Effect of processing techniques and residual solvent on the thermal/mechanical properties of epoxy-cellulose nanocrystal nanocomposites

Epoxy nanocomposites reinforced with 0–5 wt% cellulose nanocrystal (CNC) were fabricated by solution casting method. The CNCs were first dispersed in the hardener together with a co-solvent and subsequently mixed with epoxy monomer using two different protocols: bulk mixing and dropwise mixing. The dropwise mixing resulted in more homogeneously-dispersed CNCs and provided better mechanical properties, particularly higher tensile strength, tensile modulus, and work of fracture with increasing CNC content. Investigation of various CNC/hardener mixtures with different solvent content showed that the residual solvent in the composite mixture had an impact on the curing behavior of the nanocomposite epoxy and may have lowered its crosslinking density, leading to T_g depression. However, the presence of the solvent was shown to be essential for the preparation of a well-dispersed CNC phase in the hardener solution, and to form homogenous composite mixtures. Our results show that the reinforcement effect due to higher CNC concentration is more significant than the solvent effect in the hardener system and shows greater improvement in mechanical properties. The CNC component reversed the solvent plasticizing effect through its superior mechanical reinforcing effects, and the dropwise mixing process led to better dispersion compared with the bulk mixing process.     

Phase Relations in the Systems SrO-Y2O3-CuO-O2 and CaO-Y2O3-CuO-O2 at 1173 K

Phase relations in the systems SrO-Y₂O₃-CuO-O₂ and CaO-Y₂O₃-CuO-O₂ at 1173 K were estab-lished by equilibrating different compositions in flowing oxygen gas at a pressure of 1.01 × 10⁵ Pa. The quenched samples were examined by optical microscopy, X-ray diffraction (XRD), energy dis-persive analysis of X-rays (EDAX), and electron spin resonance (ESR). In the system SrO-Y₂O₃-CuO-O₂, except for the limited substitution of Y₃+ for Sr₂+ ions in the ternary oxide Sr₁₄ Cu₂₄O₄₁, no new quaternary phase was found to be stable. The compositions corresponding to the solid solution Sr_14-xY_xCu₂₄O₄₁ and the compound SrCuO₂+δ lie above the plane containing SrO, Y₂O₃, and CuO, displaced towards the oxygen apex. However, in the system CaO-Y₂O₃-CuO-O₂ at 1173 K, all the condensed phases lie on the plane containing CaO, Y₂O₃, and CuO, and a new quaternary oxide YCa₂Cu₃O_6.5 is present. The quaternary phase has a composition that lies at the center of the non-stoichiometric field of the analogous phase YBa₂Cu₃O_7-δ in the BaO-Y₂O₃-CuO-O₂ system. The com-pound YCa₂Cu₃O_6.5 has the tetragonal structure and does not become superconducting at low temperature. Surprisingly, phase relations in the three systems CaO-Y₂O₃-CuO-O₂, SrO-Y₂O₃-CuO-O₂, and BaO-Y₂O₃-CuO-O₂ are found to be quite different.     

(Sn-Ag)<Subscript>eut</Subscript>+Cu soldering materials,part II: Electrical and mechanical studies

Electrical (solder resistivity and solder joint resistance) and mechanical (tensile strength and shear strength of solder joints) parameters of the binary eutectic Sn-Ag and two alloys close to the ternary eutectic Sn-Ag-Cu composition were investigated. The four-probe technique was used for the measurement of electrical parameters. Special equipment was constructed for the tensile strength measurements and also for determination of the shear strengths of solder joints between a typical circuit component and a Cu contact on a printed circuit board (PCB). It was found that electrical and mechanical properties of the three alloys studied are comparable to data in the literature for traditional Pb-Sn solders. A joint resistance below 0.3 mΩ (Ω=ohm) and shear strength of above 20 MPa were found for an individual solder joint between a circuit component (in the current study a “jumper” resistor) and a copper surface on a PCB.     

