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Records added in 2018
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Showing records 901 to 925 of 1025. [First] Page: 33 34 35 36 37 38 39 40 41 Last
On-Line Dynamic Data Reconciliation in Batch Suspension Polymerizations of Methyl Methacrylate
Jamille C. Coimbra, Príamo A. Melo, Diego M. Prata, José Carlos Pinto
July 31, 2018 (v1)
Keywords: Batch Process, dynamic data reconciliation, mathematical model, methyl methacrylate, parameter estimation, soft-sensor, suspension polymerization
A phenomenological model was developed to describe the dynamic evolution of the batch suspension polymerization of methyl methacrylate in terms of reactor temperature, pressure, concentrations and molecular properties of the final polymer. Then, the phenomenological model was used as a process constraint in dynamic data reconciliation procedures, which allowed for the successful monitoring of reaction variables in real-time and on-line. The obtained results indicate that heat transfer coefficients change significantly during the reaction time and from batch to batch, exerting a tremendous impact on the process operation. Obtained results also indicate that it can be difficult to attain thermodynamic equilibrium conditions in this system, because of the continuous condensation of evaporated monomer and the large mass transfer resistance offered by the viscous suspended droplets.
Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model
Andrew Sinkoe, Juergen Hahn
July 31, 2018 (v1)
Keywords: D-optimality criterion, Fisher information matrix, IL-6 signaling, optimal experimental design, parameter estimation, piecewise constant functions, sensitivity analysis
IL-6 signaling plays an important role in inflammatory processes in the body. While a number of models for IL-6 signaling are available, the parameters associated with these models vary from case to case as they are non-trivial to determine. In this study, optimal experimental design is utilized to reduce the parameter uncertainty of an IL-6 signaling model consisting of ordinary differential equations, thereby increasing the accuracy of the estimated parameter values and, potentially, the model itself. The D-optimality criterion, operating on the Fisher information matrix and, separately, on a sensitivity matrix computed from the Morris method, was used as the objective function for the optimal experimental design problem. Optimal input functions for model parameter estimation were identified by solving the optimal experimental design problem, and the resulting input functions were shown to significantly decrease parameter uncertainty in simulated experiments. Interestingly, the deter... [more]
Synthesis of Water-Soluble Group 4 Metallocene and Organotin Polyethers and Their Ability to Inhibit Cancer
Charles E. Carraher, Michael R. Roner, Jessica Frank, Alica Moric-Johnson, Lindsey C. Miller, Kendra Black, Paul Slawek, Francesca Mosca, Jeffrey D. Einkauf, Floyd Russell
July 31, 2018 (v1)
Subject: Materials
Keywords: anticancer, breast cancer, Group 4 metallocene polymers, Group 4 metallocenes, interfacial polycondensation, organotin polyethers, pancreatic cancer, poly(ethylene glycol), prostate cancer
Water-soluble metallocene and organotin-containing polyethers were synthesized employing interfacial polycondensation. The reaction involved various chain lengths of poly(ethylene glycol), and produced water-soluble polymers in decent yield. Commercially available reactants were used to allow for easy scale up. The polymers exhibited a decent ability to inhibit a range of cancer cell lines, including two pancreatic cancer cell lines. This approach should allow the synthesis of a wide variety of other water-soluble polymers.
