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Journal articles on the topic 'Physicochemical model'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

NITYANANDA, SAHA, and MISRA ARUNABHA. "Synthesis, Characterisation and Coordinating Properties of a New Benzimidazolylpyrazole : Cobalt(II), Nickel(II) and Copper(II) Complexes of 5-Methyl-3-(2'-benzimidazolyl)pyrazole." Journal of Indian Chemical Society Vol. 70, Nov-Dec 1993 (1993): 1035–42. https://doi.org/10.5281/zenodo.5958385.

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Department of Chemistry, University of Calcutta, 92 Acharya Prafulla Chandra Road, Calcutta-700 009 <em>Manuscript received 23 December 1993</em> The synthesis, characterisation (mass, ir and pmr) and coordinating properties of a new imidazolylpyrazole, viz. 5- methyl-3-(2 -benzimidazolyl)pyrazole (MBP) containing the well-known chelating grouping -N=C-C=N- are reported. A host of electrolytic metallic complexes, M(MBP)<sub>n</sub>X<sub>2</sub>.2H<sub>2</sub>O (M = Co/Ni/Cu; n = 2 or 3; X = a counterion like Cl<sup>-</sup>, Br<sup>-</sup>, NO<sub>3</sub><sup>-</sup>, I<sup>-</sup>, CIO<su

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2

Vincze, Anna, Gergő Dargó, and György Tibor Balogh. "Cornea-PAMPA as an Orthogonal in Vitro Physicochemical Model of Corneal Permeability." Periodica Polytechnica Chemical Engineering 64, no. 3 (2020): 384–90. http://dx.doi.org/10.3311/ppch.15601.

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The present study was aimed to investigate the relationships between permeability and membrane retention values (logPe and MR) of the in vitro non-cellular permeability assay, corneal-PAMPA in comparison with experimental Caco-2 permeability data and calculated physicochemical properties (MW, clogP, clogD7.4 , TPSA). For the investigation, 50 structurally and physicochemically diverse drugs were selected and measured in PAMPA model optimized for corneal permeability. The results showed corneal-PAMPA model's orthogonality in terms of passive diffusion to the FDA approved Caco-2 as a gastrointes

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3

Gronowitz, Mitchell E., Adam Liu, Qiang Qiu, C. Ron Yu, and Thomas A. Cleland. "A physicochemical model of odor sampling." PLOS Computational Biology 17, no. 6 (2021): e1009054. http://dx.doi.org/10.1371/journal.pcbi.1009054.

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We present a general physicochemical sampling model for olfaction, based on established pharmacological laws, in which arbitrary combinations of odorant ligands and receptors can be generated and their individual and collective effects on odor representations and olfactory performance measured. Individual odor ligands exhibit receptor-specific affinities and efficacies; that is, they may bind strongly or weakly to a given receptor, and can act as strong agonists, weak agonists, partial agonists, or antagonists. Ligands interacting with common receptors compete with one another for dwell time;

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Dashkevich, Zh V., V. E. Ivanov, T. I. Sergienko, and B. V. Kozelov. "Physicochemical model of the auroral ionosphere." Cosmic Research 55, no. 2 (2017): 88–100. http://dx.doi.org/10.1134/s0010952517020022.

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5

Bryan, Nicholas D., Dominic M. Jones, Martin Appleton, et al. "A physicochemical model of metal–humate interactions." Physical Chemistry Chemical Physics 2, no. 6 (2000): 1291–300. http://dx.doi.org/10.1039/a908722b.

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6

Dutta, Samrat, Poonam Singhal, Praveen Agrawal, et al. "A Physicochemical Model for Analyzing DNA Sequences." Journal of Chemical Information and Modeling 46, no. 1 (2006): 78–85. http://dx.doi.org/10.1021/ci050119x.

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7

Shapovalov, V. I. "Hot Target. Physicochemical Model of Reactive Sputtering." Technical Physics 64, no. 7 (2019): 926–32. http://dx.doi.org/10.1134/s1063784219070211.

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8

Linard, Y., H. Nonnet, and T. Advocat. "Physicochemical model for predicting molten glass density." Journal of Non-Crystalline Solids 354, no. 45-46 (2008): 4917–26. http://dx.doi.org/10.1016/j.jnoncrysol.2008.07.013.

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9

Hauduc, Hélène, Imre Takács, Scott Smith, et al. "A Dynamic Physicochemical Model for Chemical Phosphorus Removal." Proceedings of the Water Environment Federation 2013, no. 4 (2013): 172–83. http://dx.doi.org/10.2175/193864713813525473.

