Supplemental material to the article "Optical n(p, T_90) measurement suite 1: He, Ar, and N_2" by PF Egan and Y Yang in International Journal of Thermophysics 2023.
The archive file contains research data and analysis scripts as follows:
* The n(p, T_90) dataset for helium. A Python script analyzes the data to produce the estimate of temperature-dependent compressibility, and reproduces Fig. 2 from the article.
* The n(p, T_90) dataset for argon. A Python script analyzes the data to determine T - T_90, and reproduces Fig. 3 from the article.
Additionally, the output data T - T_90 (Table 2 in the article) are included in a separate text file, together with the estimated uncertainty in the determination. The Python script includes the further analysis undertaken on the nonlinear terms B and C to reproduce Fig. 6.
* The n(p, T_90) dataset for nitrogen. A Python script analyzes the data to reveal the temperature-dependence of polarizability, and reproduce Fig. 4. The script includes the further analysis undertaken on the nonlinear terms B and C to reproduce Fig. 7.
* A Python script uses argon and nitrogen results from the present work to reproduce Fig. 5, a graphical depiction of difference with literature
for the second density virial coefficient B_p.
About this Dataset
| Title | Optical n(p, T_90) measurement suite 1: He, Ar, and N_2 |
|---|---|
| Description | Supplemental material to the article "Optical n(p, T_90) measurement suite 1: He, Ar, and N_2" by PF Egan and Y Yang in International Journal of Thermophysics 2023. The archive file contains research data and analysis scripts as follows: * The n(p, T_90) dataset for helium. A Python script analyzes the data to produce the estimate of temperature-dependent compressibility, and reproduces Fig. 2 from the article. * The n(p, T_90) dataset for argon. A Python script analyzes the data to determine T - T_90, and reproduces Fig. 3 from the article. Additionally, the output data T - T_90 (Table 2 in the article) are included in a separate text file, together with the estimated uncertainty in the determination. The Python script includes the further analysis undertaken on the nonlinear terms B and C to reproduce Fig. 6. * The n(p, T_90) dataset for nitrogen. A Python script analyzes the data to reveal the temperature-dependence of polarizability, and reproduce Fig. 4. The script includes the further analysis undertaken on the nonlinear terms B and C to reproduce Fig. 7. * A Python script uses argon and nitrogen results from the present work to reproduce Fig. 5, a graphical depiction of difference with literature for the second density virial coefficient B_p. |
| Modified | 2023-08-01 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | Refractometry , polarizability , interferometry , thermodynamic metrology |
{
"identifier": "ark:\/88434\/mds2-3054",
"accessLevel": "public",
"contactPoint": {
"hasEmail": "mailto:[email protected]",
"fn": "Patrick Egan"
},
"programCode": [
"006:045"
],
"landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-3054",
"title": "Optical n(p, T_90) measurement suite 1: He, Ar, and N_2",
"description": "Supplemental material to the article \"Optical n(p, T_90) measurement suite 1: He, Ar, and N_2\" by PF Egan and Y Yang in International Journal of Thermophysics 2023.\n\nThe archive file contains research data and analysis scripts as follows:\n\n* The n(p, T_90) dataset for helium. A Python script analyzes the data to produce the estimate of temperature-dependent compressibility, and reproduces Fig. 2 from the article.\n\n* The n(p, T_90) dataset for argon. A Python script analyzes the data to determine T - T_90, and reproduces Fig. 3 from the article. \nAdditionally, the output data T - T_90 (Table 2 in the article) are included in a separate text file, together with the estimated uncertainty in the determination. The Python script includes the further analysis undertaken on the nonlinear terms B and C to reproduce Fig. 6.\n\n* The n(p, T_90) dataset for nitrogen. A Python script analyzes the data to reveal the temperature-dependence of polarizability, and reproduce Fig. 4. The script includes the further analysis undertaken on the nonlinear terms B and C to reproduce Fig. 7.\n\n* A Python script uses argon and nitrogen results from the present work to reproduce Fig. 5, a graphical depiction of difference with literature\n for the second density virial coefficient B_p.\n",
"language": [
"en"
],
"distribution": [
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3106\/egan-npT-supp.zip",
"description": "The n(p,T_90) datasets for helium, argon, and nitrogen, plus the analysis scripts",
"mediaType": "application\/x-zip-compressed",
"title": "Supplemental material to article"
},
{
"downloadURL": "https:\/\/data.nist.gov\/od\/ds\/mds2-3054\/egan-npT-supp.zip",
"mediaType": "application\/zip",
"title": "egan-npT-supp"
}
],
"bureauCode": [
"006:55"
],
"modified": "2023-08-01 00:00:00",
"publisher": {
"@type": "org:Organization",
"name": "National Institute of Standards and Technology"
},
"theme": [
"Metrology:Thermometry metrology",
"Metrology:Pressure and vacuum metrology"
],
"keyword": [
"Refractometry",
"polarizability",
"interferometry",
"thermodynamic metrology"
]
}
