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This paper presents the impact of accelerated aging on selected mechanical and thermal properties and VOC emission of polypropylene composites filled with glass fiber with different fiber contents. Due to their positive properties (good thermal and mechanical properties, low weight), glass fiber reinforced thermoplastics are becoming increasingly important. Fiber reinforced thermoplastics are mainly produced by injection molding and extrusion, whereby the extrusion compounding process is primarily used to prepare fiber-filled granulates while the injection molding process is used to manufacture products. In this study, short glass fiber reinforced thermoplastics (polypropylene) are produced on a twin screw extruder. Then, tensile test specimens are produced by injection molding. The glass fiber content is between 20 and 40 wt%. In order to investigate the long-term stability, the test specimens are artificially aged in accordance with ASTM 1980. The thermal, mechanical, and emission properties were evaluated by means of differential scanning calorimetry (DSC), tensile tests, and TDS-GC-MS analysis prior to and after accelerated aging. The objective of this study was to investigate the effects of thermal aging on crystallinity and mechanical properties and on VOC emission of glass fiber reinforced isotactic polypropylene.

A series of layered heterostructures made up of varied stackings of PbSe and VSe2 was investigated using photoemission spectroscopy to study controlled doping of the metallic channel layer.

Using the normal incidence x-ray standing wave technique as well as low energy electron microscopy we have investigated the structure of quasi-freestanding monolayer graphene (QFMLG) obtained by intercalation of antimony under the $\left(6\sqrt{3}\times6\sqrt{3}\right)R30^\circ$ reconstructed graphitized 6H-SiC(0001) surface, also known as zeroth-layer graphene. We found that Sb intercalation decouples the QFMLG well from the substrate. The distance from the QFMLG to the Sb layer almost equals the expected van der Waals bonding distance of C and Sb. The Sb intercalation layer itself is mono-atomic, flat, and located much closer to the substrate, at almost the distance of a covalent Sb-Si bond length. All data is consistent with Sb located on top of the uppermost Si atoms of the SiC bulk.

Controlling charge carrier concentrations remains a major challenge in the application of quasi-two-dimensional materials. A promising approach is the modulation doping of transport channels

A series of [(SnSe)1+δ]m[TiSe2]2 heterostructure thin films built up from repeating units of m bilayers of SnSe and two layers of TiSe2 were synthesized from designed precursors. The electronic structure of the films was investigated using X-ray photoelectron spectroscopy for samples with m = 1, 2, 3, and 7 and compared to binary samples of TiSe2 and SnSe. The observed binding energies of core levels and valence bands of the heterostructures are largely independent of m. For the SnSe layers, we can observe a rigid band shift in the heterostructures compared to the binary, which can be explained by electron transfer from SnSe to TiSe2. The electronic structure of the TiSe2 layers shows a more complicated behavior, as a small shift can be observed in the valence band and Se3d spectra, but the Ti2p core level remains at a constant energy. Complementary UV photoemission spectroscopy measurements confirm a charge transfer mechanism where the SnSe layers donate electrons into empty Ti3d states at the Fermi energy.

Due to their valuable properties (low weight, and good thermal and mechanical properties), glass fiber reinforced thermoplastics are becoming increasingly important. Fiber-reinforced thermoplastics are mainly manufactured by injection molding and extrusion, whereby the extrusion compounding process is primarily used to produce fiber-filled granulates. Reproducible production of high-quality components requires a granulate in which the fiber length is even and high. However, the extrusion process leads to the fact that fiber breakages can occur during processing. To enable a significant quality enhancement, experimentally validated modeling is required. In this study, short glass fiber reinforced thermoplastics (polypropylene) were produced on two different twin-screw extruders. Therefore, the machine-specific process behavior is of major interest regarding its influence. First, the fiber length change after processing was determined by experimental investigations and then simulated with the SIGMA simulation software. By comparing the simulation and experimental tests, important insights could be gained and the effects on fiber lengths could be determined in advance. The resulting fiber lengths and distributions were different, not only for different screw configurations (SC), but also for the same screw configurations on different twin-screw extruders. This may have been due to manufacturer-specific tolerances.