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10

Nemchinova, N. V., V. A. Bychinskii, S. S. Bel’skii, and V. E. Klets. "Basic physicochemical model of carbothermic smelting of silicon." Russian Journal of Non-Ferrous Metals 49, no. 4 (2008): 269–76. http://dx.doi.org/10.3103/s1067821208040111.

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11

Zhang, Guo-Hua, and Kuo-Chih Chou. "Model for calculating physicochemical properties of aluminosilicate melt." High Temperature Materials and Processes 32, no. 2 (2013): 139–47. http://dx.doi.org/10.1515/htmp-2012-0043.

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AbstractIt is an important issue to calculate the physicochemical properties of aluminosilicate melt based on some known data. However, it is also a difficult issue to do so, especially for a multicomponent slag system since the available data are very limited due to the difficulty of experimental measurement at high temperature. In this paper, a method is suggested to resolve this problem, which is particularly significant. This model can be well used to estimate the electrical conductivity, viscosity, molar volume and surface tension of aluminosilicate melt.

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12

Kopeikin, V. A. "Physicochemical model of tin behavior in weathering profiles." Geochemistry International 55, no. 4 (2017): 389–92. http://dx.doi.org/10.1134/s0016702917040048.

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13

Varfolomeev, S. D., V. N. Kalynychenko, Yu A. Kuznetsov, I. V. Gachok, and S. B. Tsybenova. "Physicochemical Model of the Formation of Allocate Gold." Russian Journal of Physical Chemistry A 98, no. 12 (2024): 2798–809. http://dx.doi.org/10.1134/s0036024424702042.

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14

Paillat, T., J. M. Cabaleiro, H. Romat, and G. Touchard. "Flow electrification process: the physicochemical corroding model revisited." IEEE Transactions on Dielectrics and Electrical Insulation 16, no. 2 (2009): 359–63. http://dx.doi.org/10.1109/tdei.2009.4815164.

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15

Hauduc, H., I. Takács, S. Smith, et al. "A dynamic physicochemical model for chemical phosphorus removal." Water Research 73 (April 2015): 157–70. http://dx.doi.org/10.1016/j.watres.2014.12.053.

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16

Shapovalov, Viktor I., Vitaliy V. Karzin, and Anastasia S. Bondarenko. "Physicochemical model for reactive sputtering of hot target." Physics Letters A 381, no. 5 (2017): 472–75. http://dx.doi.org/10.1016/j.physleta.2016.11.028.

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17

Salamatov, Victor I., Oleg V. Salamatov, and Daria Yu Zabolotnyaya. "To the Issue of Mathematical Modeling of the Red Mud Thickening Process." Defect and Diffusion Forum 410 (August 17, 2021): 400–404. http://dx.doi.org/10.4028/www.scientific.net/ddf.410.400.

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The article focuses on the main mathematical modeling principles for engineering processes. The physical model of the red mud thickening process has been formed. The choice of mathematical model type has been described where the mathematical model represents the physicochemical character of the thickening process and allows estimating pulp water-yielding features at the stage of compression. Mathematical modeling of the engineering process, based on the studies of physicochemical patterns in its course and consideration of these patterns in the mathematical model, does not have certain disadva

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18

Kulterer, Beatrice M., Maria N. Drozdovskaya, Audrey Coutens, Sébastien Manigand, and Gwendoline Stéphan. "Physicochemical models: source-tailored or generic?" Monthly Notices of the Royal Astronomical Society 498, no. 1 (2020): 276–91. http://dx.doi.org/10.1093/mnras/staa2443.

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ABSTRACT Physicochemical models can be powerful tools to trace the chemical evolution of a protostellar system and allow to constrain its physical conditions at formation. The aim of this work is to assess whether source-tailored modelling is needed to explain the observed molecular abundances around young, low-mass protostars or if, and to what extent, generic models can improve our understanding of the chemistry in the earliest stages of star formation. The physical conditions and the abundances of simple, most abundant molecules based on three models are compared. After establishing the dis

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19

Liu, Huai Hui, Wen Long Ji, Peng Zhang, and Chuan Wen Yao. "The Research of Wine Quality Evaluation Based on Multiple Linear Regression." Advanced Materials Research 756-759 (September 2013): 2489–93. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2489.

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Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

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20

He, Qinghai, Haowen Zhang, Tianhua Li, Xiaojia Zhang, Xiaoli Li, and Chunwang Dong. "NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States." Sensors 23, no. 22 (2023): 9107. http://dx.doi.org/10.3390/s23229107.

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Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective p

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21

Kalisz, D. "Modeling Physicochemical Properties of Mold Slag." Archives of Metallurgy and Materials 59, no. 1 (2014): 149–55. http://dx.doi.org/10.2478/amm-2014-0024.

