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Search results for: deep eutectic solvent
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Management of Dark Fermentation Broth via Bio Refining and Photo Fermentation
PublicationLignocellulose and starch-based raw materials are often applied in the investigations regarding biohydrogen generation using dark fermentation. Management of the arising post-fermentation broth becomes a problem. The Authors proposed sequential processes, to improve the efficiency of both hydrogen generation and by-products management carried under model conditions. During the proposed procedure, the simple sugars remaining in...
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Application of Aqueous Biphasic Systems Extraction in Various Biomolecules Separation and Purification: Advancements Brought by Quaternary Systems
PublicationAqueous biphasic systems (ABS) extraction is a simple, selective, efficient and easy to scale-up technology that, over the years, has attracted a considerable attention from the researcher community as an alternative methodology in downstream processing of a wide variety of biomolecules. This review summarizes and discusses the fundamental features of ABS, as well as its advantages and disadvantages, as a separation and purification...
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Performance tuning of chitosan-based membranes by protonated 2-Pyrrolidone-5-carboxylic acid-sulfolane DES for effective water/ethanol separation by pervaporation
PublicationToday, the applicability of deep eutectic solvents (DES) in various fields, including membrane science and technology, is extensively investigated. In pioneering works, we have implemented different DES as a component of chitosan (CS)-based flat membranes for pervaporation (PV) separation. Herein, we present a new protonated (by sulphuric acid) 2-Pyrrolidone-5-carboxylic acid: sulfolane DES, as a green additive for its chemical...
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Deep Learning
PublicationDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Removal of sulfur from a solvent extract
PublicationThe presence of sulfur in a solvent extract that is to be analyzed chromatographically impairs and sometimes precludes properinterpretation of the chromatogram, as the sulfur peak masks the peaks of other compounds present in the sample, and sulfur alsodisrupts the operation of the mass detector. This means that discrepancies in the results can lead to erroneous interpretations andfalse assessments of environmental risk. For this...
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Neural networks and deep learning
PublicationIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
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A Closed Bipolar Electrochemical Cell for the Interrogation of BDD Single Particles: Electrochemical Advanced Oxidation
PublicationA closed bipolar electrochemical cell containing two conductive boron-doped diamond (BDD) particles of size 250 – 350 m, produced by high-pressure high-temperature (HPHT) synthesis, has been used to demonstrate the applicability of single BDD particles for electrochemical oxidative degradation of the dye, methylene blue (MB). The cell is fabricated using stereolithography 3D printing and the BDD particles are located at either...
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Experimental tests of reinforced concrete deep-beams
PublicationThe paper presents results of experimental research of the reinforced concrete deep beam with a spatial arrangement. Tested structural elements consist of the cantilever deep beam loaded on the height and transverse deep beam with hanging on it another one. The analysis includes crack morphology, effort of steel and load distribution. The article verified effectiveness of two different kind of reinforcement in both tested deep...
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Medium-sized cyclic bis(anisylphosphonothioyl)- disulfanes and their corresponding cyclic sulfane-structures and most characteristic reactions
PublicationCyclic 8-, 9-, 10-, and 12-membered bis(anisylphosphonothioyl)disulfanes were synthesized. Next, structurally related 7 to 9-membered cis and trans sulfanes were isolated as a result of sulfur atom extrusion from the parent cyclic disulfanes. The results of the desulfurization of the disulfanes were compared to the results obtained for desulfurization of the respective bis(anisylphosphodithioates). Cyclic disulfanes predominantly...
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EXPERIMENTAL AND THEORETICAL FLOW OF THE FORCES IN DEEP BEAMS WITH CANTILEVAR
PublicationThis article presents the results of experimental research carried out on deep beams with cantilever which was loaded throughout the depth. The main deep beam was directly simply supported on the one side. On the other side the deep beam was suspended in another deep member situated at right angles. All deep beams created a spatial arrangement. The paper is focused on the analysis of the cracks morphology and flow of the internal...
