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EC number: 911-168-8 | CAS number: -
- 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
Vapour pressure
Administrative data
Link to relevant study record(s)
- Endpoint:
- vapour pressure
- Type of information:
- (Q)SAR
- Adequacy of study:
- key study
- Reliability:
- 2 (reliable with restrictions)
- Rationale for reliability incl. deficiencies:
- accepted calculation method
- Justification for type of information:
- 1. SOFTWARE: MPBPWIN as implemented by EPISuite tool
2. IDENTIFIERS USED AS INPUT FOR THE MODEL:
constituent 1: C1=CC=C2C(=C1)N=C(O2)C3=CC=C(C=C3)C=CC4=CC=C(C=C4)C5=NC6=CC=CC=C6O5
constituent 2: CC1=CC2=C(C=C1)OC(=N2)C3=CC=C(C=C3)C=CC4=CC=C(C=C4)C5=NC6=C(O5)C=CC(=C6)C
constituent 3: CC1=CC2=C(C=C1)OC(=N2)C3=CC=C(C=C3)C=CC4=CC=C(C=C4)C5=NC6=CC=CC=C6O5
3. DETAILS ON MODEL
Estimation Methodology
MPBPWIN estimates vapor pressure (VP) by three separate methods: (1) the Antoine method, (2) the modified Grain method, and (3) the Mackay method. All three use the normal boiling point to estimate VP. Unless the user enters a boiling point on the data entry screen, MPBPWIN uses the estimated boiling point from the adapted Stein and Brown method as described in the Boiling Point section of this help file. When a boiling point is entered on the data entry screen, MPBPWIN uses it. Each VP method is discussed below.
Antoine Method: Chapter 14 of Lyman et al (1990) includes the description of the Antoine method used by MPBPWIN. It was developed for gases and liquids.
MPBPWIN has extended the Antoine method to make it applicable to solids by using the same methodology as the modified Grain method to convert a super-cooled liquid VP to a solid-phase VP as shown below.
Modified Grain Method: Chapter 2 of Lyman (1985) describes the modified Grain method used by MPBPWIN. This method is a modification and significant improvement of the modified Watson method. It is applicable to solids, liquids and gases. The modified Grain method may be the best all-around VP estimation method currently available.
Mackay Method: it was derived from two chemical classes: hydrocarbons (aliphatic and aromatic) and halogenated compounds (again aliphatic and aromatic).
MPBPWIN reports the VP estimate from all three methods. It then reports a "suggested" VP. For solids, the modified Grain estimate is the suggested VP. For liquids and gases, the suggested VP is the average of the Antoine and the modified Grain estimates. The Mackay method is not used in the suggested VP because its application is currently limited to its derivation classes.
Estimation Accuracy
The accuracy of MPBPWIN's "suggested" VP estimate was tested on a dataset of 3037 compounds with known, experimental VP values between 15 and 30 °C (the vast majority at 25 or 20 °C). The experimental values were taken from the PHYSPROP Database that is part of the EPI Suite. For this test, the CAS numbers were run through MPBPWIN as a standard batch-mode run (using the default VP estimation temperature of 25 °C) and the batch estimates were compared to PHYSPROP's experimental VP. The estimation error increases as the vapor pressure (both experimental and estimated) decreases, especially when the vapor pressure decreases below 1×10-6 mm Hg (0.0001333 Pascals).
The estimation methodology uses the normal boil point to estimate the liquid-phase vapor pressure. For solids, the melting point is required to convert the liquid-phase vapor pressure to the solid-phase vapor pressure. VP estimation error can be introduced by:
(1) poor Boiling Point estimates or values
(2) poor Melting Point estimates or values (for solids)
The 3037 compound test set contains 1642 compounds with available experimental Boiling points and Melting points ... For this subset of compounds, the estimation accuracy statistics are (based on log VP):
number = 1642
r2 = 0.949
std deviation = 0.59
avg deviation = 0.32
These statistics clearly indicate that VP estimates are more accurate with experimental BP and MP data.
For maximum VP accuracy, good experimental Boiling Points and/or Melting Points should be entered on the data entry screen (or available from the experimental databases included with the EPI Suite).
Availability of Test Data Sets:
The complete vapor pressure test is available at:
http://esc.syrres.com/interkow/EpiSuiteData.htm
Substructure searchable data set of vapor pressure test is available at:
http://esc.syrres.com/interkow/EpiSuiteData_ISIS_SDF.htm
Estimation Domain
Currently there is no universally accepted definition of model domain. However, users may wish to consider the possibility that property estimates are less accurate for compounds outside the Molecular Weight range of the training set compounds, and/or that have more instances of a given fragment than the maximum for all training set compounds. It is also possible that a compound may have a functional group(s) or other structural features not represented in the training set, and for which no fragment coefficient was developed. These points should be taken into consideration when interpreting model results.
The complete training sets for MPBPWIN's estimation methodology are not available. Therefore, describing a precise estimation domain for this methodology is not possible.
The current applicability of the MPBPWIN methodology is best described by its accuracy in predicting vapor pressure as described above in the Accuracy section. - Principles of method if other than guideline:
- Prediction done by EPISuite computational tool.
- Type of method:
- other: modified Grain method
- Temp.:
- 25 °C
- Vapour pressure:
- 0 mm Hg
- Remarks on result:
- other: const. 1
- Temp.:
- 25 °C
- Vapour pressure:
- 0 mm Hg
- Remarks on result:
- other: const. 2
- Temp.:
- 25 °C
- Vapour pressure:
- 0 mm Hg
- Remarks on result:
- other: const. 3
Reference
Description of key information
negligible at 25 °C.
Key value for chemical safety assessment
Additional information
Prediction by EPISuite v.4.11 tool.
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