Perspectives on Resource Recovery from Bio-Based Production Processes: From Concept to Implementation
Isuru A. Udugama, Seyed Soheil Mansouri, Aleksandar Mitic, Xavier Flores-Alsina, Krist V. Gernaey
July 31, 2018 (v1)
Keywords: bio-based production, economics, resource recovery, separation processes
Recovering valuable compounds from waste streams of bio-based production processes is in line with the circular economy paradigm, and is achievable by implementing “simple-to-use” and well-established process separation technologies. Such solutions are acceptable from industrial, economic and environmental points of view, implying relatively easy future implementation on pilot- and full-scale levels in the bio-based industry. Reviewing such technologies is therefore the focus here. Considerations about technology readiness level (TRL) and Net Present Value (NPV) are included in the review, since TRL and NPV contribute significantly to the techno-economic evaluation of future and promising process solutions. Based on the present review, a qualitative guideline for resource recovery from bio-based production processes is proposed. Finally, future approaches and perspectives toward identification and implementation of suitable resource recovery units for bio-based production processes are... [more]
A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems
Yuan Wang, Kirubakaran Velswamy, Biao Huang
July 31, 2018 (v1)
Keywords: artificial neural networks, HVAC, reinforcement learning
Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using SketchUp. Using OpenStudio, the HVAC system is installed in the office. The control schemes (ideal thermal comfort, a traditional control and the RL control) are implemented in MATLAB. Using the Building Control Virtual Test Bed (BCVTB), the control of the thermostat schedule during each sample time is implemented for the office in EnergyPlus alongside local weather data. Results from training and validation indicate that the RL controller improves thermal comfort by an average of 15% and energy effici... [more]
Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach
Kanchana Padmanabhan, Kelly Nudelman, Steve Harenberg, Gonzalo Bello, Dongwha Sohn, Katie Shpanskaya, Priyanka Tiwari Dikshit, Pallavi S. Yerramsetty, Rudolph E. Tanzi, Andrew J. Saykin, Jeffrey R. Petrella, P. Murali Doraiswamy, Nagiza F. Samatova, Alzheimer’s Disease Neuroimaging Initiative
July 31, 2018 (v1)
Subject: Biosystems
Keywords: Alzheimer’s disease, biomarker, disease prediction, pathway crosstalk
Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis... [more]
Data Visualization and Visualization-Based Fault Detection for Chemical Processes
Ray C. Wang, Michael Baldea, Thomas F. Edgar
July 31, 2018 (v1)
Keywords: data visualization, multivariate fault detection, time series data
Over the years, there has been a consistent increase in the amount of data collected by systems and processes in many different industries and fields. Simultaneously, there is a growing push towards revealing and exploiting of the information contained therein. The chemical processes industry is one such field, with high volume and high-dimensional time series data. In this paper, we present a unified overview of the application of recently-developed data visualization concepts to fault detection in the chemical industry. We consider three common types of processes and compare visualization-based fault detection performance to methods used currently.
Rheology of Green Plasticizer/Poly(vinyl chloride) Blends via Time⁻Temperature Superposition
Roya Jamarani, Hanno C. Erythropel, Daniel Burkat, James A. Nicell, Richard L. Leask, Milan Maric
July 31, 2018 (v1)
Subject: Materials
Keywords: blends, extrusion, green plasticizers, poly(vinyl chloride) (PVC), rheology, time–temperature superposition
Plasticizers are commonly added to poly(vinyl chloride) (PVC) and other brittle polymers to improve their flexibility and processing properties. Phthalate plasticizers such as di(2-ethylhexyl phthalate) (DEHP) are the most common PVC plasticizers and have recently been linked to a wide range of developmental and reproductive toxicities in mammals. Our group has developed several replacement compounds that have good biodegradation kinetics, low toxicity profiles, and comparable plasticizer properties to DEHP. Knowledge of the rheology of PVC⁻plasticizer blends at elevated temperatures is crucial for understanding and predicting the behavior of the compounds during processing. In this work, the time⁻temperature profiles of PVC blended with our replacement green plasticizers—succinates, maleates, and dibenzoates, of varying alkyl chain length—are compared to blends prepared with DEHP and a commercially available non-phthalate plasticizer, di(isononyl cyclohexane-1,2-dicarboxylate) (Hexamo... [more]
Design of Experiments for Control-Relevant Multivariable Model Identification: An Overview of Some Basic Recent Developments
Shobhit Misra, Mark Darby, Shyam Panjwani, Michael Nikolaou
July 31, 2018 (v1)
Keywords: design of experiments, integral controllability, model order, multivariable control, subspace identification
The effectiveness of model-based multivariable controllers depends on the quality of the model used. In addition to satisfying standard accuracy requirements for model structure and parameter estimates, a model to be used in a controller must also satisfy control-relevant requirements, such as integral controllability. Design of experiments (DOE), which produce data from which control-relevant models can be accurately estimated, may differ from standard DOE. The purpose of this paper is to emphasize this basic principle and to summarize some fundamental results obtained in recent years for DOE in two important cases: Accurate estimation of the order of a multivariable model and efficient identification of a model that satisfies integral controllability; both important for the design of robust model-based controllers. For both cases, we provide an overview of recent results that can be easily incorporated by the final user in related DOE. Computer simulations illustrate outcomes to be a... [more]
Effects of Inoculum Type and Aeration Flowrate on the Performance of Aerobic Granular SBRs
Mariele K. Jungles, Ángeles Val del Río, Anuska Mosquera-Corral, José Luis Campos, Ramón Méndez, Rejane H. R. Costa
July 31, 2018 (v1)
Keywords: aeration flowrate, aerobic granules, inoculum, sequencing batch reactor, Wastewater
Aerobic granular sequencing batch reactors (SBRs) are usually inoculated with activated sludge which implies sometimes long start-up periods and high solids concentrations in the effluent due to the initial wash-out of the inoculum. In this work, the use of aerobic mature granules as inoculum in order to improve the start-up period was tested, but no clear differences were observed compared to a reactor inoculated with activated sludge. The effect of the aeration rate on both physical properties of granules and reactor performance was also studied in a stable aerobic granular SBR. The increase of the aeration flow rate caused the decrease of the average diameter of the granules. This fact enhanced the COD and ammonia consumption rates due to the increase of the DO level and the aerobic fraction of the biomass. However, it provoked a loss of the nitrogen removal efficiency due to the worsening of the denitrification capacity as a consequence of a higher aerobic fraction.
