Browsing QnSAR-s or QnSPR-s for nanostructures, nanoparticles or nanomaterials by Submit Date

Showing items 1-7 of 7

Martin, D.; Maran, U.; Sild, S.; Karelson, M. QSPR Modeling of Solubility of Polyaromatic Hydrocarbons and Fullerene in 1-Octanol and n-Heptane. J. Phys. Chem. B 2007, 111, 33, 9853-9857.

Published: Geven Piir (2014-10-10)

Abstract: Solubility of polyaromatic hydrocarbons (PAH) and carbon nanostructures is important both from the technical and environmental points of view. In the present work, two general quantitative structure - property relationship (QSPR) models were developed, describing the solubility of PAH-s and fullerene (C60 ) in two different condensed media (1-octanol and ...

Regression
Martin, D.; Sild, S.; Maran, U.; Karelson, M. QSPR Modeling of the Polarizability of Polyaromatic Hydrocarbons and Fullerenes. J. Phys. Chem. C 2008, 112, 13, 4785–4790.

Published: Geven Piir (2014-10-10)

Abstract: Polarizability is one of the key properties determining the nonlinear optical effects of the new materials. In the current study, a quantitative structure - property relationship approach is used to model the polarizability of polyaromatic hydrocarbons (PAHs) and fullerenes. The model is derived using the data set of 40 PAHs and fullerenes and includes just ...

Regression
Puzyn, T.; Rasulev, B.; Gajewicz, A.; Hu, X.; Dasari, T. P.; Michalkova, A.; Hwang, H.; Toropov, A.; Leszczynska, D.; Leszczynski, J. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat. Nanotechnol. 2011, 6, 3, 175–178.

Published: Geven Piir (2014-10-10)

Abstract: It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efﬁcient ...

Regression
Käärik, M.; Maran, U.; Arulepp, M.; Perkson, A.; Leis, J. Quantitative nano-structure-property relationships for the nanoporous carbon: Predicting the performance of energy storage materials. ACS Appl. Energy Mater. 2018, 1, 4016-4024

Published: Uko Maran, Maike Käärik, Mati Arulepp, Anti Perkson, Jaan Leis (2018-07-26)

Abstract: Nanoporous carbon-based energy storage is a fast-growing research field thanks to high energy densities of carbon electrodes with nanoporous amorphous texture. To support the developments on electrical double-layer based ultra-capacitors it is necessary to improve understanding about relationships between the porous structure and energy storage behavior of ...

Regression
Käärik, M.; Arulepp, M.; Käärik, M.; Maran, U.; Leis, J. Characterization and prediction of double-layer capacitance of nanoporous carbon materials using the Quantitative nano-Structure-Property Relationship approach based on experimentally determined porosity descriptors. Carbon 2020, 158, 494-504.

Published: Maike Käärik, Mati Arulepp, Meelis Käärik, Uko Maran, Jaan Leis (2019-11-06)

Abstract: The development of nanoporous carbon-based energy storage is a fast-growing area. To assist these developments, it is necessary to establish simple criteria and relationships between electric double-layer (EDL) capacitance and the nature of porous carbon used as an electrode material. Under special attention is carbide-derived carbon (CDC) due to high content ...

Regression
Gajewicz, A.; Schaeublin, N.; Rasulev, B.; Hussain, S.; Leszczynska, D.; Puzyn, T.; Leszczynski, J. Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: Hints from nano-QSAR studies. Nanotoxicology 2014, 9, 313–325.

Published: Geven Piir (2020-01-23)

Abstract: The production of nanomaterials increases every year exponentially and therefore the probability these novel materials that they could cause adverse outcomes for human health and the environment also expands rapidly. We proposed two types of mechanisms of toxic action that are collectively applied in a nano-QSAR model, which provides governance over the toxicity ...

Regression
Käärik, M.; Krjukova, N.; Maran, U.; Oja, M.; Piir, G.; Leis, J. Nanomaterial texture-based machine learning of ciprofloxacin adsorption on nanoporous carbon. Int. J. Mol. Sci. 2024, 25, 11696.

Published: Maike Käärik, Nadežda Krjukova, Uko Maran, Mara Oja, Geven Piir, Jaan Leis (2024-10-25)

Abstract: Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular ...

Regression

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