Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Analytical Chemistry
Title: Black Phosphorus Gas Sensors
Abstract: The utilization of black phosphorus and its monolayer (phosphorene) and few-layers in field-effect transistors has attracted a lot of attention to this elemental two-dimensional material. Various studies on optimization of black phosphorus field-effect transistors, PN junctions, photodetectors, and other applications have been demonstrated. Although chemical sensing based on black phosphorus devices was theoretically predicted, there is still no experimental verification of such an important study of this material. In this article, we report on chemical sensing of nitrogen dioxide (NO2) using field-effect transistors based on multilayer black phosphorus. Black phosphorus sensors exhibited increased conduction upon NO2 exposure and excellent sensitivity for detection of NO2 down to 5 ppb. Moreover, when the multilayer black phosphorus field-effect transistor was exposed to NO2 concentrations of 5, 10, 20, and 40 ppb, its relative conduction change followed the Langmuir isotherm for molecules adsorbed on a surface. Additionally, on the basis of an exponential conductance change, the rate constants for adsorption and desorption of NO2 on black phosphorus were extracted for different NO2 concentrations, and they were in the range of 130-840 s. These results shed light on important electronic and sensing characteristics of black phosphorus, which can be utilized in future studies and applications.
Extracted statement — Sensing material: Black phosphorus
Research field: Analytical Chemistry
Title: Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication
Abstract: Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.
Extracted statement — Material: complex biological matrices
Research field: Analytical Chemistry
Title: Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication
Abstract: Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.
Extracted statement — Material: complex NPs extracts
Research field: Analytical Chemistry
Title: Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication
Abstract: Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.
Extracted statement — Material: dedicated fragmentation databases
Research field: Analytical Chemistry
Title: Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication
Abstract: Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.
Extracted statement — Material: extensive in-silico MS/MS fragmentation database
Research field: Analytical Chemistry
Title: Highly sensitive MoTe
                    2
                    chemical sensor with fast recovery rate through gate biasing
Abstract: The unique properties of two dimensional (2D) materials make them promising candidates for chemical and biological sensing applications. However, most 2D nanomaterial sensors suffer very long recovery time due to slow molecular desorption at room temperature. Here, we report a highly sensitive molybdenum ditelluride (MoTe2) gas sensor for NO2 and NH3 detection with greatly enhanced recovery rate. The effects of gate bias on sensing performance have been systematically studied. It is found that the recovery kinetics can be effectively adjusted by biasing the sensor to different gate voltages. Under the optimum biasing potential, the MoTe2 sensor can achieve more than 90% recovery after each sensing cycle well within 10 min at room temperature. The results demonstrate the potential of MoTe2 as a promising candidate for high-performance chemical sensors. The idea of exploiting gate bias to adjust molecular desorption kinetics can be readily applied to much wider sensing platforms based on 2D nanomaterials.
Extracted statement — Sensing material: Molybdenum ditelluride
Research field: Analytical Chemistry
Title: Black Phosphorus Gas Sensors
Abstract: The utilization of black phosphorus and its monolayer (phosphorene) and few-layers in field-effect transistors has attracted a lot of attention to this elemental two-dimensional material. Various studies on optimization of black phosphorus field-effect transistors, PN junctions, photodetectors, and other applications have been demonstrated. Although chemical sensing based on black phosphorus devices was theoretically predicted, there is still no experimental verification of such an important study of this material. In this article, we report on chemical sensing of nitrogen dioxide (NO2) using field-effect transistors based on multilayer black phosphorus. Black phosphorus sensors exhibited increased conduction upon NO2 exposure and excellent sensitivity for detection of NO2 down to 5 ppb. Moreover, when the multilayer black phosphorus field-effect transistor was exposed to NO2 concentrations of 5, 10, 20, and 40 ppb, its relative conduction change followed the Langmuir isotherm for molecules adsorbed on a surface. Additionally, on the basis of an exponential conductance change, the rate constants for adsorption and desorption of NO2 on black phosphorus were extracted for different NO2 concentrations, and they were in the range of 130-840 s. These results shed light on important electronic and sensing characteristics of black phosphorus, which can be utilized in future studies and applications.
