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The growth of oxygenated carbonaceous (oxy-carbon) species on the surface of Pt/Al 2 O 3 during total oxidation of propane is analyzed in detail—including their composition, their location on the catalyst surface, their reactivity, and their role in the propane oxidation mechanism—by in situ diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS). Platinum nanoparticles catalyze the transformation of propane into many different oxy-carbon surface species, including acetate, enolate, aliphatic ester, and acetone, which spillover and grow on the Al 2 O 3 support. There is no correlation between the concentration of oxy-carbon surface species and the rate of CO 2 production in the gas-phase, which indicates that these species are inert spectators in the propane oxidation mechanism. Temperature-programmed oxidation of the oxy-carbon surface species reveals that enolate, aliphatic ester, and acetone species are removed from the surface by combustion at similar temperatures with an activation barrier of 112 kJ/mol, whereas acetate species are removed at higher temperatures with an activation barrier of 147 kJ/mol. Both the formation and combustion of oxy-carbon surface species occur in pathways that are parallel to, and orders-of-magnitude slower than, the main pathway to CO 2 production. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) or the extraction of concepts from clinical text is the task of identifying entities in text and slotting them into categories such as problems, treatments, tests, clinical departments, occurrences (such as admission and discharge) and others. NER forms a critical component of processing and leveraging unstructured data from Electronic Health Records (EHR). While identifying the spans and categories of concepts is itself a challenging task, these entities could also have attributes such as negation that pivot their meanings implied to the consumers of the named entities. There has been little research dedicated to identifying the entities and their qualifying attributes together. This research hopes to contribute to the area of detecting entities and their corresponding attributes by modelling the NER task as a supervised, multi-label tagging problem with each of the attributes assigned tagging sequence labels. In this paper, we propose 3 architectures to achieve this multi-label entity tagging: BiLSTM n-CRF, BiLSTM-CRF-Smax-TF and BiLSTM n-CRF-TF. We evaluate these methods on the 2010 i2b2/VA and the i2b2 2012 shared task datasets. Our different models obtain best NER scores of 0.903 and 0.808 on the i2b2 2010/VA and i2b2 2012 respectively. The highest span based micro-averaged F1 polarity scores obtained were 0.832 and 0.836 on the i2b2 2010/VA and i2b2 2012 datasets respectively, and the highest macro-averaged F1 polarity scores obtained were 0.924 and 0.888 respectively. The modality studies conducted on i2b2 2012 dataset revealed high scores of 0.818 and 0.501 for span based micro-averaged F1 and macro-averaged F1 respectively. [ABSTRACT FROM AUTHOR]

The developed multispectral imaging system was tested in the current study for its detection accuracy of aflatoxin B1 (AFB1) in single kernel almonds. Based on our previous study, characteristic wavelengths for aflatoxin B1 (AFB1) were selected from the hyperspectral imaging data and a prototype of almond sorter was designed and developed using multispectral imaging system. Five levels of AFB1 concentrations, viz. 0.25, 0.50, 0.75, 1.00 μg/kernel were used for testing of the sorter. Artificial neural network (ANN) was used to predict the AFB1 contamination levels and four discriminant models, viz. support vector machine (SVM), logistic regression (LR), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were analyzed to classify the contaminated kernels from the normal ones. Results indicated that ANN was very efficient in prediction of AFB1 threshold contamination levels of 0.250 µg/g with accuracy of 99.300%, 98.100%, 95.700%, 86.200% and least error rate of 0.042, 0.077, 0.109 µg/g, 0.187 µg/g respectively for training, cross-validation, prediction and testing using external test set data. The two-class LR model accurately discriminated the contaminated from the noncontaminated almonds with a threshold of 0.25 µg/g and the correct classification rate of 90.800% and 95% for cross-validation and external test datasets, respectively. The multispectral imaging system demonstrated its potential application to rapid online detection of AFB1 contamination levels in single almond kernels and discrimination of contaminated kernels at the same time. • A multispectral imaging system was tested for its accuracy in detection of AFB1 in single kernel almonds. • ANN used for prediction of AFB1 in the almond kernels with R v of 0.98 & RMSEV of 0.077 µg/g. • SVM, LR, LDA, QDA models were developed to classify the control and contaminated kernels. • LR model accurately discriminated the contaminated kernels from the control with CCR of 90.8%. [ABSTRACT FROM AUTHOR]

