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Normality can be used for acid-base titrations. For example, sulfuric acid (H 2 SO 4) is a diprotic acid. Since only 0.5 mol of H 2 SO 4 are needed to neutralize 1 mol of OH −, the equivalence factor is: f eq (H 2 SO 4) = 0.5. If the concentration of a sulfuric acid solution is c(H 2 SO 4) = 1 mol/L, then its normality is 2 N. It can also be ...
Diagram showing the cumulative distribution function for the normal distribution with mean (μ) 0 and variance (σ 2) 1. These numerical values "68%, 95%, 99.7%" come from the cumulative distribution function of the normal distribution.
In analytical chemistry, a solution of any substance which contains one equivalent per litre is known as a normal solution (abbreviated N), so the example sodium hydroxide solution would be 0.0893 N. [3] [13] The relative uncertainty (u r) in the measured concentration can be estimated by assuming a Gaussian distribution of the measurement ...
Near 0 °Bé would be approximately the density of water. −100 °Bé (specific gravity, 0.615) would be among the lightest fluids known, such as liquid butane. Thus, the system could be understood as representing a practical spectrum of the density of liquids between −100 and 100, with values near 0 being the approximate density of water.
Here i is the van 't Hoff factor as above, K b is the ebullioscopic constant of the solvent (equal to 0.512 °C kg/mol for water), and m is the molality of the solution. The boiling point is the temperature at which there is equilibrium between liquid and gas phases.
Using the number density of an ideal gas at 0 °C and 1 atm as a yardstick: n 0 = 1 amg = 2.686 7774 × 10 25 m −3 is often introduced as a unit of number density, for any substances at any conditions (not necessarily limited to an ideal gas at 0 °C and 1 atm). [3]
At 15.6 °C (60.1 °F), the density of a saturated solution is 0.88 g/ml; it contains 35.6% ammonia by mass, 308 grams of ammonia per litre of solution, and has a molarity of approximately 18 mol/L. At higher temperatures, the molarity of the saturated solution decreases and the density increases. [8]
Bayes' theorem says that the posterior probability measure is proportional to the product of the prior probability measure and the likelihood function. Proportional to implies that one must multiply or divide by a normalizing constant to assign measure 1 to the whole space, i.e., to get a probability measure.