Extracted statement — Analyte: Nitrogen dioxide
Research field: Analytical Chemistry
Title: Physisorption-Based Charge Transfer in Two-Dimensional SnS2 for Selective and Reversible NO2 Gas Sensing
Abstract: Nitrogen dioxide (NO2) is a gas species that plays an important role in certain industrial, farming, and healthcare sectors. However, there are still significant challenges for NO2 sensing at low detection limits, especially in the presence of other interfering gases. The NO2 selectivity of current gas-sensing technologies is significantly traded-off with their sensitivity and reversibility as well as fabrication and operating costs. In this work, we present an important progress for selective and reversible NO2 sensing by demonstrating an economical sensing platform based on the charge transfer between physisorbed NO2 gas molecules and two-dimensional (2D) tin disulfide (SnS2) flakes at low operating temperatures. The device shows high sensitivity and superior selectivity to NO2 at operating temperatures of less than 160 °C, which are well below those of chemisorptive and ion conductive NO2 sensors with much poorer selectivity. At the same time, excellent reversibility of the sensor is demonstrated, which has rarely been observed in other 2D material counterparts. Such impressive features originate from the planar morphology of 2D SnS2 as well as unique physical affinity and favorable electronic band positions of this material that facilitate the NO2 physisorption and charge transfer at parts per billion levels. The 2D SnS2-based sensor provides a real solution for low-cost and selective NO2 gas sensing.
Extracted statement — Analyte: Nitrogen dioxide
Research field: Analytical Chemistry
Title: Physisorption-Based Charge Transfer in Two-Dimensional SnS2 for Selective and Reversible NO2 Gas Sensing
Abstract: Nitrogen dioxide (NO2) is a gas species that plays an important role in certain industrial, farming, and healthcare sectors. However, there are still significant challenges for NO2 sensing at low detection limits, especially in the presence of other interfering gases. The NO2 selectivity of current gas-sensing technologies is significantly traded-off with their sensitivity and reversibility as well as fabrication and operating costs. In this work, we present an important progress for selective and reversible NO2 sensing by demonstrating an economical sensing platform based on the charge transfer between physisorbed NO2 gas molecules and two-dimensional (2D) tin disulfide (SnS2) flakes at low operating temperatures. The device shows high sensitivity and superior selectivity to NO2 at operating temperatures of less than 160 °C, which are well below those of chemisorptive and ion conductive NO2 sensors with much poorer selectivity. At the same time, excellent reversibility of the sensor is demonstrated, which has rarely been observed in other 2D material counterparts. Such impressive features originate from the planar morphology of 2D SnS2 as well as unique physical affinity and favorable electronic band positions of this material that facilitate the NO2 physisorption and charge transfer at parts per billion levels. The 2D SnS2-based sensor provides a real solution for low-cost and selective NO2 gas sensing.
