An XPS investigation of thermal degradation and charring on poly(vinyl chloride)–clay nanocomposites

More information concerning the thermal degradation and charring of nanocomposites of poly(vinyl chloride), dioctyl phthalate and clay has been obtained by the use of X-ray photoelectron spectroscopy and the acquisition of the carbon (C1s), chlorine (Cl2p), and oxygen (O1s) spectra. In the cases of polystyrene–clay and poly(methyl methacrylate)–clay nanocomposites, it has been shown that the clay migrates to the surface as the temperature is raised and the polymer degrades, thereby confirming the barrier properties as a mechanism by which these materials function. For PVC–clay nanocomposites the surface at high temperatures is dominated by carbon, and not the oxygen of the clay. The presence of the clay does retard the chain-stripping degradation of the PVC and the enhanced char formation accounts for the observation of enrichment of carbon

An XPS study of the thermal degradation of polystyrene-clay nanocomposites

X-ray photoelectron spectroscopy, XPS, has been used to examine several polystyrene-clay nanocomposites. The accumulation of oxygen, from the almuniosilicate, on the surface of the polymer was observed, along with the loss of carbon. This confirms that the barrier properties of the clay provide a mechanism by which nanocomposite formation can enhance the fire retardancy of the polymers. No difference is detected depending upon the extent of exfoliation or intercalation of the nanocomposite. #2002 Elsevier Science Ltd. All rights reserved

An XPS Investigation of Thermal Degradation and Charring of PMMA Clay Nanocomposites

Poly(methyl methacrylate)–clay nanocomposites have been studied using X-ray photoelectron spectroscopy. It is clear that as the polymer undergoes thermal degradation, the clay accumulates at the surface and the barrier properties which result from this clay accumulation have been described as the reason for the decreased heat release rate for nanocomposites. The surface composition of the clay changes as the nanocomposite is heated and the changes are affected by the organic-modification that were applied to the clay in order to prepare the nanocomposite

Additional XPS Studies on the Degradation of Poly(Methyl Methacryalte) and Polystyrene Nanocomposites

XPS studies have been undertaken on exfoliated nanocomposites of polystyrene and poly(methyl methacrylate). One can clearly see that carbon is lost and that oxygen, silicon and aluminum accumulate at the surface of the degrading polymer. The concentration of aluminum at the surface is very low at the beginning of the experiment but makes a large jump at the same temperature at which carbon is lost and oxygen begins to accumulate at the surface. It appears that the ratio of silicon to aluminum changes as the polymer is lost. A brief discussion is given to explain the origin of oxygen at the surface

XPS Characterization of Friedel-Crafts Cross-Linked Polystyrene

The combination of a difunctional alkylating agent, either hydroxymethylbenzyl chloride or α,α′-dichloroxylene with polystyrene or high-impact polystyrene together with a Friedel-Crafts catalyst, 2-ethylhexyldiphenylphosphate, and an amine to react with hydrogen chloride has been studied by X-ray photoelectron spectroscopy. The results confirm what had been suggested from previous investigations using thermogravimetric analysis; cross-linking of the polymer occurs as the temperature is raised and the alcohol-containing alkylating agent gives a greater amount of cross-linking than does the dichloro compound

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD.Comment: 10 pages, 14 tables, and 3 figure

Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning

The coupling of computational thermodynamics and kinetics has been the central research theme in Integrated Computational Material Engineering (ICME). Two major bottlenecks in implementing this coupling and performing efficient ICME-guided high-throughput multi-component industrial alloys discovery or process parameters optimization, are slow responses in kinetic calculations to a given set of compositions and processing conditions and the quality of a large amount of calculated thermodynamic data. Here, we employ machine learning techniques to eliminate them, including (1) intelligent corrupt data detection and re-interpolation (i.e. data purge/cleaning) to a big tabulated thermodynamic dataset based on an unsupervised learning algorithm and (2) parameterization via artificial neural networks of the purged big thermodynamic dataset into a non-linear equation consisting of base functions and parameterization coefficients. The two techniques enable the efficient linkage of high-quality data with a previously developed microstructure model. This proposed approach not only improves the model performance by eliminating the interference of the corrupt data and stability due to the boundedness and continuity of the obtained non-linear equation but also dramatically reduces the running time and demand for computer physical memory simultaneously. The high computational robustness, efficiency, and accuracy, which are prerequisites for high-throughput computing, are verified by a series of case studies on multi-component aluminum, steel, and high-entropy alloys. The proposed data purge and parameterization methods are expected to apply to various microstructure simulation approaches or to bridging the multi-scale simulation where handling a large amount of input data is required. It is concluded that machine learning is a valuable tool in fueling the development of ICME and high throughput materials simulations.publishedVersio

SoyTEdb: a comprehensive database of transposable elements in the soybean genome

<p>Abstract</p> <p>Background</p> <p>Transposable elements are the most abundant components of all characterized genomes of higher eukaryotes. It has been documented that these elements not only contribute to the shaping and reshaping of their host genomes, but also play significant roles in regulating gene expression, altering gene function, and creating new genes. Thus, complete identification of transposable elements in sequenced genomes and construction of comprehensive transposable element databases are essential for accurate annotation of genes and other genomic components, for investigation of potential functional interaction between transposable elements and genes, and for study of genome evolution. The recent availability of the soybean genome sequence has provided an unprecedented opportunity for discovery, and structural and functional characterization of transposable elements in this economically important legume crop.</p> <p>Description</p> <p>Using a combination of structure-based and homology-based approaches, a total of 32,552 retrotransposons (Class I) and 6,029 DNA transposons (Class II) with clear boundaries and insertion sites were structurally annotated and clearly categorized, and a soybean transposable element database, SoyTEdb, was established. These transposable elements have been anchored in and integrated with the soybean physical map and genetic map, and are browsable and visualizable at any scale along the 20 soybean chromosomes, along with predicted genes and other sequence annotations. BLAST search and other infrastracture tools were implemented to facilitate annotation of transposable elements or fragments from soybean and other related legume species. The majority (> 95%) of these elements (particularly a few hundred low-copy-number families) are first described in this study.</p> <p>Conclusion</p> <p>SoyTEdb provides resources and information related to transposable elements in the soybean genome, representing the most comprehensive and the largest manually curated transposable element database for any individual plant genome completely sequenced to date. Transposable elements previously identified in legumes, the third largest family of flowering plants, are relatively scarce. Thus this database will facilitate structural, evolutionary, functional, and epigenetic analyses of transposable elements in soybean and other legume species.</p