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Employing Quasi-SMILES Notation in Development of Nano-QSPR Models for Nanofluids

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QSPR/QSAR Analysis Using SMILES and Quasi-SMILES

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 33))

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Abstract

Nowadays, variant strategies are proposed and evaluated to find the best scenario for upgrading the high-accurate QSAR/QSPR modeling, particularly on nano-scale. One of the most interesting samples is nanofluids because of high potential in heat transfer applications. In the case of nano-QSPR, some optimum empirical conditions and characteristic features (e.g., size of nanoparticles and temperature) play impressive roles in nanofluids’ properties. Quasi-simplified molecular input-line entry-system (quasi-SMILES) is nominated as valuable linear notation to meet the demands for representation of nanofluids, either chemical structure or defined conditions. The outcomes of nano-QSPR modeling of nanofluids by quasi-SMILES not only make possible the incorporation of molecular structure with experimental conditions in modeling process but also reveal the influence of some molecular features on studied thermophysical properties. Herein, recent studies on the development of predictive models of nanofluids using quasi-SMILES, which is a new trend to estimate the properties of nanofluids, were discussed comprehensively. It is remarkable to point out that the statistical evaluation of proposed models confirmed the predictability power, reliability, and credit of developed models in all reported cases. It is rational that scholars are working on improving QSAR/QSPR modeling; employing quasi-SMILES is an open opportunity to overcome the limitations of conventional molecular representation.

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Abbreviations

ANN:

Artificial neural network

ANFIS:

Adaptive neuro-fuzzy inference system

F k :

Extracted feature of quasi-SMILES

AAD:

Average absolute deviation

CORAL:

Correlation and logic

CW(Fk):

Correlation weight of Fk

CCC:

Concordance correlation coefficient

C p :

Isobaric heat capacity

CII :

Correlation intensity index

DTR:

Decision tree regression

DCW:

Optimal descriptor based on quasi-SMILES

EG:

Ethylene glycol

IIC :

Index of ideality of correlation

GBR:

Gradient boosting regression

MAE:

Mean absolute error

MLP :

Multi-layer perceptron

Q 2 :

Leave-one-out cross-validated correlation coefficient

QSAR:

Quantitative structure–activity relationship

QSPR:

Quantitative structure–property relationship

Quasi-SMILES:

Quasi-simplified molecular input-line entry-system

R 2 :

Correlation coefficient

RBF:

Radial basis function

RFR:

Random forest regression

RMSE:

Root mean square error

LDM:

Liquid drop model

LSSVM:

Least square support vector machine

SVR:

Support vector regression

TC:

Thermal conductivity

TF:

Target function

ρ :

Density

φ :

Volume fraction of nanoparticle (%)

bf:

Base fluid

nf:

Nanofluid

p:

Nanoparticle

v:

Volume fraction

Ag:

Silver

Al2O3:

Aluminium oxide

AlN:

Aluminum nitride

Au:

Gold

Bi2O3:

Bismuth (III) oxide

CeO2:

Cerium (IV) oxide

Co3O4:

Cobalt (II,III) oxide

Cr2O3:

Chromium (III) oxide

Cu:

Copper

CuO:

Copper oxide

Dy2O3:

Dysprosium (III) oxide

Fe:

Iron

Fe2O3:

Iron (III) oxide

Fe3O4:

Iron (II,III) oxide

Gd2O3:

Gadolinium (III) oxide

HfO2:

Hafnium (IV) oxide

In2O3:

Indium (III) oxide

La2O3:

Lanthanum oxide

MgO:

Magnesium oxide

Mn2O3:

Manganese (III) oxide

Mn3O4:

Manganese (II,III) oxide

Ni2O3:

Nickel (III) oxide

NiO:

Nickel (II) oxide

Sb2O3:

Antimony oxide

Si3N4:

Silicon nitride

SiC:

Silicon carbide

SiO2:

Silicon dioxide

SnO2:

Tin (IV) oxide

TiN:

Titanium nitride

TiO2:

Titanium dioxide

WO3:

Tungsten (VI) oxide

Y2O3:

Yttrium (III) oxide

Yb2O3:

Ytterbium (III) oxide

ZnO:

Zinc oxide

ZrO2:

Zirconium oxide

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Declaration of Competing Interest

The authors declare that they have not any known personal relationships or competing financial interests that could have appeared to effect on this chapter.

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Authors and Affiliations

  1. Chemometrics Laboratory, Faculty of Chemistry, University of Mazandaran, Babolsar, Iran

    Kimia Jafari & Mohammad Hossein Fatemi

Authors
  1. Kimia Jafari
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  2. Mohammad Hossein Fatemi
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Corresponding author

Correspondence to Mohammad Hossein Fatemi .

Editor information

Editors and Affiliations

  1. Department of Environmental Health Science, Institute of Pharmacological Research Mario Negri IRCCS, Milan, Italy

    Alla P. Toropova

  2. Department of Environmental Health Science, Institute of Pharmacological Research Mario Negri IRCCS, Milan, Italy

    Andrey A. Toropov

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Jafari, K., Fatemi, M.H. (2023). Employing Quasi-SMILES Notation in Development of Nano-QSPR Models for Nanofluids. In: Toropova, A.P., Toropov, A.A. (eds) QSPR/QSAR Analysis Using SMILES and Quasi-SMILES. Challenges and Advances in Computational Chemistry and Physics, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_15

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