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Abstract This paper deals with the mathematical modeling of physicochemical properties of mold slag such as: viscosity, surface tension, temperature liquidus, basicity. Computer simulation of slag viscosity was made by the Nakamoto structural model. The effect of addition CaF2 to the mold slag was estimated by using of Urbain model. The results were compared with the results of the experiment. Surface tension for the basic slag composition: CaO - SiO2 - Al2O3 was calculated with using Nakamoto model. The results of calculations indicate that the content of the SiO2 lowers the surface tension,

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22

Kopeikin, V. A. "Physicochemical Model of Scandium Behavior in a Weathering Profile." Geochemistry International 59, no. 3 (2021): 328–32. http://dx.doi.org/10.1134/s001670292103006x.

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23

Kopeikin, V. A. "Physicochemical Model of Silver Behavior in a Weathering Profile." Geochemistry International 58, no. 6 (2020): 746–52. http://dx.doi.org/10.1134/s001670292006004x.

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24

Luffer, Debra R., Wilhelm Ecknig, and Milos Novotny. "Physicochemical model of retention for capillary supercritical fluid chromatography." Journal of Chromatography A 505, no. 1 (1990): 79–97. http://dx.doi.org/10.1016/s0021-9673(01)93069-9.

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25

Shende, Pravin, Renuka Chaphalkar, Kiran Deshmukh, and R. S. Gaud. "Physicochemical Investigation of Engineered Nanosuspensions Containing Model Drug, Lansoprazole." Journal of Dispersion Science and Technology 37, no. 4 (2015): 504–11. http://dx.doi.org/10.1080/01932691.2015.1046553.

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26

Gupta, Suneel K., Mary Southam, Robert Gale, and Stephen S. Hwang. "System functionality and physicochemical model of fentanyl transdermal system." Journal of Pain and Symptom Management 7, no. 3 (1992): S17—S26. http://dx.doi.org/10.1016/0885-3924(92)90049-n.

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27

Loveday, Simon M., Jason P. Hindmarsh, Lawrence K. Creamer, and Harjinder Singh. "Physicochemical changes in a model protein bar during storage." Food Research International 42, no. 7 (2009): 798–806. http://dx.doi.org/10.1016/j.foodres.2009.03.002.

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28

Shapovalov, Viktor I. "Physicochemical model for reactive sputtering of a sandwich target." Journal of Applied Physics 133, no. 8 (2023): 085301. http://dx.doi.org/10.1063/5.0128399.

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A non-isothermal physicochemical model of reactive sputtering is extended in this work. The new version is used in this work to simulate reactive sputtering of a sandwich target with two plates of different metals located on the same axis. The external plate contains cut-outs through which the internal plate is sputtered. The main independent process parameters are the reactive gas flow introduced into the vacuum chamber, the discharge current density, and the total area of cut-outs in the external plate. The physical model of the process is described by a system containing 14 algebraic equati

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29

Awasthi, Naveen. "MATHEMATICAL CORRELATION OF THERMOPHYSICAL PROPERTIES FOR ACETONITRILE + N, N -DIMETHYLFORMAMIDE FROM 293.15-313.15K BY JOUYBAN ACREE MODEL." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (2021): 119–23. http://dx.doi.org/10.33564/ijeast.2021.v06i06.016.

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Density, viscosity and refractive indices were measured for a weak interacting liquid formed by acetonitrile and N, N-dimethylformamide (DMF) at 293.15, 298.15,303.15,308.15,313.15K temperature and 1atm pressure over the whole concentration range (0.2142-0.9567). Jouyban Acree model was used to calculate the physicochemical properties. Results obtained from Jouyban Acree model for various physicochemical properties were compared and tested with experimental values. Standard deviation was calculated from calculated and experimental values at different temperatures for all the three physicochemi

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30

Peyrow Hedayati, Davood, Gita Singh, Michael Kucher, Tony D. Keene, and Robert Böhm. "Physicochemical Modeling of Electrochemical Impedance in Solid-State Supercapacitors." Materials 16, no. 3 (2023): 1232. http://dx.doi.org/10.3390/ma16031232.

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Solid-state supercapacitors (SSCs) consist of porous carbon electrodes and gel-polymer electrolytes and are used in novel energy storage applications. The current study aims to simulate the impedance of SSCs using a clearly defined equivalent circuit (EC) model with the ultimate goal of improving their performance. To this end, a conventional mathematical and a physicochemical model were adapted. The impedance was measured by electrochemical impedance spectroscopy (EIS). An EC consisting of electrical elements was introduced for each modeling approach. The mathematical model was purely based o

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31

Aurian-Blajeni, B., M. M. Boucher, A. G. Kimball, and L. S. Robblee. "Physicochemical characterization of sputtered iridium oxide." Journal of Materials Research 4, no. 2 (1989): 440–46. http://dx.doi.org/10.1557/jmr.1989.0440.

