Treffer: Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach.
Title:
Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach.
Authors:
Carvalho FS; Departamento de Química - ICEx, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil., Braga JP; Departamento de Química - ICEx, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil. jjppbraga@gmail.com., Alves MO; Departamento de Química, CEFET, Contagem, Minas Gerais, Brazil.
Source:
Journal of molecular modeling [J Mol Model] 2022 Sep 02; Vol. 28 (9), pp. 286. Date of Electronic Publication: 2022 Sep 02.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Germany NLM ID: 9806569 Publication Model: Electronic Cited Medium: Internet ISSN: 0948-5023 (Electronic) Linking ISSN: 09485023 NLM ISO Abbreviation: J Mol Model Subsets: MEDLINE
Imprint Name(s):
Original Publication: Berlin : Springer, c1996-
MeSH Terms:
References:
Moon CJ, Lee JH (2005) Use of curdlan and activated carbon composed adsorbents for heavy metal removal. Process Biochem 40:1279–1283. (PMID: 10.1016/j.procbio.2004.05.009)
De Gisi S, Lofrano G, Grassi M, Notarnicola M (2016) Characteristics and adsorption capacities of low-cost sorbents for wastewater treatment: a review. Sustainable Mater 9:10–40.
Jeirani Z, Niu CH, Soltan J (2017) Adsorption of emerging pollutants on activated carbon. Rev Chem Eng 33:491–522. (PMID: 10.1515/revce-2016-0027)
Rajahmundry GK, Garlapati C, Kumar PS, Alwi RS, Vo DVN (2021) Statistical analysis of adsorption isotherm models and its appropriate selection. Chemosphere 276:130176. (PMID: 10.1016/j.chemosphere.2021.130176)
Ghosal PS, Gupta AK (2017) Development of a generalized adsorption isotherm model at solid-liquid interface: A novel approach. J Mol Liq 240:21–24. (PMID: 10.1016/j.molliq.2017.05.042)
Ranjan P, Verma P, Agrawal S, Rao TR, Samanta SK, Thakur AD (2019) Inducing dye-selectivity in graphene oxide for cationic dye separation applications. Mater Chem Phys 226:350–355. (PMID: 10.1016/j.matchemphys.2019.01.047)
Carr R, Comer J, Ginsberg MD, Aksimentiev A (2011) Microscopic perspective on the adsorption isotherm of a heterogeneous surface. J Phys Chem Lett 2:1804–1807. (PMID: 10.1021/jz200749d)
Khalfaoui M, Knani S, Hachicha MA, Lamine AB (2003) New theoretical expressions for the five adsorption type isotherms classified by BET based on statistical physics treatment. J Colloid Interface Sci 263:350–356. (PMID: 10.1016/S0021-9797(03)00139-5)
Nakhli A, Bergaoui M, Aguir C, Khalfaoui M, M’henni MF, Lamine AB, (2016) Adsorption thermodynamics in the framework of the statistical physics formalism: basic blue 41 adsorption onto Posidonia biomass. Desalin Water Treat 57:12730–12742.
Torkia YB, Atrous M, Bouzid M, Dotto GL, Lamine AB (2020) Stereographic and energetic studies of acid blue 9 adsorption onto Spirulina platensis (strain LEB-52) based on statistical physics approach. Chem Eng Commun 207:445–457. (PMID: 10.1080/00986445.2019.1604513)
Oueslati K, Lima EC, Ayachi F, Cunha MR, Lamine AB (2020) Modeling the removal of Reactive Red 120 dye from aqueous effluents by activated carbon. Water Sci Technol 82:651–662. (PMID: 10.2166/wst.2020.347)
Radke CJ, Prausnitz JM (1972) Statistical mechanics of adsorption from dilute liquid solution. J Chem Phys 57:714–722. (PMID: 10.1063/1.1678304)
Braga JP (2021) Termodinâmica estatística de átomos e moléculas. Livraria da Física, São Paulo.
Hadamard J (1923) Lectures on Cauchy’s problem in linear partial differential equations. Yale University Press, New Haven.
Leon SJ, Bica I, Hohn T (2006) Linear algebra with applications. Pearson Prentice Hall, Upper Saddle River, NJ.
Tikhonov AN, Arsenin VY (1977) Solutions of Ill-Posed Problems. Wiley, New York.
