1,720,978 research outputs found
Explainable AI for understanding solute rejection in organic solvent nanofiltration
No full textChemical separations account for more than 15% of the total energy usage globally. Membrane-based separations have been proposed as an alternative for these energy intensive separations in the oil, pharmaceutical, food, water and fine chemical industries. Currently, membrane process performance cannot be predicted or calculated due to the complex molecular interactions.
We are focusing on the understanding of the structure-property relationships in membrane separations. Eventually, our aim is to develop better membrane materials and processes to ease cost and energy in the chemical industry
Chemical Diversity and Machine Learning in Organic Solvent Nanofiltration
No full textThe aim of the dissertation to study small organic solute rejection in organic solvent nanofiltration using cheminformatics and machine learning. Chemically diverse datasets were curated using a novel medium high-throughput methodology and literature data extraction. These datasets contained thousands of datapoints of small organic solute rejections and process parameters. Different chemical fingerprinting has been used for feature generation, such as molecular descriptors, Morgan fingerprints, and latent-vector representation. These features were used to train different machine learning models, such as graph neural networks, partial least squares regression, and boosting tree algorithms. The obtained models were used to predict small organic solute rejection for nanofiltration related applications. Correlation between rejection and the molecular descriptors have been studied to deepen the understanding of transport and rejection through polymeric membranes. Explainable artificial intelligence concept was used to study the effect of solute, solvent and membrane structure on solute rejection. The conclusion of the dissertation highlights the importance of the chemical structure effect in nanofiltration and provides a future perspective on data-driven approached for nanofiltration and organic solvent nanofiltration
Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration
No full textMethods for determining solute rejection in organic solvent nanofiltration (OSN) are time-consuming and expensive and still rely on wet-lab measurements, resulting in the slow development of membrane processes. OSN, similar to other membrane technologies, requires precise and comprehensive predictive models that can function on various solutes, membranes, and solvents. We present two prediction methods based on the quantitative structure–activity relationship (QSAR) using traditional machine learning (ML) and deep learning (DL) models. The partial least-squares regression model combined with the variable importance in projection and genetic algorithm achieves a slightly lower root-mean-square error score (8.04) than the DL-based graph neural network (10.40). For the first time, we visualize the effect of different solute functional groups on rejection, providing a new platform for a more in-depth investigation into the membrane–solute interactions, potentially enabling the design of membranes with improved selectivity. Our ML model is freely accessible on the OSN database website (www.osndatabase.com) for everyone.The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Synthesis of covalent organic frameworks using sustainable solvents and machine learning
No full textCovalent organic frameworks (COFs) have attracted considerable interest owing to their structural predesign ability, controllable chemistry, long-range periodicity, and pore interior functionalization ability. The most widely adopted solvothermal synthesis of COFs requires the use of toxic organic solvents. In line with the 5th principle of green chemistry and the United Nations’ 12th sustainable development goal, we aim to mitigate the adverse effect of solvents on COF synthesis. Here we have investigated twelve green solvents for the sustainable synthesis of five series of COFs using the solvothermal approach. Crystallinity and porosity were used to assess the quality of the obtained COFs. In addition, the suitability of the solvents in the synthesis of crystalline and porous COFs was investigated and color-coded for the final green assessment. In particular, γ–butyrolactone (for TpPa, TpBD, and TpAzo); para–cymene (TpAnq); and PolarClean (TpTab) were found to be excellent green solvents to produce high-quality COFs. For the first time, we successfully used quantitative structure property relationships in combination with machine learning approaches to predict both the surface area and crystallinity of the COFs by utilizing the structure of the solvents and COF building blocksThis work was supported by King Abdullah University of Science and Technology (KAUST). The postdoctoral (SK) and PhD (GI) fellowships from the Advanced Membranes and Porous Materials Center at KAUST are gratefully acknowledged
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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