Intercalation experiments on epitaxial graphene are attracting a lot of attention at present as a tool to further boost the electronic properties of 2D graphene. In this work, we studied the intercalation of Pb using buffer layers on 6H-SiC(0001) by means of electron diffraction, scanning tunneling microscopy, photoelectron spectroscopy and in situ surface transport. Large-area intercalation of a few Pb monolayers succeeded via surface defects. The intercalated Pb forms a characteristic striped phase and leads to formation of almost charge neutral graphene in proximity to a Pb layer. The Pb intercalated layer consists of 2 ML and shows a strong structural corrugation. The epitaxial heterostructure provides an extremely high conductivity of σ=100 mS/□. However, at low temperatures (70 K), we found a metal-insulator transition that we assign to the formation of minigaps in epitaxial graphene, possibly induced by a static distortion of graphene following the corrugation of the interface layer.

The analysis of integral membrane proteins (IMPs) with mass spectrometry-centered technologies has undergone great progress during the past few years, allowing for the analysis of several hundreds of IMPs. In this study, we investigated three promising shotgun approaches for the identification of IMPs of the model organism Bacillus subtilis. One comprises a classical membrane preparation procedure with carbonate and high-ionic-strength buffers, followed by SDS-PAGE and LC-MS/MS analysis. The two others are based on enzymatic trimming of the crude membrane fraction either with trypsin or proteinase K and subsequent gel-free analysis. As a result, we observed the highest degree of complementarity between the gel-based and the proteinase K approach, since the first exclusively addresses soluble loops and domains of IMPs and gave rise to 8709 unique peptides, whereas the latter contributed 1180 unique peptide identifications from otherwise inaccessible transmembrane helices (TMHs). All three methods contribute significant numbers (381, 284, and 276, respectively) to the total of 527 IMP identifications from the membrane fraction of exponentially growing B. subtilis cells, thus representing approximately 69% of all transcribed IMPs.

The proteome of exponentially growing Bacillus subtilis cells was dissected by the implementation of shotgun proteomics and a semigel-based approach for a particular exploration of membrane proteins. The current number of 745 protein identifications that was gained by the use of two-dimensional gel electrophoresis could be increased by 473 additional proteins. Therefore, almost 50% of the 2500 genes expressed in growing B. subtilis cells have been demonstrated at the protein level. In terms of exploring cellular physiology and adaptation to environmental changes or stress, proteins showing an alteration in expression level are of primary interest. The large number of vegetative proteins identified by gel-based and gel-free approaches is a good starting point for comparative physiological investigations. For this reason a gel-free quantitation with the recently introduced iTRAQ™ (isobaric tagging for relative and absolute quantitation) reagent technique was performed to investigate the heat shock response in B. subtilis. A comparison with gel-based data showed that both techniques revealed a similar level of up-regulation for proteins belonging to well studied heat hock regulons (SigB, HrcA, and CtsR). However, additional datasets have been obtained by the gel-free approach indicating a strong heat sensitivity of specific enzymes involved in amino acid synthesis.

The Gram-positive bacterium Staphylococcus aureus is a serious human pathogen causing a wide variety of diseases, and its increasing resistance toward all available antibiotics makes its further investigation absolutely essential. We examined the membrane proteome of exponentially growing cells of S. aureus COL because this subproteome plays a major role in the virulence of the bacterium in its host. In general, an analysis of membrane proteins is impeded by their hydrophobic nature as well as by a high abundance of many cytosolic proteins. The implementation of three different technologies, one-dimensional gel-LC, two-dimensional LC, and a membrane shaving approach combined with MS/MS analyses, enabled an identification of 271 integral and 86 peripheral membrane proteins from exponentially growing cells. In particular, the latter approach that combined membrane shaving with a subsequent chymotrypsin digest of integral membrane domains of proteins greatly facilitated the detection of hydrophobic peptides derived from membrane-spanning segments (713 peptides, 60% of all peptides) and therefore yielded almost exclusively highly hydrophobic integral membrane proteins (96.7%). A comparison of the various methods disclosed the one-dimensional gel-LC and the shaving approach to be highly complementary techniques. A combination of them will reveal a most comprehensive view on membrane proteomes.

With the emergence of mass spectrometry in protein science and the availability of complete genome sequences, proteomics has gone through a rapid development. The soil bacterium Bacillus subtilis, as one of the first DNA sequenced species, represents a model for Gram-positive bacteria and its proteome was extensively studied throughout the years. Having the final goal to elucidate how life really functions, one basic requirement is to know the entirety of cellular proteins. This review presents how far we have got in unraveling the proteome of B. subtilis. The application of gel-based and gel-free technologies, the analyses of different subcellular proteome fractions, and the pursuance of various physiological strategies resulted in a coverage of more than one-third of B. subtilis theoretical proteome. (c) 2006 Elsevier B.V. All rights reserved.