Development of Molecular Distillation Based Simulation and Optimization of Refined Palm Oil Process Based on Response Surface Methodology
Noree Tehlah, Pornsiri Kaewpradit, Iqbal M. Mujtaba
July 31, 2018 (v1)
Keywords: ASPEN HYSYS, molecular distillation, Optimization, process simulation, response surface methodology
The deodorization of the refined palm oil process is simulated here using ASPEN HYSYS. In the absence of a library molecular distillation (MD) process in ASPEN HYSYS, first, a single flash vessel is considered to represent a falling film MD process which is simulated for a binary system taken from the literature and the model predictions are compared with the published work based on ASPEN PLUS and DISMOL. Second, the developed MD process is extended to simulate the deodorization process. Parameter estimation technique is used to estimate the Antoine’s parameters based on literature data to calculate the pure component vapor pressure. The model predictions are then validated against the patented results of refining edible oil rich in natural carotenes and vitamin E and simulation results were found to be in good agreement, within a ±2% error of the patented results. Third, Response Surface Methodology (RSM) is employed to develop non-linear second-order polynomial equations based model... [more]
Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing
James Moyne, Jimmy Iskandar
July 31, 2018 (v1)
Keywords: anomaly detection, Big Data, predictive analytics, predictive maintenance, process control, semiconductor manufacturing, smart manufacturing
Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predict... [more]
Principal Component Analysis of Process Datasets with Missing Values
Kristen A. Severson, Mark C. Molaro, Richard D. Braatz
July 31, 2018 (v1)
Keywords: chemometrics, Machine Learning, missing data, multivariable statistical process control, principal component analysis, process data analytics, process monitoring, Tennessee Eastman problem
Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA), which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based... [more]
On the Use of Multivariate Methods for Analysis of Data from Biological Networks
Troy Vargason, Daniel P. Howsmon, Deborah L. McGuinness, Juergen Hahn
July 31, 2018 (v1)
Keywords: autism spectrum disorder, classification, Fisher discriminant analysis, Machine Learning, Multivariate Statistics, one carbon metabolism, probability density function, transsulfuration, urine toxic metals
Data analysis used for biomedical research, particularly analysis involving metabolic or signaling pathways, is often based upon univariate statistical analysis. One common approach is to compute means and standard deviations individually for each variable or to determine where each variable falls between upper and lower bounds. Additionally, p-values are often computed to determine if there are differences between data taken from two groups. However, these approaches ignore that the collected data are often correlated in some form, which may be due to these measurements describing quantities that are connected by biological networks. Multivariate analysis approaches are more appropriate in these scenarios, as they can detect differences in datasets that the traditional univariate approaches may miss. This work presents three case studies that involve data from clinical studies of autism spectrum disorder that illustrate the need for and demonstrate the potential impact of multivariate... [more]
Reduction of Dust Emission by Monodisperse System Technology for Ammonium Nitrate Manufacturing
Maksym Skydanenko, Vsevolod Sklabinskyi, Saad Saleh, Shahzad Barghi
July 31, 2018 (v1)
Keywords: ammonium nitrate, liquid jet breakup, mathematical modeling, monodispersity, prilling
Prilling is a common process in the fertilizer industry, where the fertilizer melt is converted to droplets that fall, cool down and solidify in a countercurrent flow of air in a prilling tower. A vibratory granulator was used to investigate liquid jet breakup into droplets. The breakup of liquid jets subjected to a forced perturbation was investigated in the Rayleigh regime, where a mechanical vibration was applied in order to achieve the production of monodispersed particles. Images of the jet trajectory, breakup, and the formed drops were captured using a high-speed camera. A mathematical model for the liquid outflow conditions based on a transient two-dimensional Navier⁻Stokes equation was developed and solved analytically, and the correlations between the process parameters of the vibrator and the jet pressure that characterize their disintegration mode were identified. The theoretical predications obtained from the correlations showed a good agreement with the experimental result... [more]
Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis
Marco S. Reis, Geert Gins
July 31, 2018 (v1)
Keywords: equipment health, fault detection and diagnosis, industrial process monitoring, process health, prognosis
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos.
Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms
Jackson Udy, Brigham Hansen, Sage Maddux, Donald Petersen, Spencer Heilner, Kevin Stevens, David Lignell, John D. Hedengren
July 31, 2018 (v1)
Subject: Optimization
Keywords: EOR, history matching, recovery optimization, waterflooding, well placement
This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model v... [more]
Techno-Economic Assessment of Benzene Production from Shale Gas
Salvador I. Pérez-Uresti, Jorge M. Adrián-Mendiola, Mahmoud M. El-Halwagi, Arturo Jiménez-Gutiérrez
July 31, 2018 (v1)
Keywords: benzene, CO2 emissions, direct methane aromatization, energy integration, shale gas
The availability and low cost of shale gas has boosted its use as fuel and as a raw material to produce value-added compounds. Benzene is one of the chemicals that can be obtained from methane, and represents one of the most important compounds in the petrochemical industry. It can be synthesized via direct methane aromatization (DMA) or via indirect aromatization (using oxidative coupling of methane). DMA is a direct-conversion process, while indirect aromatization involves several stages. In this work, an economic, energy-saving, and environmental assessment for the production of benzene from shale gas using DMA as a reaction path is presented. A sensitivity analysis was conducted to observe the effect of the operating conditions on the profitability of the process. The results show that production of benzene using shale gas as feedstock can be accomplished with a high return on investment.
Stoichiometric Network Analysis of Cyanobacterial Acclimation to Photosynthesis-Associated Stresses Identifies Heterotrophic Niches
Ashley E. Beck, Hans C. Bernstein, Ross P. Carlson
July 31, 2018 (v1)
Subject: Biosystems
Keywords: cross-feeding, cyanobacteria, elementary flux mode analysis, irradiance, resource allocation, RuBisCO, stress acclimation
Metabolic acclimation to photosynthesis-associated stresses was examined in the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 using integrated computational and photobioreactor analyses. A genome-enabled metabolic model, complete with measured biomass composition, was analyzed using ecological resource allocation theory to predict and interpret metabolic acclimation to irradiance, O₂, and nutrient stresses. Reduced growth efficiency, shifts in photosystem utilization, changes in photorespiration strategies, and differing byproduct secretion patterns were predicted to occur along culturing stress gradients. These predictions were compared with photobioreactor physiological data and previously published transcriptomic data and found to be highly consistent with observations, providing a systems-based rationale for the culture phenotypes. The analysis also indicated that cyanobacterial stress acclimation strategies created niches for heterotrophic organisms and that heter... [more]
Special Issue: Water Soluble Polymers
Alexander Penlidis
July 31, 2018 (v1)
Subject: Materials
This Special Issue (SI) of Processes on water soluble polymers (WSP), and the associated Special Issue reprint, contain papers that deal with this extremely popular area of scientific investigation in polymer science and engineering, both in academic and industrial environments.[...]
