Thermal energy storage properties of polyethylene glycol grafted styrenic copolymer as novel solid-solid phase change materials

Polymeric solid-solid phase change materials (S-SPCMs) are functional materials with phase transition-heat storing/releasing ability. With this respect, a series of polyethylene glycol (PEG) grafted styrenic copolymer were produced as novel S-SPCMs. PEGs with three different molecular weights were used for synthesis of isocyanate-terminated polymers (ITPs). To achieve cross-linking S-SPCMs, the ITPs were grafted with styrene-co-ally alcohol) (PSAA) at three different PSAA:PEG mole ratios. The produced polymers were characterized using Fourier transform infrared (FT-IR), proton nuclear magnetic resonance (¹H NMR), and X-ray diffraction (XRD) technique. The crystalline-amorphous phase transitions of the polymers were examined using polarized optical microscopy (POM). The FT-IR, NMR, and XRD results confirmed the expected chemical structures and crystallization performances of the polymers. Thermal energy storage (TES) properties of the S-SPCMs were determined by differential scanning calorimetry (DSC). The DSC results revealed that the polymers with grafting ratio of PSAA:PEG(1:1) had phase transition enthalpies between about 74 and 142 J/g and phase transition temperatures between about 26°C and 57°C. Thermogravimetric analysis (TGA) measurements demonstrated that the S-SPCMs were resistant to thermal decomposition until about 300°C. Thermal conductivities of the produced S-SPCMs were measured in a range of about 0.18 to 0.19 W/mK. Furthermore, TES properties of the S-SPCMs were slightly changed as their chemical structures were remained after 5000 thermal cycles. By overall evaluation of the findings, it can be foreseen that particularly PSAA-g-PEG(1:1) polymers can be considered as promising S-SPCMs for some TES practices such as air conditioning of buildings, thermoregulation of food packages, automobile components, electronic devices, and solar photovoltaic panels.     

Preparation and characterization of nano‐enhanced myristic acid using metal oxide nanoparticles for thermal energy storage

Fatty acids have been broadly used as phase change materials (PCMs) for thermal energy storage. However, low thermal conductivity limits their performances. This paper investigates the influence of metal oxide nanoparticle addition on myristic acid (MA) as nano‐enhanced PCM (NEPCM). Stability, chemical, and thermal properties were considered. Four types of nanoaprticles, TiO₂, CuO, Al₂O₃, and ZnO, were dispersed in MA at 0.1, 0.5, 1, and 2 wt%. Stability and dispersion were checked by sediment photograph capturing and scanning electron microscopy/energy‐dispersive spectroscopy. The Fourier‐transformed infrared (FTIR) and X‐ray diffraction analysis confirmed no chemical interaction between the nanoparticles and MA. The results revealed a ratio of thermal conductivity of 1.50, 1.49, 1.45, and 1.37, respectively, for 2 wt% of ZnO, Al₂O₃, CuO, and TiO₂. The T‐history method confirmed this enhancement. The latent heat thermal energy storage (LHTES) properties of the nano‐enhanced MA were evaluated using differential scanning calorimetry. The latent heat capacities of nano‐enhanced MA samples have dropped between 9.64 and 5.01 % compared with pure MA, and phase change temperature range was not affected significantly. The NEPCM was subjected to 500 thermal cycling, it showed a good thermal reliability as LHTES properties remained unchanged, while FTIR analysis showed similar characteristics compared with uncycled samples, indicating a good chemical stability. Based on the results regarding with the LHTES properties, cycling thermal reliability, and higher thermal conductivity improvement, it can be achieved that the MA/Al₂O₃ (2.0 wt%) and MA/ZnO (2.0 wt%) composites could be better PCMs for solar TES applications.     

On establishing the genetic basis of mental disease

Many of the recent studies reporting genetic linkages for mental illnesses such as schizophrenia and manic depression have been retracted. The authors of this article argue that the fundamental reason for the difficulties in this research field lies in the strongly held preconceived belief that the primary cause of these illnesses is in fact genetic. All scientists hold preconceived ideas. However, such ideas are more likely to result in erroneous conclusions in the study of human behavior than in other more 'objective' research areas. Moreover, it is especially important that researchers studying human behavior be aware of their biases and learn to compensate for them because of the social consequences of their work.     

Performance of an RTL contactor for gas-liquid systems: Effective interfacial area and volumetric mass transfer coefficient by oxidation of sodium sulphite solution

Effective interfacial area a and volumetric liquid-side mass transfer coefficient k_La of an RTL contactor were obtained at different stirring speeds by absorption of oxygen from air into 0.8 kmol/m³ sodium sulphite solution, in the presence of Co⁺⁺ ions. The values of a and k_La ranged from 80 to 150 m²/m³ and 0.0003 to 0.00053 s^?1, respectively, when stirrer speed was increased from 8 to 40 rpm. When k_L alone was evaluated, it was found to be practically constant, irrespective of stirring speed.     

