Relevant bibliographies by topics / Solubility prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Solubility prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Solubility prediction"

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Bhandari, Bikash K., Paul P. Gardner, and Chun Shen Lim. "Solubility-Weighted Index: fast and accurate prediction of protein solubility." Bioinformatics 36, no. 18 (2020): 4691–98. http://dx.doi.org/10.1093/bioinformatics/btaa578.

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Abstract Motivation Recombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified. Results We have discovered that global structural flexibility, which can be modeled by normalized B-factors, accurately predicts the solubility of 12 216 recombinant proteins expressed in Escherichia coli. We have optimized these B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted In
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Jouyban, Abolghasem. "Review of the Cosolvency Models for Predicting Drug Solubility in Solvent Mixtures: An Update." Journal of Pharmacy & Pharmaceutical Sciences 22 (September 23, 2019): 466–85. http://dx.doi.org/10.18433/jpps30611.

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The cosolvency models frequently used in solubility data modeling of drugs in mixed solvents were reviewed and their accuracies for calculating the solubility of solutes were briefly discussed. The models could be used either for correlation of the generated solubility data with temperature, solvent composition etc or for prediction of unmeasured solubility data using interpolation/extrapolation technique. Concerning the correlation results employing a given number of independent variables, the accuracies of the investigated models were comparable, since they could be converted to a single mat
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Hewitt, M., M. T. D. Cronin, S. J. Enoch, J. C. Madden, D. W. Roberts, and J. C. Dearden. "In Silico Prediction of Aqueous Solubility: The Solubility Challenge." Journal of Chemical Information and Modeling 49, no. 11 (2009): 2572–87. http://dx.doi.org/10.1021/ci900286s.

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Parsafar, Gholamabbas, and Ezat Keshavarzi. "Solubility Prediction Using Statistical Mechanics." Bulletin of the Chemical Society of Japan 73, no. 11 (2000): 2445–52. http://dx.doi.org/10.1246/bcsj.73.2445.

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Ran, Yingqing, and Samuel H. Yalkowsky. "Prediction of Drug Solubility by the General Solubility Equation (GSE)." Journal of Chemical Information and Computer Sciences 41, no. 2 (2001): 354–57. http://dx.doi.org/10.1021/ci000338c.

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Jouyban, Abolghasem. "Review of the cosolvency models for predicting solubility of drugs in water-cosolvent mixtures." Journal of Pharmacy & Pharmaceutical Sciences 11, no. 1 (2008): 32. http://dx.doi.org/10.18433/j3pp4k.

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The cosolvency models presented from 1960 to 2007 were reviewed and their accuracies for correlating and/or predicting the solubility of drugs in water-cosolvent mixtures were discussed. The cosolvency models could be divided into theoretical, semi-empirical and empirical models, the first group of models provide basic information from the solution, while the last group of models are good suitable for solubility correlation studies. The simplest cosolvency model, i.e. the log-linear model of Yalkowsky, provides an estimate of drug solubility in water-cosolvent mixtures using aqueous solubility
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Wang, Junmei, and Tingjun Hou. "Recent Advances on Aqueous Solubility Prediction." Combinatorial Chemistry & High Throughput Screening 14, no. 5 (2011): 328–38. http://dx.doi.org/10.2174/138620711795508331.

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Jorgensen, William L., and Erin M. Duffy. "Prediction of drug solubility from structure." Advanced Drug Delivery Reviews 54, no. 3 (2002): 355–66. http://dx.doi.org/10.1016/s0169-409x(02)00008-x.

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Agostini, Federico, Michele Vendruscolo, and Gian Gaetano Tartaglia. "Sequence-Based Prediction of Protein Solubility." Journal of Molecular Biology 421, no. 2-3 (2012): 237–41. http://dx.doi.org/10.1016/j.jmb.2011.12.005.

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Jain, Parijat, and Samuel H. Yalkowsky. "Prediction of aqueous solubility from SCRATCH." International Journal of Pharmaceutics 385, no. 1-2 (2010): 1–5. http://dx.doi.org/10.1016/j.ijpharm.2009.10.003.

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Dissertations / Theses on the topic "Solubility prediction"

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Jain, Parijat. "Prediction of aqueous solubility from SCRATCH." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/193517.

