Enhanced heat transfer performance of alumina sponge-like nano-porous structures through surface wettability control in nucleate pool boiling

For several decades, a porous surface has been recognized as an efficient medium to increase boiling performance in a nucleate boiling regime. Most feasible porous surfaces have been studied in millimeter and micron-sized domains. It has been believed that a higher wall superheat is required to commence incipient nucleate boiling under a submicron regime. In this study, we demonstrate that a significantly enhanced pool boiling heat transfer is observed in a submicron regime through three dimensionally interconnected hybrid pores: the Alumina sponge-like nano-porous structure (ASNPS). The structural uniqueness of the ASNPS leads to an enlarged surface area, increases the potential number of the active nucleation site density, and improves the vapor–liquid menisci through the reentrant pore. Simultaneously, by changing the surface wettability with a hydrophobic self-assembled monolayer (SAM) coating, the number of active nucleation site density is improved. Eventually, the combination of the ASNPS and hydrophobic SAM coating can achieve substantial heat transfer coefficient (HTC) enhancement in the nucleate boiling. Also, the thickness of the ASNPS is a critical issue to adequately augment the HTC in pool boiling. The thickness of the ASNPS is optimized by examining the boiling performance of the ASNPS fabricated in different amounts of anodizing times. A classical mechanistic model from literature was modified and compared with the experimentally obtained data. The modified mechanistic model – with the combination of forced-convection and thin liquid film evaporation – showed reasonable predictions.     

Training Algorithm with Incomplete Data for Feed-Forward Neural Networks

A new algorithm is developed to train feed-forward neural networks for non-linear input-to-output mappings with small incomplete data in arbitrary distributions. The developed Training-EStimation-Training (TEST) algorithm consists of 3 steps, i.e., (1) training with the complete portion of the training data set, (2) estimation of the missing attributes with the trained neural networks, and (3) re-training the neural networks with the whole data set. Error back propagation is still applicable to estimate the missing attributes. Unlike other training methods with missing data, it does not assume data distribution models which may not be appropriate for small training data. The developed TEST algorithm is first tested for the Iris benchmark data. By randomly removing some attributes from the complete data set and estimating the values latter, accuracy of the TEST algorithm is demonstrated. Then it is applied to the Diabetes benchmark data, of which about 50% contains missing attributes. Compared with other existing algorithms, the proposed TEST algorithm results in much better recognition accuracy for test data.     

The utilization of microencapsulated phase change material wallboards for energy saving

Wallboards with micro encapsulated phase changing material (micro PCM) were used to investigate the performance and the energy saving characteristics as building materials in winter and summer climate conditions. The test house consisted of a boiler with under floor heating system, an air conditioner, micro PCM wallboard room and conventional wallboard room. The outer temperature of the rooms could be artificially controlled at the temperature range of −12 to 35 °C. Micro PCM content in wallboards was 0–4 kg/m². The melting temperature and latent heat of Micro PCM are 23 °C and 211 J/g. Also, micro PCM shows stable mechanical strength under 500 psi. As micro PCM content increased, the temperature fluctuations decreased. In case of micro PCM wallboard, temperature profiles in the room show stable and comfortable ranges. The optimum amount of micro PCM in wallboard to maximize energy saving efficiency was around 3 kg/m².     

Hypoxia Suppresses Spontaneous Mineralization and Osteogenic Differentiation of Mesenchymal Stem Cells via IGFBP3 Up-Regulation

Hypoxia has diverse stimulatory effects on human adipose-derived stem cells (ASCs). In the present study, we investigated whether hypoxic culture conditions (2% O₂) suppress spontaneous mineralization and osteogenic differentiation of ASCs. We also investigated signaling pathways and molecular mechanisms involved in this process. We found that hypoxia suppressed spontaneous mineralization and osteogenic differentiation of ASCs, and up-regulated mRNA and protein expression of Insulin-like growth factor binding proteins (IGFBPs) in ASCs. Although treatment with recombinant IGFBPs did not affect osteogenic differentiation of ASCs, siRNA-mediated inhibition of IGFBP3 attenuated hypoxia-suppressed osteogenic differentiation of ASCs. In contrast, overexpression of IGFBP3 via lentiviral vectors inhibited ASC osteogenic differentiation. These results indicate that hypoxia suppresses spontaneous mineralization and osteogenic differentiation of ASCs via intracellular IGFBP3 up-regulation. We determined that reactive oxygen species (ROS) generation followed by activation of the MAPK and PI3K/Akt pathways play pivotal roles in IGFBP3 expression under hypoxia. For example, ROS scavengers and inhibitors for MAPK and PI3K/Akt pathways attenuated the hypoxia-induced IGFBP3 expression. Inhibition of Elk1 and NF-κB through siRNA transfection also led to down-regulation of IGFBP3 mRNA expression. We next addressed the proliferative potential of ASCs with overexpressed IGFBP3, but IGFBP3 overexpression reduced the proliferation of ASCs. In addition, hypoxia reduced the osteogenic differentiation of bone marrow-derived clonal mesenchymal stem cells. Collectively, our results indicate that hypoxia suppresses the osteogenic differentiation of mesenchymal stem cells via IGFBP3 up-regulation.     

Surface photoabsorption monitoring of the growth of GaAs and InGaAs at 650°C by MOCVD

By monitoring the cyclic behavior of surface photoabsorption (SPA) reflectance changes during the growth of GaAs at 650°C and with sufficient H₂ purging time between the supply of trimethylgallium and AsH₃, we have been able to achieve controlled growth of GaAs down to a monolayer. Our results show, as confirmed by photoluminescence (PL) measurements, the possibility of growing highly accurate quantum well heterostructures by metalorganic chemical vapor deposition at conventional growth temperatures. We also present our PL measurements on the InGaAs single quantum wells grown at this temperature by monitoring the SPA signal.     

Holographic optical elements recorded in silver halide sensitized gelatin emulsions. Part 2. Reflection holographic optical elements

Silver halide sensitized gelatin (SHSG) holograms are similar to holograms recorded in dichromated gelatin (DCG), the main recording material for holographic optical elements (HOEs). The drawback of DCG is its low energetic sensitivity and limited spectral response. Silver halide materials can be processed in such away that the final hologram will have properties like a DCG hologram. Recently this technique has become more interesting since the introduction of new ultra-fine-grain silver halide (AgHal) emulsions. In particular, high spatial-frequency fringes associated with HOEs of the reflection type are difficult to construct when SHSG processing methods are employed. Therefore an optimized processing technique for reflection HOEs recorded in the new AgHal materials is introduced. Diffraction efficiencies over 90% can be obtained repeatably for reflection diffraction gratings. Understanding the importance of a selective hardening process has made it possible to obtain results similar to conventional DCG processing. The main advantage of the SHSG process is that high-sensitivity recording can be performed with laser wavelengths anywhere within the visible spectrum. This simplifies the manufacturing of high-quality, large-format HOEs, also including high-quality display holograms of the reflection type in both monochrome and full color.