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Diss Factsheets
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EC number: 213-690-5 | CAS number: 1002-67-1
- Life Cycle description
- Uses advised against
- Endpoint summary
- Appearance / physical state / colour
- Melting point / freezing point
- Boiling point
- Density
- Particle size distribution (Granulometry)
- Vapour pressure
- Partition coefficient
- Water solubility
- Solubility in organic solvents / fat solubility
- Surface tension
- Flash point
- Auto flammability
- Flammability
- Explosiveness
- Oxidising properties
- Oxidation reduction potential
- Stability in organic solvents and identity of relevant degradation products
- Storage stability and reactivity towards container material
- Stability: thermal, sunlight, metals
- pH
- Dissociation constant
- Viscosity
- Additional physico-chemical information
- Additional physico-chemical properties of nanomaterials
- Nanomaterial agglomeration / aggregation
- Nanomaterial crystalline phase
- Nanomaterial crystallite and grain size
- Nanomaterial aspect ratio / shape
- Nanomaterial specific surface area
- Nanomaterial Zeta potential
- Nanomaterial surface chemistry
- Nanomaterial dustiness
- Nanomaterial porosity
- Nanomaterial pour density
- Nanomaterial photocatalytic activity
- Nanomaterial radical formation potential
- Nanomaterial catalytic activity
- Endpoint summary
- Stability
- Biodegradation
- Bioaccumulation
- Transport and distribution
- Environmental data
- Additional information on environmental fate and behaviour
- Ecotoxicological Summary
- Aquatic toxicity
- Endpoint summary
- Short-term toxicity to fish
- Long-term toxicity to fish
- Short-term toxicity to aquatic invertebrates
- Long-term toxicity to aquatic invertebrates
- Toxicity to aquatic algae and cyanobacteria
- Toxicity to aquatic plants other than algae
- Toxicity to microorganisms
- Endocrine disrupter testing in aquatic vertebrates – in vivo
- Toxicity to other aquatic organisms
- Sediment toxicity
- Terrestrial toxicity
- Biological effects monitoring
- Biotransformation and kinetics
- Additional ecotoxological information
- Toxicological Summary
- Toxicokinetics, metabolism and distribution
- Acute Toxicity
- Irritation / corrosion
- Sensitisation
- Repeated dose toxicity
- Genetic toxicity
- Carcinogenicity
- Toxicity to reproduction
- Specific investigations
- Exposure related observations in humans
- Toxic effects on livestock and pets
- Additional toxicological data
Viscosity
Administrative data
Link to relevant study record(s)
- Endpoint:
- viscosity
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Study period:
- 19 January 2019
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- 1. SOFTWARE
Toxicity Estimation Software Tool
2. MODEL (incl. version number)
T.E.S.T. Version 4.2.1
3. SMILES OR OTHER IDENTIFIERS USED AS INPUT FOR THE MODEL
CCOCCOCCOC
4. SCIENTIFIC VALIDITY OF THE (Q)SAR MODEL
T.E.S.T allows you to estimate toxicity values using several different advanced QSAR methodologies 2:
Hierarchical method: The toxicity for a given query compound is estimated using the weighted average of the predictions from several different models. The different models are obtained by using Ward’s method to divide the training set into a series of structurally similar clusters. A genetic algorithm based technique is used to generate models for each cluster. The models are generated prior to runtime.
FDA method: The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
Single model method: Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables) using a genetic algorithm based approach. The regression model is generated prior to runtime.
Group contribution method: Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables). The regression model is generated prior to runtime.
Nearest neighbor method: The predicted toxicity is estimated by taking an average of the 3 chemicals in the training set that are most similar to the test chemical.
Consensus method: The predicted toxicity is estimated by taking an average of the predicted toxicities from the above QSAR methods (provided the predictions are within the respective applicability domains).
Random forest method: The predicted toxicity is estimated using a decision tree which bins a chemical into a certain toxicity score (i.e. positive or negative developmental toxicity) using a set of molecular descriptors as decision variables. The random forest method is currently only available for the developmental toxicity endpoint. The random forest models for the developmental toxicity endpoint were developed by researchers at Mario Negri Institute for Pharmacological Research as part of the CAESAR project 3.
Mode of action method: The predicted toxicity is estimated using a two-step process. In the first step the mode of action is determined from the linear discriminant analysis model with the highest score. In the second step the toxicity is estimated using the multilinear regression model corresponding to the predicted mode of action. The mode of action method is currently only available for the 96 hour fathead minnow LC50 endpoint.
T.E.S.T provides multiple prediction methodologies so one can have greater confidence in the predicted toxicities (assuming the predicted toxicities are similar from different methods).
5. APPLICABILITY DOMAIN
Viscosity is a measure of the resistance of a fluid to flow in cP defined as the proportionality constant between shear rate and shear stress). The viscosity at 25°C for 557 chemicals was obtained from Viswanath and Riddick. The viscosity values were obtained from Viswanath and Riddick were obtained as follows:
1. If a value is available at 25°C this value is used
2. If an experimental value is not available, a value is extrapolated to 25°C (as long as the closest data point is within 10°C of 25°C) using the following empirical correlation:
log10 viscosity A+B/T
Extrapolation was used in order to expand size of the overall dataset. The modeled property was log10(viscosity cP).
6. ADEQUACY OF THE RESULT
For this property, the consensus method gives the best results if you consider both prediction accuracy and coverage. The low k values for this endpoint can be attributed to the two possible outliers in the test set that fall below the Y=X line.. Further details are attached. - Principles of method if other than guideline:
- QSAR estmiation by imput of SMILES Code Toxicity Estimation Software Tool developed by US EPA
- GLP compliance:
- no
- Type of method:
- other: QSAR assessment
- Key result
- Temp.:
- 20°C
- Parameter:
- dynamic viscosity (in mPa s)
- Value:
- 1.15
- Conclusions:
- The viscosity is predicted to be 1.15
- Executive summary:
The Viscosity has been estimated using the Toxicity Estimation Software Tool developed by US EPA. The viscosity if predicted to be 1.15
Reference
Description of key information
The Viscosity has been estimated using the Toxicity Estimation Software Tool developed by US EPA. The viscosity if predicted to be 1.15
Key value for chemical safety assessment
- Viscosity:
- 1.15 mPa · s (dynamic)
- at the temperature of:
- 20 °C
Additional information
Information on Registered Substances comes from registration dossiers which have been assigned a registration number. The assignment of a registration number does however not guarantee that the information in the dossier is correct or that the dossier is compliant with Regulation (EC) No 1907/2006 (the REACH Regulation). This information has not been reviewed or verified by the Agency or any other authority. The content is subject to change without prior notice.
Reproduction or further distribution of this information may be subject to copyright protection. Use of the information without obtaining the permission from the owner(s) of the respective information might violate the rights of the owner.