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A solvent selection guide based on chemometrics and multicriteria decision analysis
PublicationThe selection of suitable solvents is a crucially important subject in a wide range of chemical processes. This study presents a solvent selection guide where 151 solvents were assessed, including a significant number of recently reported bio-based solvents. The assessment procedure involves grouping of solvents according to their physicochemical parameters and ranking within clusters according to their toxicological and hazard...
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Deep learning in the fog
PublicationIn the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high...
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Minimal parameter implicit solvent model for ab initioelectronic-structure calculations
PublicationAbstract - We present an implicit solvent model for ab initio electronic-structure calculations which is fully self-consistent and is based on direct solution of the nonhomogeneous Poisson equation. The solute cavity is naturally defined in terms of an isosurface of the electronic density according to the formula of Fattebert and Gygi (J. Comput. Chem., 23 (2002) 662). While this model depends on only two parameters, we demonstrate...
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Headspace Solvent Microextraction
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Diethyl carbonate as a green extraction solvent for chlorophenol determination with dispersive liquid–liquid microextraction
PublicationThe principles of green analytical chemistry indicate that the search for greener organic solvents for extraction applications is crucial. In this study diethyl carbonate (DEC) is proved to be a green solvent, as it is relatively nontoxic, obtainable from renewable resources and biodegradable. Here it is applied as an extraction solvent for chlorophenol determination in water samples with dispersive liquid–liquid microextraction....
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Removal of organic compounds from water by solvent sublation. Criteria for solvent selection.
PublicationProces flotoekstrakcji zastosowano do usuwania z wody następujących związków organicznych: tetrachlorku węgla, chloroformu, 1,3,5-trimetylobenzenu (mezytylenu), fenolu i 2,4,5-trichlorofenolu. Jako odbierające fazy organiczne użyto: olej mineralny, 1-dekanol, 1-oktanol, octan amylu, dodekan i tetradekan. Mechanizmem decydującym o usuwaniu lotnych związków organicznych (VOC) było odparowanie do wnętrza pęcherzyków gazu, podczas...
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Discussion:Horizontal stress increase induced by deep vibratory compaction
PublicationDeep compaction control of granular material using the results of field tests. The analysis include the CPTU and DMT tests terformed before and after compaction works.
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Deep compaction control of sandy soils
PublicationVibroflotation, vibratory compaction, micro-blasting or heavy tamping are typical improvement methods for the cohesionless deposits of high thickness. The complex mechanism of deep soil compaction is related to void ratio decrease with grain rearrangements, lateral stress increase, prestressing effect of certain number of load cycles, water pressure dissipation, aging and other effects. Calibration chamber based interpretation...
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Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures
PublicationDapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS:...
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Morphology control via dual solvent crystallization for high-mobility functionalized pentacene-blend thin film transistors
PublicationWe present an approach to improving the performance of solution processed organic semiconductor transistors based on a dual solvent system. We here apply this to a blend containing the π-conjugated small molecule 6,13 bis(triisopropylsilylethynyl) pentacene (TIPS-pentacene) and polystyrene, which acts as an inert binder. Using a semiconductor-binder solution of two solvents, where the main solvent is a better solvent of the small...
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Breast MRI segmentation by deep learning: key gaps and challenges
PublicationBreast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature...
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Deep Learning: A Case Study for Image Recognition Using Transfer Learning
PublicationDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Solvent Exchange around Aqueous Zn(II) from Ab Initio Molecular Dynamics Simulations
PublicationHydrated zinc(II) cations, due to their importance in biological systems, are the subject of ongoing research concerning their hydration shell structure and dynamics. Here, ab initio molecular dynamics (AIMD) simulations are used to study solvent exchange events around aqueous Zn2+, for which observation in detail is possible owing to the considerable length of the generated trajectory. While the hexacoordinated Zn(H2O)62+ is the...