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Several methods have been proposed for the prediction of aqueous solubility. This study proposes the SCRATCH model for the aqueous solubility estimation of a compound directly from its structure. The algorithm utilizes predicted melting points and predicted aqueous activity coefficients for the solubility estimation, reflecting the truly predictive nature of the model. It uses two additive, constitutive molecular descriptors (enthalpy of melting and aqueous activity coefficient) and two non-additive molecular descriptors (symmetry and flexibility). The melting point prediction is trained o
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Yang, Gang. "Aqueous solubility prediction of organic compounds." Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/298795.

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Aqueous solubility is one of the most important physical properties to consider in drug discovery and development. Drug candidates with poor solubility often have poor bioavailability, which leads to increased developmental cost and efforts. Therefore, there is a strong trend to perform solubility screening of drug candidates as early as possible in the drug discovery and development process. While experimental methods are being developed to increase the throughput of solubility measurement, the development of aqueous solubility prediction methods can be a powerful complementary tool. This dis
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Ochsner, Allison Becker. "Solubility prediction in nonideal liquid mixtures /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487260859494571.

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Aldhubiab, Bandar Essa. "Prediction of Melting Point Lowering in Eutectic Mixtures." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/195415.

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Three solution models: ideal, regular, and quasi- regular, were used to predict the melting point of eutectic mixtures containing Polyethylene Glycol (PEG) 400 and PEG 4000 with nine poorly water- soluble drugs: 1-naphthoic acid, estrone, griseofulvin, indomethacin, phenobarbital, paracetamol, salicylic acid, salicylamide and naproxen. PEG 400 was physically mixed with drug at different weight percentages to determine the melting points of the pure drugs and the melting point depression using Differential Scanning Calorimetry (DSC). The PEG 4000 eutectic mixtures were processed by the solv
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Zvaigzne, Anita Ilze. "Thermochemical investigations of crystalline solutes in non-electrolyte solutions: Mathematical representation of solubility data and the development of predictive solubility equations in systems with specific and non-specific interactions." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc28369/.

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Understanding the thermodynamic properties of multicomponent mixtures is of critical importance in many chemical and industrial applications. Experimental measurements become progressively difficult as the number of solution components increases -- producing the need for predictive models. Problems in development of predictive models arise if the mixture has one or more components that interact through molecular complexation or association. Experimental solubilities of anthracene and pyrene dissolved in binary systems containing one or more alcohols were measured in order to address this prob
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Bergström, Christel A. S. "Computational and Experimental Models for the Prediction of Intestinal Drug Solubility and Absorption." Doctoral thesis, Uppsala University, Department of Pharmacy, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3593.

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<p>New effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the number of new drugs undergoing clinical trial has not augmented at the same pace, which in part has been attributed to poor absorption of the compounds.</p><p>The main objective of this thesis was to investigate whether computer-based models devised from calculated molecular descriptors can be used to predict aqueous drug solubility, an important property influencing the absorption process. For this purpose, bot
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Patel, Raj B., and Raj B. Patel. "Prediction of Human Intestinal Absorption." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624487.

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The proposed human intestinal absorption prediction model is applied to over 900 pharmaceuticals and has about 82.5% true prediction power. This study will provide a screening tool that can differentiate well absorbed and poorly absorbed drugs in the early stage of drug discovery and development. This model is based on fundamental physicochemical properties and can be applied to virtual compounds. The maximum well-absorbed dose (i.e., the maximum dose that will be more than 50 percent absorbed) calculated using this model can be utilized as a guideline for drug design, synthesis, and pre-clini
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Norrsjö, Viktor. "Prediction of compound solubility in Dimethyl sulfoxide using machinelearning methods including graph neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283113.

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In drug discovery, compounds that are insoluble in Dimethyl sulfoxide (DMSO) are not wanted and can be disregarded. To avoid wasting time and resources pharmaceutical companies are trying to predict compound solubility before selecting compounds for further research. Compound solubility is hard to predict with confidence and this project focus on prediction using machine learning methods. The used dataset consists of almost 12 thousand compounds label soluble or insoluble and is very label biased towards soluble compounds. Different ways of representing compounds are tested with the four machi
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Lewandowski, Jason A. "Modified UNIFAC-LLE Group-Interaction Parameters for the Prediction of Gasoline-Ethanol-Water Equilibria." Digital WPI, 2008. https://digitalcommons.wpi.edu/etd-theses/461.