Modeling of thermodynamics of substituted toluene derivatives and benzylic radicals via group additivity

The thermodynamic properties of unsubstituted, mono‐, and di‐substituted toluene derivatives and benzylic radicals with hydroxy, methoxy, formyl, vinyl, methyl, and ethyl substituents are calculated with the bond additivity corrected (BAC) post‐Hartree‐Fock CBS‐QB3 method. Benson's group additivity (GA) scheme is extended to toluene derivatives by determining six group additive value (GAV) and five non‐nearest neighbor interaction (NNI) parameters through least‐squares regression to a database of thermodynamic properties of 168 compounds and to benzylic radicals by defining 6 GAV and 14 NNI parameters based on a set of 168 radicals. Comparison between CBS‐QB3/BAC and GA‐calculated thermodynamic values shows that the standard enthalpies of formation generally agree within 4 kJ mol^?1, whereas the entropies and the heat capacities generally deviate <4 J mol^?1 K^?1. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3649–3661, 2018     

Comparison of hemispheric asymmetry measurements for emotional recordings from controls

Four asymmetry measurements (conventional coherence function (CCF), cross wavelet correlation (CWC), phase lag index (PLI), and mean phase coherence (MPC)) have been compared to each other for the first time in order to recognize emotional states (pleasant (P), neutral (N), unpleasant (UP)) from controls in EEG sub-bands (delta (0–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), beta (16–32 Hz), gamma (32–64 Hz)) mediated by affective pictures from the International Affective Picture Archiving System (IAPS). Eight emotional features, computed as hemispheric asymmetry between eight electrode pairs (Fp1 − Fp2, F7 − F8, F3 − F4, C3 − C4, T7 − T8, P7 − P8, P3 − P4, and O1 − O2), have been classified by using data mining methods. Results show that inter-hemispheric emotional functions are mostly mediated by gamma. The best classification is provided by a neural network classifier, while the best features are provided by CWC in time-scale domain due to non-stationary nature of electroencephalographic (EEG) series. The highest asymmetry levels are provided by pleasant pictures at mostly anterio-frontal (F3 − F4) and central (C3 − C4) electrode pairs in gamma. Inter-hemispheric asymmetry levels are changed by each emotional state at all lobes. In conclusion, we can state the followings: (1) Nonlinear and wavelet transform-based methods are more suitable for characterization of EEG; (2) The highest difference in hemispheric asymmetry was observed among emotional states in gamma; (3) Cortical emotional functions are not region-specific, since all lobes are effected by emotional stimuli at different levels; and (4) Pleasant stimuli can strongly mediate the brain in comparison to unpleasant and neutral stimuli.

     

Simulation platform for anticipative plant-level maintenance decision support system

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.     

A comparison of clustering-based privacy-preserving collaborative filtering schemes

Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.In this study, we first propose a content-based profiling (CBP) of users to overcome sparsity issues while performing clustering because the very sparse nature of rating profiles sometimes do not allow strong discrimination. To cope with scalability and accuracy problems of PPCF schemes, we then show how to apply k-means clustering (KMC), fuzzy c-means method (FCM), and self-organizing map (SOM) clustering to CF schemes while preserving users’ confidentiality. After presenting an evaluation of clustering-based methods in terms of privacy and supplementary costs, we carry out real data-based experiments to compare the clustering algorithms within and against traditional CF and PPCF approaches in terms of accuracy. Our empirical outcomes demonstrate that FCM achieves the best low cost performance compared to other methods due to its approximation-based model. The results also show that our privacy-preserving methods are able to offer precise predictions.     

An automated industrial conveyor belt system using image processing and hierarchical clustering for classifying marble slabs

Although there are many industrial machines used in marble industry, classification of marble slabs in terms of quality is generally performed by human experts. Due to economic losses of this rather subjective process, automatic and computerized methods are needed in order to obtain reproducible and objective results in classification. With the aim of remedying this insufficiency in marble industry, a new electro-mechanical system, which automatically classifies marble slabs while they are on a conveyor belt and groups them with the help of a control mechanism, is proposed. The developed system is composed of two parts: the software part acquires digital images of marble slabs, extracts several features using these images, and finally performs the classification using clustering methods. The hardware part is composed of a conveyor belt, a serial port communication system, pneumatic pistons, a programmable logic controller (PLC), and its control circuits, all employed together for grouping the marble slabs mechanically. Although similar studies exist, this paper proposes three novelties over the existing systems. Firstly, a new hierarchical clustering approach is introduced for quality classification without requiring a training set. Secondly, a new feature set based on morphological properties of marble surface images is proposed. Finally, an electro-mechanical system is designed for accomplishing the task of sorting out the classified marble slabs. In the literature, only a system with a labeling mechanism has been presented. Our system, on the other hand, comes with a complete conveyor belt acting as an element that links the production line with the proposed system. This allows the possibility of embedding the proposed system into the production line of a marble factory. It has been observed that although the performance of the developed system is not as high as neural network based systems that use training, it could still be employed in industry when there is no available training set of samples. With this advantage, it provides an increase in the quality control standards of marble slab classification, since marbles are classified with an objective and uniform-through-time criterion.