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Bio‐polyols synthesized by liquefaction of cellulose: Influence of liquefaction solvent molecular weight
PublicationCurrently, the plastics industry including polyurethanes is based on the use of petrochemicals. For this reason, scientists are looking for new types of renewable resources for the substitution of petrochemical substances. This work aims to evaluate the effect of polyethylene glycols (PEG) with different molecular mass impact on properties of bio-based polyols synthesized via biomass liquefaction of cellulose. To date, research...
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The influence of reinforcement on load carrying capacity and cracking of the reinforced concrete deep beam joint
PublicationThe paper presents the results of experimental research of the spatial reinforced concrete deep beam systems orthogonally reinforced and with additional inclined bars. Joint of the deep beams in this research was composed of the longitudinal deep beam with a cantilever suspended at the transversal deep beam. The cantilever deep beam was loaded throughout the depth and the transversal deep beam was loaded at the mid-span by longitudinal...
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Unique agreement of experimental and computational infrared spectroscopy: a case study of lithium bromide solvation in an important electrochemical solvent
PublicationInfrared (IR) spectroscopy is a widely used and invaluable tool in the studies of solvation phenomena in electrolyte solutions. Using state-of-the-art chemometric analysis of a spectral series measured in a concentration-dependent manner, the spectrum of the solute-affected solvent can be extracted, providing a detailed view of the structural and energetic states of the solvent molecules influenced by the solute. Concurrently,...
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Volatile Organohalogen Compounds Determination with Dispersive Liquid-Liquid Microextraction Applying Solvent Lighter than Water
PublicationBackground: Volatile organohalogen compounds (VOX) are a group of environmental pollutants that risk the quality of drinking water. These compounds are characterized by certain acute and chronic toxicities, possessing mutagenic properties. Because of these potential risks factors that these compounds cause, the maximum allowable concentrations (MACs) have been established by respective organizations. The abundance of VOX, their...
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The porosity and morphology of PU foams prepared by solvent casting/salt leaching method with different solvents
PublicationIn this study, the polyurethane (PU) system based on poly(ethylene-butylene) adipate diol, 1,6-hexamethylene diisocyanate, 1,4-butanediol and ascorbic acid is used to prepare a foamed material. Polymer foams were created using solvent casting/salt-particle leaching (SC/PL) method. The influence of the PU concentration in different solutions [either in a DMF or in DMF with THF as a co-solvent] on the morphology and porosity of the...
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Comparison of Deep Learning Approaches in Classification of Glacial Landforms
PublicationGlacial landforms, created by the continuous movements of glaciers over millennia, are crucial topics in geomorphological research. Their systematic analysis affords invaluable insights into past climatic oscillations and augments understanding of long-term climate change dynamics. The classification of these types of terrain traditionally depends on labor-intensive manual or semi-automated methods. However, the emergence of automated...
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Deep learning for recommending subscription-limited documents
PublicationDocuments recommendation for a commercial, subscription-based online platform is important due to the difficulty in navigation through a large volume and diversity of content available to clients. However, this is also a challenging task due to the number of new documents added every day and decreasing relevance of older contents. To solve this problem, we propose deep neural network architecture that combines autoencoder with...
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Fabrication of Composite Polyurethane/Hydroxyapatite Scaffolds Using Solvent-Casting Salt Leaching Technique
PublicationScaffolds are porous three-dimensional structures which are used to fill bone losses and make them possible to cells to grow. Many different structural and biological properties are required from them: porosity, mechanical strength and biocompability. The present research is aimed at development of composite polyurethane/hydroxyapatite scaffolds by using the solvent-casting salt leaching method. The SEM examinations were applied...
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Predicting sulfanilamide solubility in the binary mixtures using a reference solvent approach
PublicationBackground. Solubility is a fundamental physicochemical property of active pharmaceutical ingredients. The optimization of a dissolution medium aims not only to increase solubility and other aspects are to be included such as environmental impact, toxicity degree, availability, and costs. Obtaining comprehensive...