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Gasoline spills are sources of groundwater contamination. In the event of a spill, timely remediation efforts can advert most of the potential groundwater contamination due to the immiscibility of gasoline in water. Ethanol functions as a cosolvent that can increase the solubility of gasoline in water. Therefore, the risk of groundwater contamination in the event of a fuel spill increases as the ethanol content in automobile fuels increases. This study examines the effect fuel spill size and ethanol content has on the quantities of toluene, ethylbenzene, m-xylene and o-xylene (TEMO) that disso
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Carballo, Amador Manuel. "Altering the solubility of recombinant proteins through modification of surface features." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/altering-the-solubility-of-recombinant-proteins-through-modification-of-surface-features(a2a7e7d5-3cc5-4f0c-924e-61bb817c0f3e).html.

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Protein solubility plays an important role whether for biophysical and structural studies, or for production and delivery of therapeutic proteins. Poor solubility could lead to protein aggregation, which is an undesired physicochemical mechanism at any stage of recombinant proteins production. To date, more than half of all recombinant therapeutic proteins are produced in mammalian cells, mainly due to the high similarity of the final product to human protein structures. However, poor secretion can occur, due to misfolded proteins or aggregates leading to cellular stress and proteolysis. Anoth
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Books on the topic "Solubility prediction"

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Dressman, J. B., and C. Reppas. Oral drug absorption: Prediction and assessment. 2nd ed. Informa Healthcare USA, 2010.

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Banakar, Umesh V. Drug dissolution and bioavailability: Critical considerations including simulations and predictions : 3-day seminar. [Technomic Pub. Co], 1994.

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Gerrard, W. Solubility of Gases and Liquids: A Graphic Approach Data - Causes - Prediction. Springer, 2013.

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Nirmalakhandan, Nagamany N. Prediction of aqueous solubility of organic chemicals based on molecular structure. American Chemical Society, 1989.

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B, Dressman J., and Lennernäs Hans, eds. Oral drug absorption: Prediction and assessment. Marcel Dekker, 2000.

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Book chapters on the topic "Solubility prediction"

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Hardacre, Christopher, Johan Jacquemin, Norfaizah Ab Manan, David W. Rooney, and Tristan G. A. Youngs. "Prediction of Gas Solubility using COSMOthermX." In ACS Symposium Series. American Chemical Society, 2009. http://dx.doi.org/10.1021/bk-2009-1030.ch024.

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Yalkowsky, S. H. "Estimation of the Aqueous Solubility of Organic Compounds." In Physical Property Prediction in Organic Chemistry. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_27.

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Dannenfelser, R. M., and S. H. Yalkowsky. "The Arizona Database: An Aqueous Solubility Database for Nonelectrolytes." In Physical Property Prediction in Organic Chemistry. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_29.

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Abramov, Yuriy A., and Klimentina Pencheva. "Thermodynamics and Relative Solubility Prediction of Polymorphic Systems." In Chemical Engineering in the Pharmaceutical Industry. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882221.ch25.

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Abramov, Yuriy A., and Klimentina Pencheva. "THERMODYNAMICS AND RELATIVE SOLUBILITY PREDICTION OF POLYMORPHIC SYSTEMS." In Chemical Engineering in the Pharmaceutical Industry. John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119600800.ch22.

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Raevsky, O. A., E. P. Trepalina, and S. V. Trepalin. "SLIPPER — A New Program for Water Solubility, Lipophilicity and Permeability Prediction." In Molecular Modeling and Prediction of Bioactivity. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_131.

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Cano, Gaspar, José García-Rodríguez, Sergio Orts-Escolano, et al. "Support Vector Machine Prediction of Drug Solubility on GPUs." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16480-9_62.

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Cunico, Larissa P., Anjan K. Tula, Roberta Ceriani, and Rafiqul Gani. "Modeling and Prediction of Solid Solubility by GE Models." In Computational Pharmaceutical Solid State Chemistry. John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118700686.ch10.

