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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

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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

QDB archive DOI: 10.15152/QDB.205   DOWNLOAD

QsarDB content

Property Cv_pos: Volumetric anodic capacitance [F cm-3]

Compounds: 100 | Models: 1 | Predictions: 1

Tab1-2: Two-parameter model for volumetric anodic capacitance (SetA)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 100 0.928 3.910

Property Cv_neg: Volumetric cathodic capacitance [F cm-3]

Compounds: 100 | Models: 4 | Predictions: 5

Tab1-8: Two-parameter model for volumetric cathodic capacitance (SetA)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 100 0.933 3.105
Tab2: Three-parameter model for volumetric cathodic capacitance - main model (SetA)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 100 0.941 2.904
SetB: Three-parameter model to verify influence of impurities from TiO2 derivatives (SetB)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 81 0.943 2.957
Tab3: Three-parameter model to verify influence of H2O-activation of nanopores (SetC)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 53 0.944 2.681
Validation set external validation 47 0.936 3.242

Citing

When using this data, please cite the original article and this QDB archive:

  • 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. http://dx.doi.org/10.1021/acsaem.8b00708

  • Maran, U.; Käärik, M.; Arulepp, M.; Perkson, A.; Leis, J. QDB archive #205. QsarDB repository, 2018. http://dx.doi.org/10.15152/QDB.205

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dc.date.accessioned 2018-07-26T14:30:20Z
dc.date.available 2018-07-10T14:30:20Z
dc.date.issued 2018-07-26
dc.identifier.uri http://hdl.handle.net/10967/205
dc.identifier.uri http://dx.doi.org/10.15152/QDB.205
dc.description.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 carbon materials. This can be facilitated by the analysis of complex data sets and the development of corresponding descriptive and predictive models. Related to that this paper presents a in silico regression model to predict the suitability of various carbon materials for energy storage, thus being probably the first time a quantitative nano-structure-property relationship (QnSPR) approach is applied to the nanoporous carbon materials. With this study, which is based on the experimental data of 100 carbide-derived carbon materials, it has been shown that the electrical double-layer capacitance of carbon electrode in a nonaqueous electrolyte can be predicted using experimentally determined specific surface area and a volume of certain pore size fraction of carbon and a bulk density of carbon electrode. The three-parameter QnSPR model for volumetric cathodic capacitance of carbon in triethylmethylammonium tetrafluoroborate / propylene carbonate electrolyte, Cv,neg = f(SBET, Vd<1.14, Del), comprising the above-mentioned parameters and characterized by R2=0.94 and s2=8.7, confirms the important role of carbon pore size for the double layer capacitance. It was shown that carbon pores with a size below 1.1 nm have the most significance for achieving high energy densities in the nonaqueous electrochemical systems studied. Putting the results of this research into wider perspective, it has been shown that the QnSPR approach provides a useful tool for describing and predicting the variable performance-related physical properties of nanoporous carbon and nanomaterial properties in general. The models are available in the QsarDB repository.
dc.publisher Uko Maran
dc.publisher Maike Käärik
dc.publisher Mati Arulepp
dc.publisher Anti Perkson
dc.publisher Jaan Leis
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.title 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
qdb.property.endpoint 6. Other (Volumetric capacitance) en_US
qdb.prediction.application Codessa 2.20 en_US
bibtex.entry article en_US
bibtex.entry.author Käärik, M.
bibtex.entry.author Maran, U.
bibtex.entry.author Arulepp, M.
bibtex.entry.author Perkson, A.
bibtex.entry.author Leis, J.
bibtex.entry.doi 10.1021/acsaem.8b00708 en_US
bibtex.entry.journal ACS Appl. Energy Mater. en_US
bibtex.entry.month
bibtex.entry.number 8 en_US
bibtex.entry.pages 4016-4024 en_US
bibtex.entry.title Quantitative nano-structure-property relationships for the nanoporous carbon: Predicting the performance of energy storage materials en_US
bibtex.entry.volume 1 en_US
bibtex.entry.year 2018
qdb.model.type Regression model (regression) en_US


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