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79:2554–2558. (PMID: 10.1073/pnas.79.8.2554)
Hopfield JJ, Tank DW (1985) Neuralcomputation of decisions in optimization problems. Biol Cybern 52:141–152. (PMID: 10.1007/BF00339943)
Carvalho FS, Braga JP (2020) Indirect Solution of Ornstein-Zernike Equation Using the Hopfield Neural Network Method. Braz J Phys 50:489–494. (PMID: 10.1007/s13538-020-00769-4)
Carvalho FS, Braga JP (2020) Radial distribution function for liquid gallium from experimental structure factor: a Hopfield neural network approach. J Mol Model 26:1–5. (PMID: 10.1007/s00894-019-4247-5)
Carvalho FS, Braga JP, Alves MO, Gonçalves CEM (2020) Neural network in the inverse problem of liquid argon structure factor: from gas-to-liquid radial distribution function. Theor Chem Acc 139:1–6. (PMID: 10.1007/s00214-019-2531-1)
Sebastião RCO, Braga JP (2005) Retrieval of transverse relaxation time distribution from spin-echo data by recurrent neural network. J Magn Reson 177:146–151. (PMID: 10.1016/j.jmr.2005.07.017)
Braga JP, de Almeida MB, Braga AP, Belchior JC (2000) Hopfield neural network model for calculating the potential energy function from second virial data. Chem Phys 260:347–352. (PMID: 10.1016/S0301-0104(00)00218-4)
Lemes NHT, Borges E, Sousa RV, Braga JP (2008) Potential energy function from differential cross-section data: An inverse quantum scattering theory approach. Int J Quantum Chem 108:2623–2627. (PMID: 10.1002/qua.21701)
Araujo BC, Carvalho FS, Marques MBF, Braga JP, Sebastião RCO (2020) Hopfield Neural Network-Based Algorithm Applied to Differential Scanning Calorimetry Data for Kinetic Studies in Polymorphic Conversion. J Braz Chem Soc 31:1392–1400.
Viterbo VC, Braga JP, Braga AP, de Almeida MB (2001) Inversion of simulated positron annihilation lifetime spectrum using a neural network. J Chem Inf Comput Sci 41:309–313. (PMID: 10.1021/ci0000833)
Vemuri V, Jang GS (1992) Inversion of Fredholm integral equations of the first kind with fully connected neural networks. J Franklin Inst 329:241–257. (PMID: 10.1016/0016-0032(92)90031-B)
Lemes NHT, Borges E, Braga JP (2007) A general algorithm to solve linear and nonlinear inverse problems. J Braz Chem Soc 18:1342–1347. (PMID: 10.1590/S0103-50532007000700008)
McLachlan AD (1965) Effect of the medium on dispersion forces in liquids. Discuss. Faraday Soc 40:239–245. (PMID: 10.1039/df9654000239)
Kestner NR, Sinanoǧlu O (1965) Intermolecular forces in dense media. Discuss Faraday Soc 40:266–267. (PMID: 10.1039/DF9654000266)
Shampine LF, Reichelt MW (1997) The MATLAB ODE Suite. SIAM J Sci Comput 18:1–22. (PMID: 10.1137/S1064827594276424)
Shampine LF, Reichelt MW, Kierzenka JA (1999) Solving Index-1 DAEs in MATLAB and Simulink. SIAM Review 41:538–552. (PMID: 10.1137/S003614459933425X)
De Gisi S, Lofrano G, Grassi M, Notarnicola M (2016) Characteristics and adsorption capacities of low-cost sorbents for wastewater treatment: a review. Sustainable Mater 9:10–40.
Jeirani Z, Niu CH, Soltan J (2017) Adsorption of emerging pollutants on activated carbon. Rev Chem Eng 33:491–522. (PMID: 10.1515/revce-2016-0027)
Rajahmundry GK, Garlapati C, Kumar PS, Alwi RS, Vo DVN (2021) Statistical analysis of adsorption isotherm models and its appropriate selection. Chemosphere 276:130176. (PMID: 10.1016/j.chemosphere.2021.130176)
Ghosal PS, Gupta AK (2017) Development of a generalized adsorption isotherm model at solid-liquid interface: A novel approach. J Mol Liq 240:21–24. (PMID: 10.1016/j.molliq.2017.05.042)
Ranjan P, Verma P, Agrawal S, Rao TR, Samanta SK, Thakur AD (2019) Inducing dye-selectivity in graphene oxide for cationic dye separation applications. Mater Chem Phys 226:350–355. (PMID: 10.1016/j.matchemphys.2019.01.047)
Carr R, Comer J, Ginsberg MD, Aksimentiev A (2011) Microscopic perspective on the adsorption isotherm of a heterogeneous surface. J Phys Chem Lett 2:1804–1807. (PMID: 10.1021/jz200749d)
Khalfaoui M, Knani S, Hachicha MA, Lamine AB (2003) New theoretical expressions for the five adsorption type isotherms classified by BET based on statistical physics treatment. J Colloid Interface Sci 263:350–356. (PMID: 10.1016/S0021-9797(03)00139-5)
Nakhli A, Bergaoui M, Aguir C, Khalfaoui M, M’henni MF, Lamine AB, (2016) Adsorption thermodynamics in the framework of the statistical physics formalism: basic blue 41 adsorption onto Posidonia biomass. Desalin Water Treat 57:12730–12742.
Torkia YB, Atrous M, Bouzid M, Dotto GL, Lamine AB (2020) Stereographic and energetic studies of acid blue 9 adsorption onto Spirulina platensis (strain LEB-52) based on statistical physics approach. Chem Eng Commun 207:445–457. (PMID: 10.1080/00986445.2019.1604513)
Oueslati K, Lima EC, Ayachi F, Cunha MR, Lamine AB (2020) Modeling the removal of Reactive Red 120 dye from aqueous effluents by activated carbon. Water Sci Technol 82:651–662. (PMID: 10.2166/wst.2020.347)
Radke CJ, Prausnitz JM (1972) Statistical mechanics of adsorption from dilute liquid solution. J Chem Phys 57:714–722. (PMID: 10.1063/1.1678304)
Braga JP (2021) Termodinâmica estatística de átomos e moléculas. Livraria da Física, São Paulo.
