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<h2>SL Paper 3</h2><div class="specification">
<p>The circuit shown may be used to measure the internal resistance of a cell.</p>
<p style="text-align: center;"><img src="images/Schermafbeelding_2017-09-27_om_07.51.02.png" alt="M17/4/PHYSI/SP3/ENG/TZ2/02"></p>
</div>
<div class="specification">
<p>The ammeter used in the experiment in (b) is an analogue meter. The student takes measurements without checking for a “zero error” on the ammeter.</p>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p>An ammeter and a voltmeter are connected in the circuit. Label the ammeter with the letter A and the voltmeter with the letter V.</p>
<div class="marks">[1]</div>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p>In one experiment a student obtains the following graph showing the variation with current <em>I</em> of the potential difference <em>V</em> across the cell.</p>
<p><img src="images/Schermafbeelding_2017-09-27_om_08.05.10.png" alt="M17/4/PHYSI/SP3/ENG/TZ2/02b"></p>
<p>Using the graph, determine the best estimate of the internal resistance of the cell.</p>
<div class="marks">[3]</div>
<div class="question_part_label">b.</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p>State what is meant by a zero error.</p>
<div class="marks">[1]</div>
<div class="question_part_label">c.i.</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p>After taking measurements the student observes that the ammeter has a positive zero error. Explain what effect, if any, this zero error will have on the calculated value of the internal resistance in (b).</p>
<div class="marks">[2]</div>
<div class="question_part_label">c.ii.</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question" style="padding-left: 20px;">
<p>correct labelling of both instruments</p>
<p> </p>
<p><img 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"></p>
<p><em><strong>[1 mark]</strong></em></p>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p><em>V = E – Ir</em></p>
<p>large triangle to find gradient and correct read-offs from the line<br><em><strong>OR</strong></em><br>use of intercept <em>E</em> = 1.5 V and another correct data point</p>
<p>internal resistance = 0.60 Ω</p>
<p><em>For MP1 – do not award if only <span class="mjpage"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="R = \frac{V}{I}">
<mi>R</mi>
<mo>=</mo>
<mfrac>
<mi>V</mi>
<mi>I</mi>
</mfrac>
</math></span> is used.</em></p>
<p><em>For MP2 points at least 1A apart must be used.</em></p>
<p><em>For MP3 accept final answers in the range of 0.55 Ω to 0.65 Ω.</em></p>
<p><strong><em>[3 marks]</em></strong></p>
<div class="question_part_label">b.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p>a non-zero reading when a zero reading is expected/no current is flowing<br><em><strong>OR</strong></em><br>a calibration error</p>
<p> </p>
<p><em>OWTTE</em><br><em>Do not accept just “systematic error”.</em></p>
<p><strong><em>[1 mark]</em></strong></p>
<div class="question_part_label">c.i.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p>the error causes «all» measurements to be high/different/incorrect</p>
<p>effect on calculations/gradient will cancel out<br><em><strong>OR</strong></em><br>effect is that value for <em>r</em> is unchanged</p>
<p><em>Award <strong>[1 max]</strong> for statement of “no effect” without valid argument.</em></p>
<p><em>OWTTE</em></p>
<p><strong><em>[2 marks]</em></strong></p>
<div class="question_part_label">c.ii.</div>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">b.</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">c.i.</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">c.ii.</div>
</div>
<br><hr><br><div class="specification">
<p>An electrical circuit is used during an experiment to measure the current <em>I</em> in a variable resistor of resistance <em>R</em>. The emf of the cell is e and the cell has an internal resistance <em>r</em>.</p>
<p><img style="display: block; margin-left: auto; margin-right: auto;" src="data:image/png;base64,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"></p>
<p>A graph shows the variation of <span class="mjpage"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="\frac{1}{I}">
<mfrac>
<mn>1</mn>
<mi>I</mi>
</mfrac>
</math></span> with <em>R</em>.</p>
<p style="text-align: center;"><img src="data:image/png;base64,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"></p>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p>Show that the gradient of the graph is equal to <span class="mjpage"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="\frac{1}{e}">
<mfrac>
<mn>1</mn>
<mi>e</mi>
</mfrac>
</math></span>.</p>
<div class="marks">[2]</div>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p>State the value of the intercept on the <em>R</em> axis.</p>
<div class="marks">[1]</div>
<div class="question_part_label">b.</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question" style="padding-left: 20px;">
<p>«<em>ε</em> = <em>IR + Ir</em>»</p>
<p><span class="mjpage"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="\frac{1}{I} = \frac{R}{\varepsilon } + \frac{r}{\varepsilon }">
<mfrac>
<mn>1</mn>
<mi>I</mi>
</mfrac>
<mo>=</mo>
<mfrac>
<mi>R</mi>
<mi>ε</mi>
</mfrac>
<mo>+</mo>
<mfrac>
<mi>r</mi>
<mi>ε</mi>
</mfrac>
</math></span></p>
<p>identifies equation with <em>y</em> = <em>mx + c</em></p>
