IB Mathematics AHL 5.9 The derivatives of sin x-AI HL Paper 2- Exam Style Questions- New Syllabus
A student investigating the relationship between chemical reactions and temperature finds the Arrhenius equation on the internet:
\(k = A e^{-\frac{c}{T}}\)
This equation links a variable \(k\) with the temperature \(T\), where \(A\) and \(c\) are positive constants and \(T > 0\).
(a) Show that \(\frac{dk}{dT}\) is always positive:
(b) Given that \(\lim_{T \to \infty} k = A\) and \(\lim_{T \to 0} k = 0\), sketch the graph of \(k\) against \(T\):
(c) (i) The Arrhenius equation predicts that the graph of \(\ln k\) against \(\frac{1}{T}\) is a straight line. Write down the gradient of this line in terms of \(c\):
(ii) Write down the y-intercept of this line in terms of \(A\):
(d) The following data are found for a particular reaction, where \(T\) is measured in Kelvin and \(k\) is measured in cm3 mol-1 s-1:
\(T\) | \(k\) |
---|---|
590 | \(5 \times 10^{-4}\) |
600 | \(6 \times 10^{-4}\) |
610 | \(10 \times 10^{-4}\) |
620 | \(14 \times 10^{-4}\) |
630 | \(20 \times 10^{-4}\) |
640 | \(29 \times 10^{-4}\) |
650 | \(36 \times 10^{-4}\) |
Find the equation of the regression line for \(\ln k\) on \(\frac{1}{T}\):
(e) (i) Find an estimate of \(c\):
(ii) Find an estimate of \(A\):
▶️ Answer/Explanation
(a)
\(k = A e^{-\frac{c}{T}}\)
Differentiate using chain rule: \(\frac{dk}{dT} = A \cdot e^{-\frac{c}{T}} \cdot \frac{d}{dT} \left(-\frac{c}{T}\right)\)
\(\frac{d}{dT} \left(-\frac{c}{T}\right) = -c \cdot (-T^{-2}) = \frac{c}{T^2}\)
\(\frac{dk}{dT} = A e^{-\frac{c}{T}} \cdot \frac{c}{T^2}\)
Since \(A > 0\), \(c > 0\), \(T > 0\), and \(e^{-\frac{c}{T}} > 0\), \(\frac{dk}{dT} > 0\)
Result:
\(\frac{dk}{dT}\) is always positive
(b)
As \(T \to 0\), \(\frac{c}{T} \to \infty\), \(e^{-\frac{c}{T}} \to 0\), so \(k \to 0\)
As \(T \to \infty\), \(\frac{c}{T} \to 0\), \(e^0 = 1\), so \(k \to A\)
From (a), \(\frac{dk}{dT} > 0\), so \(k\) increases with \(T\)
Result:
Graph: Increasing curve, starts at (0, 0), approaches \(k = A\) asymptotically
(c)(i)
\(\ln k = \ln (A e^{-\frac{c}{T}}) = \ln A – \frac{c}{T}\)
Let \(x = \frac{1}{T}\), so \(\ln k = -c x + \ln A\)
Gradient is \(-c\)
Result:
\(-c\)
(c)(ii)
From \(\ln k = -c \cdot \frac{1}{T} + \ln A\)
Y-intercept when \(\frac{1}{T} = 0\) is \(\ln A\)
Result:
\(\ln A\)
(d)
Convert data: e.g., \(T = 590\), \(k = 5 \times 10^{-4}\), \(\frac{1}{T} = \frac{1}{590} \approx 0.001695\), \(\ln k = \ln (5 \times 10^{-4}) \approx -7.601\)
Regression line: \(\ln k = m \cdot \frac{1}{T} + b\)
From data, slope \(m \approx -13383.1 \approx -13400\), intercept \(b \approx 15.0107 \approx 15.0\)
Result:
\(\ln k = -13400 \cdot \frac{1}{T} + 15.0\)
(e)(i)
From (c)(i), gradient = \(-c\)
From (d), gradient \(\approx -13400\), so \(c \approx 13400\) (or 13383.1 exactly)
Result:
13400
(e)(ii)
From (c)(ii), y-intercept = \(\ln A\)
From (d), y-intercept \(\approx 15.0\) (or 15.0107), so \(\ln A \approx 15.0\)
\(A = e^{15.0} \approx 3,269,017 \approx 3,300,000\) (or \(e^{15.0107} \approx 3,304,258\))
Result:
3300000