluating the water absorption isotherms. Results indicated a significant difference among different natural fibers, with kenaf fibers and newsprint fibers exhibiting the highest and wood flour and rice hulls the lowest water absorption values, respectively. The difference between 25 and 50% fiber contents for all composite formulations increased at longer immersion times. Water diffusion coefficients of the composites were found to be about 3 orders of magnitude higher than that of pure PP. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

Predicting roadheader performance by using artificial neural network

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R ² = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

Study on the performance of Cd2+ sorption using dimethylethylenediamine-modified zinc-based MOF (ZIF-8-mmen): optimization of the process by RSM technique

ABSTRACT

Cadmium as a highly toxic metal is released into the environment through paper production, metal processing, phosphate fertilizers, insecticides, and treatment of wastewater. Cadmium also inhibits the body activities and is very toxic for kidney and other organisms. In the current study, zinc-based metal–organic framework, zeolitic imidazolate framework (ZIF)-8, was synthesized and modified by dimethylethylenediamine (ZIF-8-mmen) for the removal of cadmium. To optimize the experiments, response surface methodology was applied with three variables including pH, adsorbent dosage, and contact time using central composite design. The optimum conditions for pH, dosage, and time were 2, 0.1 g, and 89 min, respectively, with removal efficiency of 85.38%. The Langmuir isotherm (q _m = 1000 mg/g) indicates the monolayer adsorption. The kinetic studies reveal that the Lagergren model was predominant and cadmium was not chemisorbed. Thermodynamic parameters show spontaneous, endothermic, and physisorption processes.