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| Format: | Recurso digital |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.10854649 |
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Table of Contents:
- <div> <p><em><span>A variety number of nanoparticles will increase rapidly in coming years and there is a need<span> </span>for new methods to test the toxicity of the materials. Now a days experimental evaluation of<span> </span>the<span> </span>safety<span> </span>of<span> </span>chemicals<span> </span>is<span> </span>expensive<span> </span>and<span> </span>time<span> </span>consuming.<span> </span>Computational<span> </span>nano<span> </span>QSAR<span> </span>models<span> </span>have been found to be efficient alternatives for predicting the toxicity of metal oxide nano<span> </span>particles.</span></em></p> <p><em><span>The present study proposes a computational QSAR models for predicting the toxicity of<span> </span>MEONPs. Two types of mechanisms are collectively applied in a nano QSAR model,which<span> </span>provides control over the toxicity of metal oxide nanoparticles. The two parameters, enthalpy<span> </span><span>of formation of gaseous cation (∆H</span>me+<span>) and polarization force(Z/r) were elucidated to make a<span> </span></span>significant<span> </span>contribution for<span> </span>the toxic<span> </span>effect of the<span> </span>metal oxide<span> </span>nanoparticles.</span></em></p> </div>