Extracted statement — Sensing material: Tin disulfide
Research field: Applied Physics
Title: Mechanisms of bacterial inactivation in the liquid phase induced by a remote RF cold atmospheric pressure plasma jet
Abstract: A radio-frequency atmospheric pressure argon plasma jet is used for the inactivation of bacteria (Pseudomonas aeruginosa) in solutions. The source is characterized by measurements of power dissipation, gas temperature, absolute UV irradiance as well as mass spectrometry measurements of emitted ions. The plasma-induced liquid chemistry is studied by performing liquid ion chromatography and hydrogen peroxide concentration measurements on treated distilled water samples. Additionally, a quantitative estimation of an extensive liquid chemistry induced by the plasma is made by solution kinetics calculations. The role of the different active components of the plasma is evaluated based on either measurements, as mentioned above, or estimations based on published data of measurements of those components. For the experimental conditions being considered in this work, it is shown that the bactericidal effect can be solely ascribed to plasma-induced liquid chemistry, leading to the production of stable and transient chemical species. It is shown that HNO2, ONOO − and H2O2 are present in the liquid phase in similar quantities to concentrations which are reported in the literature to cause bacterial inactivation. The importance of plasma-induced chemistry at the gas‐liquid interface is illustrated and discussed in detail. (Some figures may appear in colour only in the online journal)
Extracted statement — Intended_Application: Bacterial inactivation
Research field: Applied Physics
Title: Single-shot soft-x-ray digital holographic microscopy with an adjustable field of view and magnification
Abstract: Single-shot digital holographic microscopy with an adjustable field of view and magnification was demonstrated by using a tabletop 32.8 nm soft-x-ray laser. The holographic images were reconstructed with a two-dimensional fast-Fourier-transform algorithm, and a new configuration of imaging was developed to overcome the pixel-size limit of the recording device without reducing the effective NA. The image of an atomic-force-microscope cantilever was reconstructed with a lateral resolution of 480 nm, and the phase contrast image of a 20 nm carbon mesh foil demonstrated that profiles of sample thickness can be reconstructed with few-nanometers uncertainty. The ultrashort x-ray pulse duration combined with single-shot capability offers great advantage for flash imaging of delicate samples.
Extracted statement — Research objective: Holographic microscopy
Research field: Artificial Intelligence
Title: Neuro-Symbolic Probabilistic Argumentation Machines
Abstract: <jats:p>Neural-symbolic systems combine the strengths of neural networks and symbolic formalisms. In this paper, we introduce a neural-symbolic system which combines restricted Boltzmann machines and probabilistic semi-abstract argumentation. We propose to train networks on argument labellings explaining the data, so that any sampled data outcome is associated with an argument labelling. Argument labellings are integrated as constraints within restricted Boltzmann machines, so that the neural networks are used to learn probabilistic dependencies amongst argument labels. Given a dataset and an argumentation graph as prior knowledge, for every example/case K in the dataset, we use a so-called K-maxconsistent labelling of the graph, and an explanation of case K refers to a K-maxconsistent labelling of the given argumentation graph. The abilities of the proposed system to predict correct labellings were evaluated and compared with standard machine learning techniques. Experiments revealed that such argumentation Boltzmann machines can outperform other classification models, especially in noisy settings.</jats:p>
Extracted statement — has Input:  an argumentation graph
Research field: Artificial Intelligence
Title: How are topics born? Understanding the research dynamics preceding the emergence of new areas
Abstract: <jats:p>The ability to promptly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an <jats:italic>embryonic stage</jats:italic>, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the ‘parents’ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise.</jats:p>
Extracted statement — users: academic publishers
Research field: Artificial Intelligence
Title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer
Abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.
Extracted statement — Material: added components
Research field: Artificial Intelligence
Title: Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions
Abstract: Image-based food calorie estimation is crucial to diverse mobile applications for recording everyday meal. However, some of them need human help for calorie estimation, and even if it is automatic, food categories are often limited or images from multiple viewpoints are required. Then, it is not yet achieved to estimate food calorie with practical accuracy and estimating food calories from a food photo is an unsolved problem. Therefore, in this paper, we propose estimating food calorie from a food photo by simultaneous learning of food calories, categories, ingredients and cooking directions using deep learning. Since there exists a strong correlation between food calories and food categories, ingredients and cooking directions information in general, we expect that simultaneous training of them brings performance boosting compared to independent single training. To this end, we use a multi-task CNN [1]. In addition, in this research, we construct two kinds of datasets that is a dataset of calorie-annotated recipe collected from Japanese recipe sites on the Web and a dataset collected from an American recipe site. In this experiment, we trained multi-task and single-task CNNs. As a result, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs. For the Japanese recipe dataset, by introducing a multi-task CNN, 0.039 were improved on the correlation coefficient, while for the American recipe dataset, 0.090 were raised compared to the result by the single-task CNN.
Extracted statement — data source: American recipe site
Research field: Artificial Intelligence
Title: Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer
Abstract: With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.
Extracted statement — Process: automatic identification