Hadamard J (1923) Lectures on Cauchy’s problem in linear partial differential equations. Yale University Press, New Haven.
Leon SJ, Bica I, Hohn T (2006) Linear algebra with applications. Pearson Prentice Hall, Upper Saddle River, NJ.
Tikhonov AN, Arsenin VY (1977) Solutions of Ill-Posed Problems. Wiley, New York.
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79:2554–2558. (PMID: 10.1073/pnas.79.8.2554)
Hopfield JJ, Tank DW (1985) Neuralcomputation of decisions in optimization problems. Biol Cybern 52:141–152. (PMID: 10.1007/BF00339943)
Carvalho FS, Braga JP (2020) Indirect Solution of Ornstein-Zernike Equation Using the Hopfield Neural Network Method. Braz J Phys 50:489–494. (PMID: 10.1007/s13538-020-00769-4)
Carvalho FS, Braga JP (2020) Radial distribution function for liquid gallium from experimental structure factor: a Hopfield neural network approach. J Mol Model 26:1–5. (PMID: 10.1007/s00894-019-4247-5)
Carvalho FS, Braga JP, Alves MO, Gonçalves CEM (2020) Neural network in the inverse problem of liquid argon structure factor: from gas-to-liquid radial distribution function. Theor Chem Acc 139:1–6. (PMID: 10.1007/s00214-019-2531-1)
Sebastião RCO, Braga JP (2005) Retrieval of transverse relaxation time distribution from spin-echo data by recurrent neural network. J Magn Reson 177:146–151. (PMID: 10.1016/j.jmr.2005.07.017)
Braga JP, de Almeida MB, Braga AP, Belchior JC (2000) Hopfield neural network model for calculating the potential energy function from second virial data. Chem Phys 260:347–352. (PMID: 10.1016/S0301-0104(00)00218-4)
Lemes NHT, Borges E, Sousa RV, Braga JP (2008) Potential energy function from differential cross-section data: An inverse quantum scattering theory approach. Int J Quantum Chem 108:2623–2627. (PMID: 10.1002/qua.21701)
Araujo BC, Carvalho FS, Marques MBF, Braga JP, Sebastião RCO (2020) Hopfield Neural Network-Based Algorithm Applied to Differential Scanning Calorimetry Data for Kinetic Studies in Polymorphic Conversion. J Braz Chem Soc 31:1392–1400.
Viterbo VC, Braga JP, Braga AP, de Almeida MB (2001) Inversion of simulated positron annihilation lifetime spectrum using a neural network. J Chem Inf Comput Sci 41:309–313. (PMID: 10.1021/ci0000833)
Vemuri V, Jang GS (1992) Inversion of Fredholm integral equations of the first kind with fully connected neural networks. J Franklin Inst 329:241–257. (PMID: 10.1016/0016-0032(92)90031-B)
Lemes NHT, Borges E, Braga JP (2007) A general algorithm to solve linear and nonlinear inverse problems. J Braz Chem Soc 18:1342–1347. (PMID: 10.1590/S0103-50532007000700008)
McLachlan AD (1965) Effect of the medium on dispersion forces in liquids. Discuss. Faraday Soc 40:239–245. (PMID: 10.1039/df9654000239)
Kestner NR, Sinanoǧlu O (1965) Intermolecular forces in dense media. Discuss Faraday Soc 40:266–267. (PMID: 10.1039/DF9654000266)
Shampine LF, Reichelt MW (1997) The MATLAB ODE Suite. SIAM J Sci Comput 18:1–22. (PMID: 10.1137/S1064827594276424)
Shampine LF, Reichelt MW, Kierzenka JA (1999) Solving Index-1 DAEs in MATLAB and Simulink. SIAM Review 41:538–552. (PMID: 10.1137/S003614459933425X)
Contributed Indexing:
Keywords: Adsorption; Hopfield neural network; Ill-posed inverse problems; Second virial coefficient
Entry Date(s):
Date Created: 20220902 Date Completed: 20220907 Latest Revision: 20220907
Update Code:
20250114
DOI:
10.1007/s00894-022-05274-w
PMID:
36056267
Database:
MEDLINE
Weitere Informationen
The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of potential energy function, [Formula: see text], between adsorbate and adsorbent from experimental data. Since the adsorption data is related to the second virial coefficient and therefore to [Formula: see text] through an integral equation, the Hopfield Neural Network will be used to find the best parameters which fits the experimental data. Initially simulated results will be analyzed to verify the method performance for data sets with and without noise addition. Then, experimental data for adsorption of propionitrile on activated carbon will be treated. Results presented here corroborate to the robustness of this method.
(© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)