An experimental characterization of a tunable separation device

By the use of an experimental setup for microfluidic flows, we have characterized the separation and concentration characteristics of the so-called Trilobite^? separation unit. Our separation unit consists of microfluidic channels and an elliptical separation geometry with a solid and a permeable wall region. We show that it is possible to adjust the thickness of different flow layers by changing the flow rates and pressure drop over the permeable wall. For high pressure drops, the separator shows promising concentration characteristics. For low pressure drops, an increase in flow rate results in a thinning of the flow layers. For sufficiently high flow rates, it should therefore be possible to create flow layers sufficiently thin that the particle separation is entirely dominated by hydrodynamic forces. This, in turn, will enable clog-free particle separation.     

Creating geometrically robust designs for highly sensitive problems using topology optimization

Resonance and wave-propagation problems are known to be highly sensitive towards parameter variations. This paper discusses topology optimization formulations for creating designs that perform robustly under spatial variations for acoustic cavity problems. For several structural problems, robust topology optimization methods have already proven their worth. However, it is shown that direct application of such methods is not suitable for the acoustic problem under consideration. A new double filter approach is suggested which makes robust optimization for spatial variations possible. Its effect and limitations are discussed. In addition, a known explicit penalization approach is considered for comparison. For near-uniform spatial variations it is shown that highly robust designs can be obtained using the double filter approach. It is finally demonstrated that taking non-uniform variations into account further improves the robustness of the designs.     

Efficiently adapting graphical models for selectivity estimation

Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.     

Hardware Technologies for High-Performance Data-Intensive Computing

Data-intensive problems challenge conventional computing architectures with demanding CPU, memory, and I/O requirements. Experiments with three benchmarks suggest that emerging hardware technologies can significantly boost performance of a wide range of applications by increasing compute cycles and bandwidth and reducing latency.     

Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin

Spatially distributed estimates of evaporative fraction and actual evapotranspiration are pursued using a simple remote sensing technique based on a remotely sensed vegetation index (NDVI) and diurnal changes in land surface temperature. The technique, known as the triangle method, is improved by utilizing the high temporal resolution of the geostationary MSG-SEVIRI sensor. With 15 min acquisition intervals, the MSG-SEVIRI data allow for a precise estimation of the morning rise in land surface temperature which is a strong proxy for total daytime sensible heat fluxes. Combining the diurnal change in surface temperature, dT_s with an interpretation of the triangular shaped dT_s − NDVI space allows for a direct estimation of evaporative fraction. The mean daytime energy available for evapotranspiration (R_n − G) is estimated using several remote sensors and limited ancillary data. Finally regional estimates of actual evapotranspiration are made by combining evaporative fraction and available energy estimates. The estimated evaporative fraction (EF) and actual evapotranspiration (ET) for the Senegal River basin have been validated against field observations for the rainy season 2005. The validation results showed low biases and RMSE and R² of 0.13 [−] and 0.63 for EF and RMSE of 41.45 W m^− 2 and R² of 0.66 for ET.     

Development of a genetic algorithm for component placement sequence optimization in printed circuit board assembly

This paper describes the development and evaluation of a custom application exploring the use of genetic algorithms (GA) to solve a component placement sequencing problem for printed circuit board (PCB) assembly. In the assembly of PCB’s, the component placement process is often the bottleneck, and the equipment to complete component placement is often the largest capital investment. The number of components placed on a PCB can range from few to hundreds. As a result, developing an application to determine an optimal or near-optimal placement sequence can translate into reduced cycle times for the overall assembly process and reduced assembly costs. A custom application was developed to evaluate the effectiveness of using GA’s to solve the component placement sequencing problem. A designed experiment was used to determine the best representation and crossover type, crossover rate, and mutation rate to use in solving a component sequencing problem for a PCB consisting of 10 components being placed on a single-headed placement machine. Three different representations (path, ordinal, and adjacency) and six appropriate crossover types (partially mapped, ordered, cycle, classical, alternating edges, and heuristic) were evaluated at three different mutation rates and at 11 crossover rates. Two algorithm response variables, the total distance traveled by the placement head and the algorithm solution efficiency (measured as number of generations and algorithm solution time) were used to evaluate the different GA applications. The combination of representation and crossover type along with mutation rate were found to be the most significant parameters in the algorithm design. In particular, path representation with order crossover was found to produce the best solution as measured by the total distance traveled as well as the solution generation efficiency. Increasing the mutation rate led to slightly improved solutions in terms of head travel, but also resulted in increased solution time.