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EC number: 262-114-9 | CAS number: 60239-68-1
- 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
Partition coefficient
Administrative data
Link to relevant study record(s)
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- QSAR prediction from a well-known and acknowledged tool. See below under ''attached background material section' for QPRF containing methodology and domain evaluation details.
- Qualifier:
- according to guideline
- Guideline:
- other: REACH guidance on QSARs: Chapter R.6. QSARs and grouping of chemicals
- Principles of method if other than guideline:
- The partition coefficient (log Kow) value for the test substance was estimated using the group contributions methodology of Molinspiration (miLogP2.2 - November 2005). The log Kow value for test substance was predicted using SMILES codes as the input parameter.
- Type of method:
- other: Group contributions
- Partition coefficient type:
- other: QSAR prediction
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 2.85
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- Molinspiration (miLogP2.2)
- Conclusions:
- Using the group contribution method, of Molinspiration (miLogP 2.2), the partition coefficient (log Kow) value for test substance was predicted to be 2.85.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C11-unsatd. DEA was predicted using the group contribution method, of Molinspiration (miLogP 2.2) program. The log Kow value for test substance was predicted using SMILES codes as the input parameter. Using the group contribution method, the Log Kow values for the individual constituents of the test substance was predicted to be 2.85. The constituent meets the molecular weight and log Kow descriptor domain criteria. Overall, considering either the log Kow predictions for the constituent, the test substance is expected to be less hydrophobic with a good absorption and low accumulation potential. Therefore, the log Kow predictions for the test substance using Molinspiration (miLogP 2.2), can be considered to be reliable with moderate confidence.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- QSAR prediction from a well-known and acknowledged tool. See below under ''attached background material section" for QPRF containing methodology and domain evaluation details.
- Qualifier:
- according to guideline
- Guideline:
- other: REACH guidance on QSARs: Chapter R.6. QSARs and grouping of chemicals
- Principles of method if other than guideline:
- The Partition Coefficient (Log Kow) value for the test substance were estimated using the efficient partition alogorith (EPA) associative neural network (ASNN) method of the ALOGPS v.2.1 program from the Virtual Computational Chemistry Laboratory. Since the test substance is a UVCB, the Log Kow values were predicted for the individual constituents using SMILES codes as the input parameter.
- Type of method:
- other: Associative neural network method (ALOGPS v.2.1)
- Partition coefficient type:
- other: QSAR
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 2.83
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- ALOGPS v.2.1
- Conclusions:
- Using the Efficient Partition Algorithm (EPA) and Associative Neural Networks (ASNN) based regression equations from ALOGPS V.2.1, the partition coefficient (log Kow) value for test substance was predicted to be 2.83.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C11-unsatd. DEA was predicted using the EPA and ASNN based regression equations from ALOGPS V.2.1. the log Kow values for test substance were predicted using SMILES codes as the input parameter. Using the Associative neural network method, the log Kow values for the test substance was predicted to be 2.83 (original estimates). The constituent meets the E-indices,molecular weight and number of non-hydrogen atoms descriptor domain criteria. Overall, considering either the log Kow predictions for the constituent, the test substance is expected to be less hydrophobic with a good absorption and low accumulation potential.The log Kow predictions for the test substance using ALOGPS v.2.1 can be considered to be reliable with moderate confidence.
- Endpoint:
- partition coefficient
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- results derived from a valid (Q)SAR model, but not (completely) falling into its applicability domain, with adequate and reliable documentation / justification
- Justification for type of information:
- QSAR prediction from a well-known and acknowledged tool. See below under ''attached background material section' for QPRF containing methodology and domain evaluation details.
- Qualifier:
- according to guideline
- Guideline:
- other: REACH guidance on QSARs: Chapter R.6. QSARs and grouping of chemicals
- Principles of method if other than guideline:
- The partition coefficient (log Kow) value for the test substance were estimated using the KOWWIN v.1.68. program in EPI SuiteTM v4.11. The log Kow values were predicted for the constituent using SMILES codes as the input parameter.
- Type of method:
- other: Fragment constant method
- Partition coefficient type:
- other: QSAR prediction
- Key result
- Type:
- log Pow
- Partition coefficient:
- ca. 2.26
- Remarks on result:
- other: predicted for the main constituents
- Remarks:
- KOWWIN v.1.68. EPI SuiteTM v4.11
- Conclusions:
- Using the fragment constant method, of KOWWIN V.1.68 program of EPI SuiteTM, the Partition coefficient (log Kow) value for test substance was predicted to be 2.26.
- Executive summary:
The partition coefficient (log Kow) value for the test substance, C11-unsatd. DEA was predicted using the fragment constant method, of KOWWIN V.1.68 program. The log Kow values for the constituent were predicted using SMILES codes as the input parameter. Using the fragment constant method, the log Kow value for the test substance was predicted to be 2.26. The constituent meets the molecular weight and structural fragment descriptor domain criteria. Overall, considering either the individual log Kow predictions for the constituent, the test substance is expected to be less hydrophobic with a good absorption and low accumulation potential. Therefore, the log Kow predictions for the test substance using KOWWIN v1.69 can be considered to be reliable with moderate confidence.
- Endpoint:
- partition coefficient
- Type of information:
- experimental study
- Adequacy of study:
- key study
- Study period:
- February 22, 2017
- Reliability:
- 1 (reliable without restriction)
- Rationale for reliability incl. deficiencies:
- guideline study
- Qualifier:
- according to guideline
- Guideline:
- other: preliminary studies: OECD Guideline 105, EU Method A6
- Deviations:
- no
- GLP compliance:
- not specified
- Type of method:
- other: preliminary studies: OECD Guideline 105, EU Method A6
- Partition coefficient type:
- octanol-water
- Key result
- Type:
- log Pow
- Partition coefficient:
- > 1.58 - < 3.32
- Remarks on result:
- other: Temperature and pH not specified
- Conclusions:
- Under the study conditions, the estimated octanol solubility of the test substance is in the range of 200 g/L – 1000 g/L, which results in the partition coefficient within range of: 1.58 and 3.33.
- Executive summary:
A study was conducted to determine octanol-water partition coefficient of the test substance C11 DEA (90.6% active). The octanol solubility of the test substance was estimated according to the preliminary test for water solubility (OECD Guideline 105 and EU Method A6. Under the study conditions, the estimated octanol solubility of the test substance is in the range of 200 g/L – 1000 g/L, which results in the partition coefficient within range of: 1.58 and 3.33 (Mund, 2017).
Referenceopen allclose all
Description of key information
The partition coefficient was determined according to OECD Guideline 117 (HPLC Method) (Torp, 1996). Weighted average partition coefficient values for the substance were also modelled using the fragment constant method of the KOWWIN V.1.68 program of EPI Suite, the group contribution method of Molinspiration (miLogP 2.2) and the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1.
Key value for chemical safety assessment
- Log Kow (Log Pow):
- 3.33
- at the temperature of:
- 20 °C
Additional information
The experimental partition coefficient was 3.33. Using the fragment constant method of the KOWWIN V.1.68 program of EPI Suite, the weighted average partition coefficient (log Kow) value for test substance was predicted to be 2.26. According to the group contribution method of Molinspiration (miLogP 2.2), the weighted average log Kow was 2.85. According to the efficient partition algorithm and associative neural network-based regression equations from ALOGPS V.2.1, the weighted average log Kow was 2.83.
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.
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