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Kansy, Manfred, Krystyna Kratzat, Isabelle Parrilla, Frank Senner, and Björn Wagner. "Physicochemical High Throughput Screening (pC-HTS): Determination of Membrane Permeability, Partitioning and Solubility." In Molecular Modeling and Prediction of Bioactivity. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_28.

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Rohbock, E. "Water Solubility of Heavy Metals in Deposition Samples-Interpretation and Prediction of Bioavailability." In Atmospheric Pollutants in Forest Areas. Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4736-8_17.

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Conference papers on the topic "Solubility prediction"

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Samak, Taghrid, Dan Gunter, and Zhong Wang. "Prediction of protein solubility in E. coli." In 2012 IEEE 8th International Conference on E-Science (e-Science). IEEE, 2012. http://dx.doi.org/10.1109/escience.2012.6404416.

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Nichols, David, Neil Goodwin, Gordon Graham, and Dario Frigo. "Scale Prediction and Mineral Solubility Under HPHT Conditions." In SPE International Conference on Oilfield Chemistry. Society of Petroleum Engineers, 2019. http://dx.doi.org/10.2118/193564-ms.

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Munjal, Nupur S., Narendra Kumar, Manu Sharma, and Chittaranjan Rout. "QSAR model development for solubility prediction of Paclitaxel." In 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552139.

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Toropova, Alla, Andrey Toropov, and Emilio Benfenati. "Idealized correlations: prediction of solubility of fullerene in organic solvents." In MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition. MDPI, 2018. http://dx.doi.org/10.3390/mol2net-04-05898.

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Pushkarova, Yaroslava, Valentina Panchenko, and Yuriy Kholin. "Application an Artificial Neural Network for Prediction of Substances Solubility." In IEEE EUROCON 2021 - 19th International Conference on Smart Technologies. IEEE, 2021. http://dx.doi.org/10.1109/eurocon52738.2021.9535593.

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Singh, P., S. Gharfeh, and D. Blumer. "A Comprehensive Solubility Model for Asphaltene-Flocculation Prediction at Field Conditions." In Offshore Technology Conference. Offshore Technology Conference, 2006. http://dx.doi.org/10.4043/18317-ms.

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Cheung, Mars, Stephen Johnson, David Hecht, and Gary B. Fogel. "Quantitative structure-property relationships for drug solubility prediction using evolved neural networks." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4630870.

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Goodwin, Neil, Mark May, Sam Wilson, and Gordon Graham. "Mineral Solubility Measurement and Prediction Under High Pressure, High Temperature Reservoir Conditions." In SPE International Conference and Exhibition on Formation Damage Control. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/199285-ms.

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Bahadori, A., H. B. Vuthaluru, and M. O. Tade. "A Novel Method for Prediction of Silica Carry-Over and Solubility in Steam." In Canadian International Petroleum Conference. Petroleum Society of Canada, 2009. http://dx.doi.org/10.2118/2009-010.

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Machin, Laura, and Luis Alberto Torres-Gomez. "Prediction of pH-dependent aqueous solubility of druglike molecules of different chemical behavior." In MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition. MDPI, 2018. http://dx.doi.org/10.3390/mol2net-04-05308.

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Reports on the topic "Solubility prediction"

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Criscenti, L. J., R. J. Serne, K. M. Krupka, and M. I. Wood. Predictive calculations to assess the long-term effect of cementitious materials on the pH and solubility of uranium(VI) in a shallow land disposal environment. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/374121.

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Felmy, Andrew R., Gregory Choppin, David A. Dixon, and James A. Campbell. Development of Fundamental Data on Chemical Speciation and Solubility for Strontium and Americium in High Level Waste: Predictive Modeling of Phase Partitioning During Tank Processing. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/825790.

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Felmy, Andrew R., Gregory Choppin, and David A. Dixon. Development of Fundamental Data on Chemical Speciation and Solubility for Strontium and Americium in High- Level Waste: Predictive Modeling of Phase Partitioning During Tank Processing. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/833733.

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Felmy, Andrew R., Gregory Choppin, and David A. Dixon. Development of Fundamental Data on Chemical Speciation and Solubility for Strontium and Americium in High-Level Waste: Predictive Modeling of Phase Partitioning During Tank Processing. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/833734.

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