A review on microwell and microfluidic geometric array fabrication techniques and its potential applications in cellular studies

The ability to trap precise quantities of cells or particles into confined areas has numerous applications for biological purposes. In particular, single cell trapping has received considerable attention because it permits the study of heterogeneity in a population, while trapping larger groups of cells have been used to form aggregates. Among several methods, the use of microwell offers a simple platform to capture cells or particles using hydrodynamic forces. This review examines the use of microwells in both static and microfluidic environments, and the application of microfluidic geometric arrays for trapping. This paper discusses the design and fabrication methods of microwells and compares the trapping and release techniques used in both static and microfluidics‐integrated microwells. Finally, we will summarize novel microfluidic geometric arrays used to capture cells or particles through hydrodynamic trapping.     

Microstructural properties,thermal insulation and thermal degradation behavior of boron-containing monolithic novolac xerogels

Enhancement of thermal stability-insulation performance of hyper porous materials is the premier issue to design of novel porous thermal protection systems. Boron-containing monolithic novolac xerogels (BCNXs) were synthesized using sol–gel networking of novolac resin with hexamethylenetetramine (HMTA) and boric acid at the solvent saturated vapor atmosphere (SSVA). The aim was to elucidate the effect of higher crosslinking density and thermal stable boron containing chemical bonds on the microstructure, thermal conductivity, and thermal oxidation stability of novolac xerogels. The results of FESEM and BET analysis showed that the microstructural characteristics of xerogels are significantly depend on the HMTA and boric acid concentration. The thermogravimetric results were analyzed using characteristic kinetic temperature (CKT)-characteristic kinetic temperature range (CKTR) approximations. The effect of micromorphology of xerogels on the thermal conductivity was investigated. The effective thermal conductivity of samples were in the range of 0.031–0.048 W/m K.     

Mechanical and viscoelastic properties of polyethylene-based microfibrillated composites from 100% recycled resources

In this study, recycled polyethylene (rPE) based microfibrillated composites (MFCs) were developed while incorporating recycled poly(ethylene terephthalate) (rPET) and recycled polyamide 6 (rPA) as the reinforcing fibrillar phases at a given weight ratio of 80 wt% (rPE)/20 wt% (rPET or rPA). The blends were first melt processed using a twin-screw extruder. The extrudates were then cold stretched at a drawing ratio of 2.5 to form rPET and rPA fibrillar structures. Next, the pelletized drawn samples were injection molded at the barrel temperatures below the melting temperatures of rPET and rPA. The tensile, three-point bending, impact strength, dynamic thermomechanical, and rheological properties of the fabricated MFCs were analyzed. The effects of injection molding barrel temperature (i.e., 150°C and 190°C) and extrusion melt processing temperature (i.e., 250°C and 275°C) on the generated fibrillar structure and the resultant properties were explored. A strong correlation between the fibrillar morphology and the mechanical properties with the extrusion and injection molding temperatures was observed. Moreover, the ethylene/n-butyl acrylate/glycidyl methacrylate (EnBAGMA) terpolymer and maleic anhydride grafted PE (MAH-g-PE) were, respectively, melt processed with rPE/rPET and rPE/rPA6 blends as compatibilizers. The compatibilizers refined the fibrillar structure and remarkably influenced mechanical properties, specifically the impact strength.     

Enhancement of electromechanical properties of natural rubber by adding barium titanate filler: An electro-mechanical study

Natural rubber is one of the most potential electro-active polymers for sensors, actuators, and energy harvesting applications. Enhancing the characteristic properties of polymers by reinforcing with fillers that possess multifunctional attributes have attracted considerable attention. In the present study, barium titanate reinforced natural rubber composite is prepared by using two-roll mill mixing. Afterwards, mechanical, electrical, and electromechanical properties of the composites are extensively analyzed by reinforcing different amounts of barium titanate into the matrix of natural rubber. The fabricated dielectric composite shows excellent properties such as high dielectric constant, low dielectric losses, high dielectric breakdown strength, and extreme stretchability. It is observed that as the filler loading reaches the value of 11 parts per hundred rubber (phr), maximum agglomeration of the particles occurs. Maximum stretchability and highest ratio of dielectric constant to elastic modulus are obtained at 8 phr of barium titanate fillers and at the loading, a maximum actuation strain of 11.24% is achieved. This study provides a simple, economical, and effective method for preparing enhanced mechanical, electrical, and electromechanical properties of natural rubber composites, facilitating the wide applications of dielectric materials as actuators and generators.     