The proteome of exponentially growing Bacillus subtilis cells was dissected by the implementation of shotgun proteomics and a semigel-based approach for a particular exploration of membrane proteins. The current number of 745 protein identifications that was gained by the use of two-dimensional gel electrophoresis could be increased by 473 additional proteins. Therefore, almost 50% of the 2500 genes expressed in growing B. subtilis cells have been demonstrated at the protein level. In terms of exploring cellular physiology and adaptation to environmental changes or stress, proteins showing an alteration in expression level are of primary interest. The large number of vegetative proteins identified by gel-based and gel-free approaches is a good starting point for comparative physiological investigations. For this reason a gel-free quantitation with the recently introduced iTRAQ (isobaric tagging for relative and absolute quantitation) reagent technique was performed to investigate the heat shock response in B. subtilis. A comparison with gel-based data showed that both techniques revealed a similar level of up-regulation for proteins belonging to well studied heat hock regulons (SigB, HrcA, and CtsR). However, additional datasets have been obtained by the gel-free approach indicating a strong heat sensitivity of specific enzymes involved in amino acid synthesis.

Staphylococcus aureus is a versatile human pathogen causing a wide variety of diseases ranging from wound infection to endocarditis, osteomyelitis, and sepsis. In order to investigate this pathogen, we sought to analyze the cytoplasmic proteome of S. aureus COL by using two different approaches: two-dimensional (2D) gel analyses combined with matrix-assisted laser ionization-time of flight mass spectrometry and a gel-free system using multidimensional liquid chromatography followed by mass spectrometry. By combining both analyses we identified 1123 cytoplasmic proteins that represent two-thirds of the cytoplasmic proteome of the organism. With our standard 2D gel setup (pI 4-7) we identified 473 proteins that cover about 40% of the cytoplasmic proteome predicted for this proteomic window. The identified proteins belong to a variety of cellular functions ranging from the transcriptional and translational machinery, tricarboxylic acid cycle (TCC), glycolysis, and fermentation pathways to biosynthetic pathways of nucleotides, fatty acids, and cell wall components. While most of the metabolic pathways predicted for S. aureus were covered by this gel-based proteomics 650 additional proteins were identified by the gel-free approach, among them alkaline or hydrophobic proteins. In our work, we established a master 2D gel that enabled us to study the regulation of core carbon metabolism in S. aureus cells grown in a complex medium. Our comparison of the protein pattern of exponentially growing cells with that of stationary-phase cells revealed a higher amount of enzymes involved in protein synthesis, transcription, and glycolysis in exponentially growing cells. In contrast, enzymes of the TCC and gluconeogenesis are increased at the stationary phase. With this comprehensive proteome map we have an essential tool for a better understanding of cell physiology of the human pathogen, S. aureus.

This study exploits statistical redundancy inherent in natural language to automatically predict scores for essays. We use a hybrid feature identification method, including syntactic structure analysis, rhetorical structure analysis, and topical analysis, to score essay responses from test-takers of the Graduate Management Admissions Test (GMAT) and the Test of Written English (TWE). For each essay question, a stepwise linear regression analysis is run on a training set (sample of human scored essay responses) to extract a weighted set of predictive features for each test question. Score prediction for cross-validation sets is calculated from the set of predictive features. Exact or adjacent agreement between the Electronic Essay Rater (e-rater) score predictions and human rater scores ranged from 87% to 94% across the 15 test questions.

This paper describes a prototype for automatically scoring College Board Advanced Placement (AP) Biology essays.1. The scoring technique used in this study was based on a previous method used to score sentence-length responses (Burstein, et al, 1996). One hundred training essays were used to build an example-based lexicon and concept grammars. The prototype accesses information from the lexicon and concept grammars to score essays by assigning a classification of Excellent or Poor based on the number of points assigned during scoring. Final computer-based essay scores are based on the system's recognition of conceptual information in the essays. Conceptual analysis in essays is essential to provide a classification based on the essay content. In addition, computer-generated information about essay content can be used to produce diagnostic feedback. The set of essays used in this study had been scored by human raters. The results reported in the paper show 94% agreement on exact or adjacent scores between human rater scores and computer-based scores for 105 test essays. The methods underlying this application could be used in a number of applications involving rapid semantic analysis of textual materials, especially with regard to scientific or other technical text.

Developing more shareable resources to support natural language analysis will make it easier and cheaper to create new language processing applications and to support research in computational linguistics. One natural candidate for such a resource is a broad-coverage dictionary, since the work required to create such a dictionary is large but there is general agreement on at least some of the information to be recorded for each word. The Linguistic Data Consortium has begun an effort to create several such lexical resources, under the rubric "COMLEX" (COMmon LEXicon); one of these projects is the COMLEX Syntax Project.