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Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks
PublicationDeep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturba-tions are unnoticeable for humans. Such attacks...
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublicationThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublicationIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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SYNTHETIZED MEMBRANES FOR ULTRASOUND-ASSISTED SOLVENT EXTRACTION OF POROUS MEMBRANE PACKED SOLID SAMPLES.
PublicationMembranes are becoming more and more popular in analytical chemistry, which is why they are used, among others, in extraction processes. Therefore, this work focuses on the process of synthesis PVDF membranes and its optimization. The obtained membranes were used as bags for the phthalate extraction in disposable diapers for babies. Extraction was accomplished by method ultrasound-assisted solvent extraction of porous PVDF membrane...
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Data augmentation for improving deep learning in image classification problem
PublicationThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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The impact of the AC922 Architecture on Performance of Deep Neural Network Training
PublicationPractical deep learning applications require more and more computing power. New computing architectures emerge, specifically designed for the artificial intelligence applications, including the IBM Power System AC922. In this paper we confront an AC922 (8335-GTG) server equipped with 4 NVIDIA Volta V100 GPUs with selected deep neural network training applications, including four convolutional and one recurrent model. We report...
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Classifying Emotions in Film Music - A Deep Learning Approach
PublicationThe paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the...
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Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublicationIn recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
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Outlier detection method by using deep neural networks
PublicationDetecting outliers in the data set is quite important for building effective predictive models. Consistent prediction can not be made through models created with data sets containing outliers, or robust models can not be created. In such cases, it may be possible to exclude observations that are determined to be outlier from the data set, or to assign less weight to these points of observation than to other points of observation....
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Radiative lifetime of a BODIPY dye as calculated by TDDFT and EOM-CCSD methods: solvent and vibronic effects
PublicationThe radiative emission lifetime and associated S1 excited state properties of a BODIPY dye are investigated with TDDFT and EOM-CCSD calculations. The effects of a solvent are described with the polarizable continuum model using the linear response (LR) approach as well as state-specific methods. The Franck–Condon (FC), Herzberg–Teller (HT) and Duschinsky vibronic effects are evaluated for the absorption and emission spectra, and...
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublicationBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublicationIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Force transfer and stress distribution in short cantilever deep beams loaded throughout the depth with a various reinforcement
PublicationDeep beams used as the main reinforced concrete structural elements which taking over the load and stiffening construction are often found in high-rise buildings. The architecture of these buildings is sometimes sophisticated and varied, arouse the admiration of the majority of recipients, and thus causing an engineering challenge to correctly design the structural system and force transfer. In such structures is important to shape...
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Statistical evaluation of the changes in cellulose properties caused by the stepwise solvent exchange and esterification
PublicationThe objective of the research was to empirically confirm the changes in cellulose reactivity caused by the pre-treatment with solvents of different polarity. Therefore, 5 solvents varying in their polar component of surface tension from 0 to 4.6 mN/m were chosen. Their impact on the biopolymer properties was carefully analysed concerning chemical structure, crystallinity and surface characteristics. It was revealed that the length...
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Application of multicriteria decision analysis in solvent type optimization for chlorophenols determination with a dispersive liquid–liquid microextraction
PublicationThis study presents a novel support tool for the optimization and development of analytical methods. The tool is based on multi-criteria decision analysis (MCDA), namely the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), that allows users to rank possible solutions according to their requirements. In this study, we performed rankings of pairs of eight extraction and three dispersive solvents used...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublicationThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Finding the Right Solvent: A Novel Screening Protocol for Identifying Environmentally Friendly and Cost-Effective Options for Benzenesulfonamide
PublicationThis study investigated the solubility of benzenesulfonamide (BSA) as a model compound using experimental and computational methods. New experimental solubility data were collected in the solvents DMSO, DMF, 4FM, and their binary mixtures with water. The predictive model was constructed based on the best-performing regression models trained on available experimental data, and their hyperparameters were optimized using a newly...