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In the present work we characterize sputtered iridium oxide films (SIROF) by differential scanning calorimetry (DSC), x-ray, and impedance spectroscopies. We show that a crystallization transition occurs at ca, 230 °C, and suggest a bilayer model for the sputtered film. The transition results in a crystalline mixture of iridium metal and iridium oxide; this suggests a decomposition-crystallization process of the type 2Ir2O3 ⇉ Ir + 3IrO2. In the bilayer model proposed by us, the layer closer to the substrate would reflect the combined influence of the sputtering conditions and of the substrate,

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32

F. J, Ogbozige, Toko M. A, and Arawo C.C. "Multiple Linear Regression (MLR) Model: A Tool for Water Quality Interpretation." Momona Ethiopian Journal of Science 12, no. 1 (2020): 123–34. http://dx.doi.org/10.4314/mejs.v12i1.8.

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The lack of standard water analysis equipment as well as inadequate trained personnel especially in the developing countries has discouraged many researchers in such countries to execute water quality researches. Hence, this paper presents developed mathematical relationship among some physicochemical parameters in order to aid the determination of the concentrations of certain parameters with the use of minimal equipment. This was achieved by weekly analyzing 7 physicochemical parameters of two sources of potable water (tap water and borehole water) stored in different containers for a period

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33

Korobko, E. V. "Physicochemical Aspects of Forming Electrorheological Fluids." International Journal of Modern Physics B 13, no. 14n16 (1999): 1739–49. http://dx.doi.org/10.1142/s0217979299001740.

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Based on the experimental results and physical representations fo ER-fluids as a "poor" dielectric, a physical model of the ER-effect is elaborated and the main approaches to creation of electrosensitive fluids with desired properties are determined.

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34

Rao, Mohan, Vahid Nassiri, Sanjay Srivastava, et al. "Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules." Pharmaceuticals 17, no. 11 (2024): 1550. http://dx.doi.org/10.3390/ph17111550.

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Background/Objectives: Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug–target interactions crucial for DIKI. This study introduces an AI/ML (artificial intelligence/machine learning) model that integrates both physicochemical properties and off-target interactions to enhance DIKI prediction. Methods: We compiled a dataset of 360 FDA-classif

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35

DMITRIEV, ANDREY N. "DEVELOPMENT OF MATHEMATICAL MODEL OF BLAST FURNACE SMELTING." New Mathematics and Natural Computation 03, no. 03 (2007): 399–407. http://dx.doi.org/10.1142/s1793005707000860.

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The solution of a problem of mathematical description of heat exchange, gas dynamics and the physicochemical phenomena taking place in blast furnace, and some of its application for the study of processes, and defining reduction of metals from multicomponent iron ores are considered.

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36

Jaiswal, Kunal. "Prediction of Ubiquitin Proteins using Artificial Neural Networks, Hidden Markov Model and Support Vector Machines." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 7, no. 6 (2007): 559–68. https://doi.org/10.3233/isi-2007-00328.

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Ubiquitin functions to regulate protein turnover in a cell by closely regulating the degradation of specific proteins. Such a regulatory role is very important, and thus I have analyzed the proteins that are ubiquitin-like, using an artificial neural network, support vector machines and a hidden Markov model (HMM). The methods were trained and tested on a set of 373 ubiquitin proteins and 373 non-ubiquitin proteins, obtained from Entrez protein database. The artificial neural network and support vector machine are trained and tested using both the physicochemical properties and PSSM matrices g

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37

Wolf, Matthew B. "Peritoneal physicochemical transport mechanisms: Hypotheses, models and controversies." Peritoneal Dialysis International: Journal of the International Society for Peritoneal Dialysis 41, no. 4 (2021): 413–16. http://dx.doi.org/10.1177/08968608211002414.

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This study answers criticisms by Waniewski et al. of the recent paper by Wolf on peritoneal transport kinetic models. Their criticisms centre on the accuracy of the data used for model fits, the hypothesis presented, which involves changes in glucose membrane parameters at high peritoneal glucose concentration and on the necessary techniques required to achieve accurate model parameter estimation. In response, this article shows that (1) the mean values previously captured from graphical depictions of Heimburger et al. are not different than those captured from the recent Waniewski et al. grap

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38

Hellriegel, Jan, Steffi Günther, Ingo Kampen, et al. "A Biomimetic Gellan-Based Hydrogel as a Physicochemical Biofilm Model." Journal of Biomaterials and Nanobiotechnology 05, no. 02 (2014): 83–97. http://dx.doi.org/10.4236/jbnb.2014.52011.