The kinetics of propane oxidation over Pt/Al 2 O 3 are investigated in this work as a function of O 2 /C 3 H 8 ratio in the 150–300 °C temperature range. At high O 2 /C 3 H 8 ratios, the platinum nanoparticles are saturated with oxygen and the reaction rate is zero-order with respect to the oxygen partial pressures in this regime. As the oxygen coverage decreases with decreasing O 2 /C 3 H 8 ratio, the reaction rate increases and the reaction order changes from zero-order to negative-order in the oxygen partial pressure. The reaction rate is controlled to a large extent by the oxygen coverage on the platinum nanoparticles. However, at lower temperatures and higher oxygen pressures there is a slow deactivation of the catalyst that cannot be explained by a slow change in the oxygen coverage. Diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) of adsorbed CO was performed to track the evolution of the nanoparticle structure over the course of the propane oxidation reaction and to determine whether the slow deactivation was caused by reconstruction of the platinum nanoparticles. We found that the platinum nanoparticles are significantly reconstructed during the course of the reaction, including the formation of a platinum oxide (PtO) which has a characteristic CO-DRIFTS band at 2123 cm −1 . The extent of PtO formation decreases with increasing temperature and, as a result, deactivation of the catalyst is less severe at higher temperatures. Unexpectedly, increasing the oxygen partial pressure resulted in less PtO formation. We believe that a different platinum oxide phase (e.g. PtO 2 or Pt 3 O 4 ) is formed at higher oxygen pressures, which is reduced to metallic platinum during CO exposure at 25 °C, and therefore is not detectable by CO-DRIFTS. These results are unique because they show how the nanoparticle structure evolves over many hours of propane oxidation, and how the temperature and oxygen pressure influence the reconstruction of the nanoparticles, which has implications for a wide range of reactions not limited to propane oxidation. [ABSTRACT FROM AUTHOR]

The pillared MWW (PMWW or MCM-36) and pillared MFI (PMFI) zeolites are 2-dimensional (2D) catalytically active materials made by pillaring of layered MCM-22(P) and multilamellar MFI precursors, respectively. The single- or near single-unit-cell thick 2D microporous layers in PMWW and PMFI expose comparable number of external surface acid sites ( i.e. , acid sites enclosed in mesopores between zeolitic layers) to those in micropores, which become important or dominant contributor to the catalytic properties. Although the acidity and catalytic activities of PMWW and PMFI have been studied, modification of their external surfaces and its implications on catalytic reactions are not available. In the present study, we report the tailoring of external surfaces of PMWW and PMFI zeolites by atomic layer deposition (ALD) of silicon (ALD-Si) and aluminum (ALD-Al), respectively. The textural, acidic and catalytic properties of the ALD modified pillared zeolites were investigated using a variety of characterization methods. ALD-Al and ALD-Si modifications kept micropore almost intact, but resulted in significant reduction in mesopore volume and considerable changes in external surface composition and acidity. The catalytic tests showed that intrinsic catalytic behavior of Brønsted acid sites in ALD modified pillared zeolites was similar to their parent counterparts. In diffusion constrained parallel reactions, ALD of Al- or Si-species altered catalyst selectivity. In addition, ALD of Si-species on PMFI zeolite deactivated surface active sites, which resulted in improved catalytic activity in direct methane aromatization reactions under optimal ALD cycles. The study exemplified for the first time that ALD is an effective tool for tuning the surface properties of 2D unit-cell thick zeolites. [ABSTRACT FROM AUTHOR]

Almonds are highly susceptible to Aflatoxin B1 (AFB1) contamination, which can result in significant economic losses. Current detection techniques are destructive, time consuming and unfit for in-line application. This study investigated the potential of hyperspectral imaging in the near infrared (NIR) range (900–1700 nm) to develop a rapid and non-destructive protocol for determination of AFB1 content of single almond kernels. Almond kernels were treated to varying AFB1 concentration by artificial infection using standard AFB1 solution and used for Experimentation. Reference AFB1 concentration and their association with the spectral data were modelled using partial least squares regression (PLSR) combined with suitable spectral preprocessing techniques. Superior models were obtained with full-spectrum regression models with R2 and RMSEP values of 0.958 and 0.089 μg/g, respectively. Competitive-adaptive reweighted sampling (CARS) was used to select the feature wavelengths for rapid quantification of AFB1 at commercial scale. Multiple linear regression (MLR) models were developed using the selected wavelengths for its applicability in multispectral imaging systems. Results showed that the MLR model achieved good prediction capabilities with R2 and RMSEP values of 0.948 and 0.090 μg/g, respectively. The results demonstrated that hyperspectral imaging had potential for rapid and nondestructive determination of AFB1 concentration in single kernel almonds. • Hyperspectral imaging was used to develop non-destructive method for detection of aflatoxin B1 in almonds. • Calibration models were developed using PLSR and suitable preprocessing techniques. • CARS-PLS was used to select the feature wavelengths for rapid and accurate quantification. • MLR models with feature wavelengths showed good prediction capabilities. [ABSTRACT FROM AUTHOR]