TITRATION BINDING ANALYSIS REPORT
Taseen Khan
2024-04-15
ABSTRACT
Binding curves were illustrated from titration data collected from two different titrations performed in the lab: a monoprotic titration and a diprotic titration.
INTRODUCTION
Acid binding behavior is a useful parameter when observing acid-base interactions in solution. Binding curves depict the fraction of bound species relative to concentration of titrant added, which a titration curve itself does not illustrate. In this report, titration data collected from lab experiments will be used to illustrate binding curves for monoprotic and diprotic acids. By analyzing said curves, the binding behavior of these different kinds of acids can be observed.
EXPERIMENT
In the procedure of a monoprotic titration, the concentration of an acid which was previously unknown is determined by gradually adding a solution of known concentration, 0.1 moles per liter of sodium hydroxide, to the unknown and tracking changes in pH with a potentiometer, which will allow for the illustration of three distinct regions of a graph depicting the rise in pH over a gradient, the pH at the equivalence point, and a sharp pH increase when base is in excess, and by analyzing the titrant volume used we can determine the concentration of acid. In the diprotic titration, the objective is also to observe the pH changes but two equivalence points are observed, which correspond to two acidic protons being neutralized by the addition of the titrant.
ANALYSIS
Monoprotic Titration
mydata <- read.csv("trial2.csv")
print(mydata)## Vol pH
## 1 0 3.550
## 2 1 3.775
## 3 2 3.945
## 4 3 4.107
## 5 4 4.231
## 6 5 4.341
## 7 6 4.440
## 8 7 4.537
## 9 8 4.624
## 10 9 4.705
## 11 10 4.790
## 12 11 4.870
## 13 12 4.957
## 14 13 5.045
## 15 14 5.142
## 16 15 5.258
## 17 16 5.387
## 18 17 5.542
## 19 18 5.767
## 20 19 6.192str(mydata)## 'data.frame': 20 obs. of 2 variables:
## $ Vol: int 0 1 2 3 4 5 6 7 8 9 ...
## $ pH : num 3.55 3.77 3.94 4.11 4.23 ...plot(mydata)
Diprotic Titration
mydata2 <- read.csv("autotitrator trial 2(Sheet1).csv")
print(mydata2)## Consumption Potential
## 1 0.000 2.16
## 2 0.005 2.16
## 3 0.010 2.15
## 4 0.023 2.15
## 5 0.054 2.15
## 6 0.132 2.15
## 7 0.328 2.16
## 8 0.528 2.16
## 9 0.728 2.18
## 10 0.928 2.23
## 11 1.128 2.25
## 12 1.328 2.29
## 13 1.528 2.31
## 14 1.728 2.35
## 15 1.928 2.39
## 16 2.128 2.43
## 17 2.328 2.44
## 18 2.528 2.50
## 19 2.728 2.54
## 20 2.928 2.58
## 21 3.129 2.59
## 22 3.329 2.65
## 23 3.529 2.70
## 24 3.729 2.71
## 25 3.929 2.77
## 26 4.129 2.81
## 27 4.329 2.86
## 28 4.529 2.91
## 29 4.729 2.95
## 30 4.929 3.00
## 31 5.129 3.05
## 32 5.329 3.10
## 33 5.529 3.16
## 34 5.729 3.22
## 35 5.929 3.30
## 36 6.129 3.36
## 37 6.329 3.45
## 38 6.529 3.54
## 39 6.729 3.65
## 40 6.929 3.77
## 41 7.129 3.89
## 42 7.329 4.02
## 43 7.529 4.18
## 44 7.729 4.30
## 45 7.929 4.44
## 46 8.129 4.55
## 47 8.329 4.64
## 48 8.529 4.72
## 49 8.729 4.80
## 50 8.929 4.89
## 51 9.129 4.95
## 52 9.329 5.02
## 53 9.529 5.08
## 54 9.730 5.14
## 55 9.930 5.19
## 56 10.130 5.24
## 57 10.330 5.29
## 58 10.530 5.34
## 59 10.730 5.39
## 60 10.930 5.43
## 61 11.130 5.48
## 62 11.330 5.52
## 63 11.530 5.57
## 64 11.730 5.61
## 65 11.930 5.66
## 66 12.130 5.70
## 67 12.330 5.75
## 68 12.530 5.80
## 69 12.730 5.85
## 70 12.930 5.89
## 71 13.130 5.95
## 72 13.330 6.00
## 73 13.530 6.06
## 74 13.730 6.12
## 75 13.930 6.20
## 76 14.130 6.27
## 77 14.330 6.34
## 78 14.530 6.42
## 79 14.730 6.52
## 80 14.930 6.62
## 81 15.130 6.73
## 82 15.330 6.88
## 83 15.530 7.10
## 84 15.664 7.28
## 85 15.785 7.50
## 86 15.855 7.59
## 87 16.030 7.85
## 88 16.142 8.08
## 89 16.204 8.28
## 90 16.242 8.40
## 91 16.301 8.60
## 92 16.347 8.72
## 93 16.440 8.96
## 94 16.511 9.10
## 95 16.635 9.30
## 96 16.774 9.47
## 97 16.968 9.65
## 98 17.168 9.80
## 99 17.368 9.95str(mydata2)## 'data.frame': 99 obs. of 2 variables:
## $ Consumption: num 0 0.005 0.01 0.023 0.054 0.132 0.328 0.528 0.728 0.928 ...
## $ Potential : num 2.16 2.16 2.15 2.15 2.15 2.15 2.16 2.16 2.18 2.23 ...plot(mydata2)
DISCUSSION
Titration data, typically showing pH versus volume of titrant added, is transformed into a binding curve by focusing on the equivalence point region. Here, pH values are converted to the fraction bound species based on the pKa. The fraction bound is then plotted against the corresponding concentration derived from titrant volume to reveal the relationship between titrant volume and percentage of fraction bound species, resulting in a binding curve.
The equation that describes binding curves is:
$ F = 1 - (CV + H)/(CV_f) $
where F is the fraction bound, C is titrant concentration, [H] is concentration of protons, and V is volume.
Monoprotic Titration
Fraction-bound Curve
mydata <- read.csv("trial2.csv")
vol <- mydata$Vol
pH <- mydata$pH
H <- 10^-(pH)
bc <- 0.01
ivol <- 25
fvol <- 20
fb <- 1 - (bc*vol + H* (ivol + fvol))/(bc * fvol)
plot(pH, fb)
The Ka determined in the lab was 1.993E-10.
Diprotic Titration
Fraction-bound Curve
mydata2 <- read.csv("autotitrator trial 2(Sheet1).csv")
vol2 <- mydata2$Consumption
pH2 <- mydata2$Potential
H <- 10^-(pH)
bc2 <- 0.01
ivol2 <- 25
fvol2 <- 20
fb2 <- 1 - (bc2*vol2 + H* (ivol2 + fvol2))/(bc2 * fvol2) ## Warning in bc2 * vol2 + H * (ivol2 + fvol2): longer object length is not a
## multiple of shorter object lengthplot(pH2, fb2)
Ka1 and Ka2 were determined to be 8.81E-5 and 1.148E-8 respectively in the lab.
The benefit of a binding curve or a titration curve is you are able to get more detailed information on the behavior of how a reactant species is interacting in solution while being neutralized but it is more difficult to ascertain relative to a titration curve.
Binding curves can be a useful tool when observing reagent behavior in solutions at a more granular level and have been used in pharmacological and biological testing and experimentation. Using computers, binding curves can be used for chemical analysis to demonstrate more characteristics of reagents.