A Novel QSPR Model for the Prediction of the Ionic Liquid Toxicities towards Green Algae {Pseudokirchneriella SubcapUata)
Ionic Liquid (IL) are defined as the salt that melting point temperatures are lower than the boiling point of water. ILs can also be called as green solvent due to their undetectable vapor pressure hence can emit no volatile organic compounds. However, many ILs are found to be harmful and toxic t...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Universiti Teknologi PETRONAS
2012
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| Online Access: | http://utpedia.utp.edu.my/9621/1/2012%20-%20A%20Novel%20QSAR%20Model%20for%20the%20Prediction%20of%20the%20Ionic%20Liquid%20Toxicities%20Towards%20Green%20Algae%28P.pdf http://utpedia.utp.edu.my/9621/ |
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| Summary: | Ionic Liquid (IL) are defined as the salt that melting point temperatures are lower
than the boiling point of water. ILs can also be called as green solvent due to their
undetectable vapor pressure hence can emit no volatile organic compounds.
However, many ILs are found to be harmful and toxic to human and environment.
Therefore, the toxicity studies of ILs become of great importance. The early study in
determining toxicity of ILs was by using experimental works. However, ILs present
in a very large combination of cations and anions that synthesized in the market and
thousands are added every year which exceed the capacity of experimental works.
Besides, experimental works are time consuming, require high cost and can kills
aquatic organism. Therefore, Quantitative Structure Activity Relationship (QSAR)
models are the best approach to overcome the above matter since QSAR approach is
suitable to be used for huge number of chemical in a rational and effective manner.
This study aims to develop a QSAR model to predict the toxicities of ILs towards
one of green algae which is Pseudokirchneriella subcapitata (P. subcapitata),
previously known as Selenastrum capricornutum. The dataset constructed by
gathering 61 effective concentrations (EC50) values of various ILs towards P.
subcapitata from published literature before they being fragmented according to their
cations, anions, and alkyl groups. The prediction model will then develop using
QSAR approach employing multiple linear regressions (MLR) with polynomial
model that will be coded using MATLAB software. To the best of our knowledge,
there is no previous model developed based on combination of these two models
towards P. subcapitata. The proposed model indicates that the model is capable of
predicting the IL toxicities accurately, where R > 100% with an average absolute
deviation error 0%. The model has the potential to be used as an alternative to
experimental measurement in the determination of EC50 values for a wide range of
ILs towards P. subcapitata. In addition, it can be one of the important references for
industrial people who deal with ILs. |
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