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39

Fujitsuka, Y., and S. Takada. "Predictin of protein 3D structures : Evolutionary information and physicochemical model." Seibutsu Butsuri 40, supplement (2000): S20. http://dx.doi.org/10.2142/biophys.40.s20_4.

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40

Maksimov, A. P. "A physicochemical model for deep degassing of water-rich magma." Journal of Volcanology and Seismology 2, no. 5 (2008): 356–63. http://dx.doi.org/10.1134/s0742046308050059.

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41

Krupenin, M. T., A. B. Kol’tsov, and A. V. Maslov. "Physicochemical model of the formation of Satka sparry magnesite deposits." Doklady Earth Sciences 452, no. 2 (2013): 1020–22. http://dx.doi.org/10.1134/s1028334x13100048.

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42

O’Neill, David P., and Peter A. Robbins. "A mechanistic physicochemical model of carbon dioxide transport in blood." Journal of Applied Physiology 122, no. 2 (2017): 283–95. http://dx.doi.org/10.1152/japplphysiol.00318.2016.

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A number of mathematical models have been produced that, given the Pco2 and Po2 of blood, will calculate the total concentrations for CO2 and O2 in blood. However, all these models contain at least some empirical features, and thus do not represent all of the underlying physicochemical processes in an entirely mechanistic manner. The aim of this study was to develop a physicochemical model of CO2 carriage by the blood to determine whether our understanding of the physical chemistry of the major chemical components of blood together with their interactions is sufficiently strong to predict the

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43

Westerman, P. W. "Physicochemical characterization of a model digestive mixture by 2H NMR." Journal of Lipid Research 36, no. 12 (1995): 2478–92. http://dx.doi.org/10.1016/s0022-2275(20)41085-5.

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44

Omron, Edward M., and Rodney M. Omron. "A Physicochemical Model of Crystalloid Infusion on Acid-Base Status." Journal of Intensive Care Medicine 25, no. 5 (2010): 271–80. http://dx.doi.org/10.1177/0885066610371633.

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45

Kopeikin, Valery A. "A PHYSICOCHEMICAL MODEL OF THORIUM BEHAVIOUR IN THE WEATHERING PROFILE." Вестник ВГУ Серия Геология, no. 3 (2022): 20–28. http://dx.doi.org/10.17308/geology/1609-0691/2022/3/20-28.

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46

Yao, Hui, Qingli Dai, and Zhanping You. "Molecular dynamics simulation of physicochemical properties of the asphalt model." Fuel 164 (January 2016): 83–93. http://dx.doi.org/10.1016/j.fuel.2015.09.045.

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47

Westesen, Kirsten, and Thomas Wehler. "Physicochemical Characterization of a Model Intravenous Oil-in-Water Emulsion." Journal of Pharmaceutical Sciences 81, no. 8 (1992): 777–86. http://dx.doi.org/10.1002/jps.2600810812.

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48

Lynn, David G., та Stephen C. Meredith. "Review: Model Peptides and the Physicochemical Approach to β-Amyloids". Journal of Structural Biology 130, № 2-3 (2000): 153–73. http://dx.doi.org/10.1006/jsbi.2000.4287.

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49

Pastuszak, Katarzyna, Elżbieta Chmiel, Bożena Kowalczyk, Jacek Tarasiuk, Małgorzata Jurak, and Marta Palusińska-Szysz. "Physicochemical Characteristics of Model Membranes Composed of Legionella gormanii Lipids." Membranes 13, no. 3 (2023): 356. http://dx.doi.org/10.3390/membranes13030356.

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Legionella gormanii is one of the species belonging to the genus Legionella, which causes atypical community-acquired pneumonia. The most important virulence factors that enable the bacteria to colonize the host organism are associated with the cell surface. Lipids building the cell envelope are crucial not only for the membrane integrity of L. gormanii but also by virtue of being a dynamic site of interactions between the pathogen and the metabolites supplied by its host. The utilization of exogenous choline by the Legionella species results in changes in the lipids’ composition, which influe

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Kadyan, Anu, Yashika Gandhi, and Siddharth Pandey. "Probing interactions within liquid media via a model H-bond donor–acceptor mixture." Physical Chemistry Chemical Physics 21, no. 9 (2019): 4791–801. http://dx.doi.org/10.1039/c8cp07733a.

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