Closed-Loop Characterization of Neuronal Activation Using Electrical Stimulation and Optical Imaging
Michelle L. Kuykendal, Gareth S. Guvanasen, Steve M. Potter, Martha A. Grover, Stephen P. DeWeerth
July 31, 2018 (v1)
Subject: Biosystems
Keywords: activation curve, closed-loop, dissociated culture, extracellular electrical stimulation, micro-electrode array (MEA), optical recording, strength-duration
We have developed a closed-loop, high-throughput system that applies electrical stimulation and optical recording to facilitate the rapid characterization of extracellular, stimulus-evoked neuronal activity. In our system, a microelectrode array delivers current pulses to a dissociated neuronal culture treated with a calcium-sensitive fluorescent dye; automated real-time image processing of high-speed digital video identifies the neuronal response; and an optimized search routine alters the applied stimulus to achieve a targeted response. Action potentials are detected by measuring the post-stimulus, calcium-sensitive fluorescence at the neuronal somata. The system controller performs directed searches within the strength⁻duration (SD) stimulus-parameter space to build probabilistic neuronal activation curves. This closed-loop system reduces the number of stimuli needed to estimate the activation curves when compared to the more commonly used open-loop approach. This reduction allows t... [more]
Structural Properties of Dynamic Systems Biology Models: Identifiability, Reachability, and Initial Conditions
Alejandro F Villaverde, Julio R Banga
July 31, 2018 (v1)
Keywords: controllability, differential geometry, identifiability, nonlinear systems, observability, parameter estimation, reachability
Abstract: Dynamic modelling is a powerful tool for studying biological networks. Reachability (controllability), observability, and structural identifiability are classical system-theoretic properties of dynamical models. A model is structurally identifiable if the values of its parameters can in principle be determined from observations of its outputs. If model parameters are considered as constant state variables, structural identifiability can be studied as a generalization of observability. Thus, it is possible to assess the identifiability of a nonlinear model by checking the rank of its augmented observability matrix. When such rank test is performed symbolically, the result is of general validity for almost all numerical values of the variables. However, for special cases, such as specific values of the initial conditions, the result of such test can be misleading—that is, a structurally unidentifiable model may be classified as identifiable. An augmented observability rank test... [more]
Outlier Detection in Dynamic Systems with Multiple Operating Points and Application to Improve Industrial Flare Monitoring
Shu Xu, Bo Lu, Noel Bell, Mark Nixon
July 31, 2018 (v1)
Keywords: dynamic system, flare monitoring, multiple operating points, outlier detection, PLS-DA, pruned exact linear time (PELT), time series Kalman filter (TSKF)
In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time series Kalman filter (TSKF) with the pruned exact linear time (PELT) approach to detect outliers is proposed. The proposed method outperformed benchmark methods in outlier removal performance using simulated data sets of dynamic systems with mean shifts. The method was also able to maintain the integrity of the original data set after performing outlier removal. In addition, the methodology was tested on industrial flaring data to pre-process the flare data for discriminant analysis. The industrial test case shows that performing outlier removal dramatically improves flare monitoring results thr... [more]
Special Issue “Real-Time Optimization” of Processes
Dominique Bonvin
July 31, 2018 (v1)
Subject: Optimization
Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints.[...]
Comparison of Polymer Networks Synthesized by Conventional Free Radical and RAFT Copolymerization Processes in Supercritical Carbon Dioxide
Patricia Pérez-Salinas, Gabriel Jaramillo-Soto, Alberto Rosas-Aburto, Humberto Vázquez-Torres, María Josefa Bernad-Bernad, Ángel Licea-Claverie, Eduardo Vivaldo-Lima
July 31, 2018 (v1)
Subject: Materials
Keywords: hydrogels, polymer network homogeneity, RAFT polymerization, solubility in supercritical fluids, supercritical carbon dioxide
There is a debate in the literature on whether or not polymer networks synthesized by reversible deactivation radical polymerization (RDRP) processes, such as reversible addition-fragmentation radical transfer (RAFT) copolymerization of vinyl/divinyl monomers, are less heterogeneous than those synthesized by conventional free radical copolymerization (FRP). In this contribution, the syntheses by FRP and RAFT of hydrogels based on 2-hydroxyethylene methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA) in supercritical carbon dioxide (scCO₂), using Krytox 157 FSL as the dispersing agent, and the properties of the materials produced, are compared. The materials were characterized by differential scanning calorimetry (DSC), swelling index (SI), infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Studies on ciprofloxacin loading and release rate from hydrogels were also carried out. The combined results show that the hydrogels synthesized by FRP and RAFT are signif... [more]
Showing records 901 to 925 of 1025. [First] Page: 33 34 35 36 37 38 39 40 41 Last
Change year: 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024
Filter by month: June | July | August | September | October | November | December