Does extrinsic goal framing enhance extrinsic goal-oriented individuals' learning and performance? An experimental test of the match perspective versus self-determination theory.

Previous work within self-determination theory has shown that experimentally framing a learning activity in terms of extrinsic rather than intrinsic goals results in poorer conceptual learning and performance, presumably because extrinsic goal framing detracts attention from the learning activity and is less directly satisfying of basic psychological needs. According to the match perspective, experimental extrinsic, compared to intrinsic, goal framing should enhance learning and performance for learners who personally hold a stronger extrinsic than intrinsic goal orientation, as these learners' personally held goals match with the situationally induced goals. An experimental field study among 5th-6th grade children shows that extrinsic goal framing resulted in poorer autonomous motivation, conceptual (but not rote) learning, and persistence compared to intrinsic goal framing, irrespective of participants' personal intrinsic versus extrinsic goal orientations and their spontaneous perception of the learning activity as serving an intrinsic or an extrinsic goal. The authors conclude that teachers can best promote intrinsic goals, even when facing students who personally hold a stronger extrinsic than intrinsic goal orientation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Influence of composition of MgAl2O4 supported NiCeO2ZrO2 catalysts on coke formation and catalyst stability for dry reforming of methane

Dry reforming of methane was studied over Ni catalysts supported on γAl₂O₃, CeO₂, ZrO₂ and MgAl₂O₄ (670 °C, 1.5 bar, 16–20 l CH₄ ml_catalyst⁻¹ h⁻¹). It is shown that MgAl₂O₄ supported Ni catalysts promoted with both CeO₂ and ZrO₂ are promising catalysts for dry reforming of methane with carbon dioxide. Within a certain composition range, the simultaneous promotion with CeO₂ and ZrO₂ has great influence on the amount of coke and the catalyst service time. XRD analyses indicate that formation of crystalline Ce_xZr_1−xO₂ mixed oxide phases occurs on double promotion. In particular, incorporation of low amounts of Zr in the CeO₂ fluorite structure provides stable dry reforming catalysis. As shown with TPR, promotion leads to a higher reduced state of Ni. SEM, XRD and TPR analyses demonstrate that highly dispersed, doubly promoted Ni catalysts with a strong metal-support interaction are essential for stable dry reforming and suppression of the formation of carbon filaments.     

Poly(vinylidene fluoride‐co‐hexafluoropropylene) (PVDF‐co‐HFP) hollow fiber membranes prepared from PVDF‐co‐HFP/PEG‐600Mw/DMAC solution for membrane distillation

Poly(vinylidene fluoride‐co‐hexafluoropropylene) (PVDF‐co‐HFP) hollow fiber membranes were prepared by using the phase inversion method. The effect of polyethylene glycol (PEG‐600Mw) with different concentrations (i.e., 0, 5, 7, 10, 12, 15, 18, and 20 wt %) as a pore former on the preparation and characterization of PVDF‐co‐HFP hollow fibers was investigated. The hollow fiber membranes were characterized using scanning electron microscopy, atomic force microscopy, and porosity measurement. It was found that there is no significant effect of the PEG concentration on the dimensions of the hollow fibers, whereas the porosity of the hollow fibers increases with increase of PEG concentration. The cross‐sectional structure changed from a sponge‐like structure of the hollow fiber prepared from pure PVDF‐co‐HFP to a finger‐like structure with small sponge‐like layer in the middle of the cross section with increase of PEG concentration. A remarkable undescribed shape of the nodules with different sizes in the outer surfaces, which are denoted as “twisted rope nodules,” was observed. The mean surface roughness of the hollow fiber membranes decreased with an increase of PEG concentration in the polymer solution. The mean pore size of the hollow fibers gradually increased from 99.12 to 368.91 nm with increase of PEG concentration in polymer solution. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Formation of an interconnected lamellar structure in PVDF membranes with nanoparticles addition via solid‐liquid thermally induced phase separation

Novel microporous membranes were prepared via thermally induced solid‐liquid (S‐L) phase separation of mixtures containing poly(vinylidene fluoride) (PVDF)/diphenyl ketone (DPK)/nanoparticles [such as montmorillonite (MMT) and polytetrafluoroethylene (PTFE)] in diluted systems with a mass ratio of 29.7/70/0.3 wt %. The crystallization and melting characteristics of these diluted systems were investigated by polarizing optical microscopy (POM), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and wide angle X‐ray diffraction (WAXD). The nanoparticle structure and the interaction between PVDF chains and nanoparticle surfaces determined the crystallization behavior and morphology of the PVDF membrane. The addition of MMT and PTFE had a significant nucleation enhancement on the crystallization of PVDF accompanied by S‐L phase separation during the thermally induced phase separation (TIPS) process. It was observed that an interconnected lamellar structure was formed in these two membranes, leading to a higher tensile strength compared with that of the reference membrane without nanoparticles addition. Additionally, addition of MMT facilitates the fiber‐like β phase crystal formation, resulting in the highest elongation at break. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional,time‐dependent Processes

High‐dimensional and time‐dependent data pose significant challenges to Statistical Process Monitoring. Most of the high‐dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data with autocorrelation. Methods, such as DPCA with Decorrelated Residuals, extend DPCA to further reduce the effects of autocorrelation and cross‐correlation on the monitoring statistics. Recursive Principal Component Analysis and Moving Window Principal Component Analysis, developed for nonstationary data, are adaptive. These fundamental methods will be systematically compared on high‐dimensional, time‐dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time‐dependent data are discussed, and areas of possible further research are highlighted. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1478–1493, 2016