This report evaluates the construct coverage of the e-rater® scoring engine. The matter of construct coverage depends on whether one defines writing skill, in terms of process or product. Originally, the e-rater engine consisted of a large set of components with a proven ability to predict human holistic scores. By organizing these capabilities into features, e-rater researchers organized the e-rater engine along the lines of trait scoring, which recognizes that essay quality has several dimensions. Some traits of essay quality cut across different methods for scoring essay quality, such as the rubrics employed by the GRE® and TOEFL® assessments, as well as the 6-trait scoring model. Factor analyses conducted by Attali and Powers (2008) suggest that e-rater features capture low-level aspects of essay quality, such as sentence complexity, vocabulary, and conventions. Future e-rater development should focus on (a) deepening and expanding coverage of the construct, such as by developing measures of essay content and organization, as well as on (b) addressing accuracy issues in existing features.

This report discusses the development and evaluation of a research prototype system designed to automatically score essay responses to the GMAT Analytical Writing Assessments: (a). Analysis of an Argument (Argument essays) and (b). Analysis of an Issue (Issue essays) item types. The system, Electronic Essay Rater (e-rater), was designed to automatically analyze several features of an essay and score the essay based on the features of writing as specified in holistic rubrics. E-rater uses a hybrid feature methodology. It incorporates several variables that are derived statistically, extracted through NLP techniques, or achieved by simple “counting” procedures. The version of the e-rater described in this report uses five sets of critical feature variables to build the final linear regression model used for predicting scores. The same set of critical variables was used to fit models for the issue and argument training essays and the following results were achieved. For the set of 275 cross-validation data, exact or adjacent agreement with human rater scores reached 95%. For the 282 cross-validation issue essays exact or adjacent agreement with human rater scores achieved 93%. The rich feature variables used as score predictors in e-rater could potentially be used to generate explanation of score predictions, and diagnostic and instructional information.

The Natural Language Processing Initiative (NLPI) at ETS currently does research and development for computer-assisted scoring of natural language constructed-responses. The purpose of NLPI's work is to develop computer-assisted methods for scoring so that computer-administered natural language constructed-response items can be integrated into ETS' tests which can be scored efficiently with regard to time and cost. Until recently, NLPI's automated scoring efforts were primarily devoted to the development of computer programs used to score short-answer responses constructed-responses of up to 15 words. In previous studies, we have developed scoring systems for main idea item types (e.g., GUIDES items) and divergent thinking item types (e.g., the Formulating-Hypotheses item (FH)). In the current study, we applied similar techniques to those used to score short-answer responses in order to implement a prototype to automatically score AP Biology essays. This paper reports on the development process involved in building a linguistically-based, computer scoring prototype for AP Biology essays and the results of the scoring system. The prototype was used to score essays that had been assigned an Excellent rating (9 or 10 points) or a Poor rating (0 - 3 points) by human graders.1 The results reported in the paper illustrate agreement between human rater scores and computer-based scores. The results of the study are promising and would encourage continued research in automated scoring of essays with highly analytical rubrics.

Constructed-response items, whose responses consist of words, phrases, sentences, paragraphs, and essays are among the most difficult and costly to score. The increased use of constructed-response items like essays creates a need for tools to partially or fully automatically score these responses. This study explores one approach to analyzing essay-length natural language constructed-responses. In this study we develop and evaluate a decision model for scoring essays. The decision model uses off-the-shelf software for grammar and style checking of the English language. The first part of this study consisted of an evaluation of several commercial grammar checking programs. From this evaluation we select the best performing grammar checking programs to construct a decision model for scoring the essays. The second part of the study uses data produced from the selected grammar checking program(s) to make a decision about the score for an essay. Through statistical and linguistic methods, we analyze the performance of the decision model in an effort to understand its usefulness and practicality in a production scoring setting.

SummaryThe Neo-Latin portion of medical vocabulary exhibits morphosemantic regularities that make it possible to determine both English syntactic subclasses and medical semantic subclasses from formal properties of these lexical items alone. This paper describes an experimental program for the automatic creation of dictionary entries that exploits the formal regularities to obtain dictionary entries sufficient for computerized medical text analysis as presently carried out by the New York University Linguistic String Project (LSP) system. Although automatic dictionary preparation does not supersede manual classification, the program takes a considerable burden off the dictionary worker’s shoulders and speeds the costly preprocessing stages of computerized text analysis.