(a) To identify a liquid substance, a student determined its density. Using a graduated cylinder, she measured out a 45-mL sample of the substance. She then measured the mass of the sample, finding that it weighed 38.5 g. She knew that the substance had to be either isopropyl alcohol (density 0.785 g/mL) or toluene (density 0.866 g/mL). What are the calculated density and the probable identity of the substance? (b) An experiment requires 45.0 g of ethylene glycol, a liquid whose density is 1.114 g/mL. Rather than weigh the sample on a balance, a chemist chooses to dispense the liquid using a graduated cylinder. What volume of the liquid should he use? (c) Is a graduated cylinder such as that shown in Figure 1.19 likely to afford the accuracy of measurement needed? (d) A cubic piece of metal measures 5.00 cm on each edge. If the metal is nickel, whose density is 8.90 g/cm3, what is the mass of the cube?
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)
Figure .19.
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYsAAAAcBAMAAABi2o73AAAACXBIWXMAAABkAAAAZAAPlsXdAAAAKlBMVEVHcEwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHrpZrAAAADnRSTlMAiERVEWahMyK7d/XM3YXNrewAAAWcSURBVFjD1Vjdb9NWFP/5I7YTUslJydjal7SQlnRMcpJRiY8Hl1XQFU1yYAWJD8lKh4rYizM6NFaQPHVUaAIplKFqPGWQdWNPASHYxB4i2KCPYUJjk/bH7Fy7pU7iOt0o6rhVkutzju/9/e49H/cWWIvGZ68hN2AA3F8P8fq2DdYuqDABYUx/DeAqNrBF54eb5YJ+C88ZDf7/jZ9/nJ3LmYgP6p/vmaukW/TiHLpjFvHp3chYdSaCh8tkjPXh8Qau4Ffc58qdyGuhFvWnm7D3F6IhYwc9pTEevCglSVsfGjHM0l9oUD3/eN/fH7Xqdw2bQ3edMCG/O4FngYNJRoe+jjTEWU7Vi1/LbzVrz2AqpaEATFG0Az8pteCMYF/COtKIlLh4Bad0qVl7XH972sCbglXEeXqsRtRgGvh2nUL8m9QfPfcuPspci23OxHPNam7yBDoFTVIjn83R49ixYNcPjT1aSn1rgE10HHQoFvtw1QkX0G3RZ+5PbMhbnY4zZpPrJ52VoM++JclS7FQgJv3X7fvVSyvO932kLY9QruH0yy0Pp/U1PO/sYak4+x24nOtsnby5WG+SGNthvJh3GQHOdOGcdwwXUrPUbUPOS1UIpUZ54uVoRHKVhueaWCdukJCguVi7uXNxt8JQVKHqN0YX/CB1+c/HdiFiQmwaaWFt46qEJ+RnliS2AC5Cvos7fi8V4Aep4DuBzPZYKoOvionUhXHa8lC2Mm9lnm6Zt86sGQ2uRFkO3O1N/MO83ai6yRyLkTs4IM5bVPyVQ7kM9TB0WxudQOSAyuVMBizzlIWQKyWz0QnFvDBxDEMHjFC2jJJzrEOoLhkR7XexhBk5ISUFNSQlKSV+EaNGFr1+jQJaYXpaCT5Y79IQP37CUT1RPCb9iDPHothQzCJNScVf6Ei+Tz2gDqOALqGUQJ0BE9ygcqRkZkxEVO35V1RnyzO0Fyyqpmgkdaq3MlITTOy/Ln5AIUpfnv2L+bW4Z7mD9S6NvWL3aB13eI9Jgskdrxd/LtOUVPzFSxikHigHDNeVKpQqbzJgnJPBHSkzG65LyZEa5DsD+v7rcHZjHnhHp8NeSJUMiLuNPKI68qg6qTjWFZSwGZZyW73gxMZBSL215jggroJ2kTrv/UZTsuKfJ5R5CljybzVSosDlDAYs6uQER8rMQuoG1qOoY4id3TgF+QnOgpe0DkGuCsaCFuXtmFxynaU/yOuZZ1i+mmltWS/WUBes7eDsKu42mhCNfmjMI7ZjQWPFf5ycaEED+VV0moCm1Og0A0aQWBgzKTsjcOpZgU6XBLKfELPdKN6LxXRw2cMderhfvLEdV7VwFn19rceJnmRAWRQbCrJiDnqejkSovnNaDoMzRqNJEcqDB5SNRzcbNC8r/s8wa13VELURzqGQmiwc6mXACBI1V0pmfOFWhZXYztQkIVaWsX7JoOiQdfZrY3KmdZW5lQ9K0ebbq5X3ktq6WOjdjsfkxeFs0jkhEDIbw06PZGRPgGAzYKKT4lwpMxsRdbZsioM4Yq2U6mt9+Dc0jjXfXgWzbR5mJkLSv/hfzvi94UqbzwiexWgJzoMtkZtsoCE2LICSXrq9voCVXrkaek0q/sV/1Pdy6EqbzwjAxtXVr6PmJXOsaHDqNvVoctt4+vQgfuzzLr4QXry95gdcfai8YcXTbfyGx0ReixOuvTq7kBmh1LbAUYIIJzkzrEb3UMrzGLwbdm+vSp0rO/qRH1Y8ll1GVzuTV9TEZ9PkUNcXadAn3FNIeWgoVti9vXImiyDSJ3l95Ut9W5NX1U6QQ+GKl0ZDtIdi3XHn9rpMI+h/E21NXlU7Z/A1/MloRF0aVI+1xlupc3tV6tDaYbwMYb1odNg40mOkZ5XC7pPp2eOF7pP5lDeulO64e3vNpyxXHxBo8cPtTP5D+wcCNUanJwqD/AAAAABJRU5ErkJggg==)
Based on this result, the compound is the most likely toluene.
![](data:image/png;base64,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)
c) The accuracy of graduated cylinder is not enough this measurement, as the uncertainty in the volume measured with this flask is ±1 mL, and we need a flask with ±0.1 mL accuracy.
![](data:image/png;base64,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)
![](data:image/png;base64,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)
(a) To identify a liquid substance, a student determined its density. Using a graduated cylinder, she measured out a 45-mL sample of the substance. She then measured the mass of the sample, finding that it weighed 38.5 g. She knew that the substance had to be either isopropyl alcohol (density 0.785 g/mL) or toluene (density 0.866 g/mL). What are the calculated density and the probable identity of the substance? (b) An experiment requires 45.0 g of ethylene glycol, a liquid whose density is 1.114 g/mL. Rather than weigh the sample on a balance, a chemist chooses to dispense the liquid using a graduated cylinder. What volume of the liquid should he use? (c) Is a graduated cylinder such as that shown in Figure 1.19 likely to afford the accuracy of measurement needed? (d) A cubic piece of metal measures 5.00 cm on each edge. If the metal is nickel, whose density is 8.90 g/cm3, what is the mass of the cube?
Figure .19.
Based on this result, the compound is the most likely toluene.
c) The accuracy of graduated cylinder is not enough this measurement, as the uncertainty in the volume measured with this flask is ±1 mL, and we need a flask with ±0.1 mL accuracy.