Scale formation has been recognised as a widespread problem in membrane filtration and water desalination processing for water treatment. In this paper, the effect of surface energy of Ni-P-PTFE composite coatings on the microstructure and adhesion of CaSO4 deposits was investigated. The surface energies of the Ni-P-PTFE coatings were altered by changing the PTFE proportion in the coatings. Initial experimental results showed that the surface energy of the coatings had a significant influence on the microstructure and adhesion of CaSO4 deposits. The Ni-P-PTFE coatings have a potential for reducing CaSO4 scale formation on water treatment equipment. 相似文献
Regularly dispersed Pt particles on SBA-15 supported catalysts were synthesized with a Pt loading of 5 wt% by a sol-immobilisation method, wherein various Pt particle sizes within 1–5 nm were finely controlled via the adjustment of the addition amount of polyvinyl alcohol (PVA). A high PVA/Pt ratio of the initial solution tended to generate small Pt particles on the SBA-15 support due to intense protection against Pt particle aggregation. In addition, the effect of Pt particle size on naphthalene hydrogenation was investigated in terms of catalytic performance. Compared with the performance of other catalysts with Pt particle sizes greater or less than 3.5 nm, Pt nanoparticles with sizes centered at 3.5 nm exhibited excellent catalytic performance towards decalin. This excellent catalytic performance was mainly attributed to a suitable ratio of the edge sites to flat sites on these Pt nanoparticles, benefitting the rapid adsorption of naphthalene and dissociation of hydrogen.
Graphical Abstract
The Pt/SBA-15 catalysts were prepared by sol-immobilisation method. The highest performance was attributed to the Pt-nanoparticles with suitable flat/edge sites ratio.
Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls (NC), 16 early MCI, and 17 early stage AD—are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD. 相似文献