Binary polymer blend of ArPTU/PI with advanced comprehensive dielectric properties and ultra-high thermally stability

Flexible high-temperature polymeric dielectrics with advanced dielectric properties are urgently demanded in various applications. In this work, series of polymer blend films were prepared from aromatic polythiourea (ArPTU) and polyimide (PI). The experimental results revealed that the blend films were properly engineered to achieve higher breakdown strength, greater dielectric constant, and larger energy density than pure PI film. For instance, the optimum property was obtained from the blend film with 10 wt% ArPTU, exhibiting prominent dielectric properties (K = 4.52, E_b = 443 MV/m), enhanced energy density (4.00 J/cm³) as well as excellent heat resistance (T_g = 419°C). In addition, stable dielectric properties at broad temperature range from −50 to 250°C were also acquired. It is deduced that the good compatibility from ArPTU and PI with similar polarity are responsible for the improved properties. The superior comprehensive properties which combine the advantages of ArPTU and PI suggest the potential applications of ArPTU/PI blend film in high-temperature dielectric areas.     

Sustainability-guided life-cycle design and assessment for bio-based composite foams: Integrate flame retardancy/lightweight in usage and energy utilization after service

Sustainable development strategy has aroused a great interest in biomass resources as alternative raw materials. A kind of biomass-derived poly(butylene succinate) (PBS), has been developed as porous foams to reduce resource exhaustion and meet lightweight demands. For fire-safety in-service, graphene oxide (GO) was functionalized by 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) to combine flame-retardant elements and heat-barrier function. Hence, a very low loading level of P-containing GO as only 5 wt% could reduce peak heat release rate (pHRR) and total heat release (THR) of PBS-based foams by 58.5% and 22.3%, respectively. Meanwhile, N-/P-doped mesoporous char with a specific surface area of 136 m²/g, which derived from combustion of flame-retardant foaming PBS, contributes to a potential of energy storage applications in the capacitor or the anode of Li-ion battery with long-term stability. Overall, the sustainability of bio-based polyester could integrate lightweight of foaming, and be extended to utilization after use via facile combustion inspired by flame-retardancy design.     

Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics

This paper develops a comprehensive interpolation scheme for non-uniform rational B-spline (NURBS) curves, which does not only simultaneously meet the requirements of both constant feedrate and chord accuracy, but also real-time integrates machining dynamics in the interpolation stage. Although the existing work in this regard has realized the importance to simultaneously consider chord error and machining dynamics, none has really incorporated these in one complete interpolation scheme. In this paper, machining dynamics is considered for three aspects: sharp corners or feedrate sensitive corners on the curves, components with high frequencies or frequencies matching machine natural ones and high jerks. A look-ahead module was developed for detecting and adaptively adjusting the feedrate at the sharp corners. By Fast Fourier Transform (FFT) analysis with a moving window in the interpolation stage identified were some special frequency components such as those containing high frequencies or with frequencies matching machine natural ones. Then, the notch filtering or time spacing method was used to eliminate these components. To more completely reduce feedrate and acceleration fluctuations, the jerk-limited algorithm was also developed. Finally, the interpolated feedrate was further smoothened with B-spline fitting method and the NURBS curves were re-interpolated with the smoothened feedrate. During the interpolation, the chord error was repeatedly checked and confined in the prescribed tolerance. Two NURBS curves were used as examples to test the feasibility of the developed interpolation scheme.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.