<p><em>«</em>hence <em>m</em> = <span class="mjpage"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="\frac{1}{\varepsilon }">
<mfrac>
<mn>1</mn>
<mi>ε</mi>
</mfrac>
</math></span><em>»</em></p>
<p><em>No mark for stating data booklet equation</em></p>
<p><em>Do not accept working where r is ignored or ε = IR is used</em></p>
<p><em>OWTTE</em></p>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p>«–» <em>r</em></p>
<p><em>Allow answer in words</em></p>
<div class="question_part_label">b.</div>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">b.</div>
</div>
<br><hr><br><div class="specification">
<p><span style="background-color: #ffffff;">A student investigates the electromotive force (emf) <em>ε</em> and internal resistance<em> r</em> of a cell.</span></p>
<p><span style="background-color: #ffffff;"><img src="data:image/png;base64,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" width="213" height="196"></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;">The current <em>I</em> and the terminal potential difference <em>V</em> are measured.<br></span></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;">For this circuit <em>V = ε - Ir</em> .<br></span></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;">The table shows the data collected by the student. The uncertainties for each measurement<br>are shown.</span></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;"><img src="data:image/png;base64,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"></span></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;"><span style="background-color: #ffffff;">The graph shows the data plotted.</span></span></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;"><span style="background-color: #ffffff;"><img src="data:image/png;base64,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"></span></span></span></p>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p><span style="background-color:#ffffff;">The student has plotted error bars for the potential difference. Outline why no error bars are shown for the current.</span></p>
<div class="marks">[1]</div>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p><span style="background-color:#ffffff;">Determine, using the graph, the emf of the cell including the uncertainty for this value. Give your answer to the correct number of significant figures.</span></p>
<div class="marks">[3]</div>
<div class="question_part_label">b.</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p><span style="background-color:#ffffff;">Outline, <strong>without</strong> calculation, how the internal resistance can be determined from this graph.</span></p>
<div class="marks">[2]</div>
<div class="question_part_label">c.</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question" style="padding-left: 20px;">
<p style="text-align:left;"><span style="background-color:#ffffff;">Δ<em>I</em> is too small to be shown/seen<br></span></p>
<p style="text-align:left;"><span style="background-color:#ffffff;"><em><strong>OR</strong></em><br></span></p>
<p><span style="background-color:#ffffff;">Error bar of negligible size compared to error bar in <em>V</em> ✔</span></p>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p style="text-align:left;"><span style="background-color:#ffffff;">evidence that ε can be determined from the y-intercept of the line of best-fit or lines of min and max gradient ✔<br></span></p>
<p style="text-align:left;"><span style="background-color:#ffffff;">states ε=1.59 <em><strong>OR</strong></em> 1.60 <em><strong>OR</strong> </em>1.61V«» ✔<br></span></p>
<p style="text-align:left;"><span style="background-color:#ffffff;">states uncertainty in ε is 0.02 V«» <em><strong>OR</strong></em> 0.03«V» ✔</span></p>
<div class="question_part_label">b.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p style="text-align:left;"><span style="background-color:#ffffff;">determine the gradient «of the line of best-fit» ✔<br></span></p>
<p style="text-align:left;"><span style="background-color:#ffffff;"><em>r</em> is the negative of this gradient ✔</span></p>
<div class="question_part_label">c.</div>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question" style="padding-left: 20px;">
<p>Almost all candidates realised that the uncertainty in I was too small to be shown. A common mistake was to mention that since I is the independent variable the uncertainty is negligible.</p>
<div class="question_part_label">a.</div>
</div>
<div class="question" style="padding-left: 20px;">
<p>The number of candidates who realised that the V intercept was EMF was disappointing. Large numbers of candidates tried to calculate ε using points on the graph, often ending up with unrealistic values. Another common mistake was not giving values of ε and Δε to the correct number of digits - 2 decimal places on this occasion. Very few candidates drew maximum and minimum gradient lines as a way of determining Δε.</p>
<div class="question_part_label">b.</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">c.</div>
</div>
<br><hr><br><div class="specification">
<p><span style="background-color: #ffffff;">The resistance<em> R</em> of a wire of length <em>L</em> can be measured using the circuit shown.</span></p>
<p style="text-align: center;"><span style="background-color: #ffffff;"><img src="data:image/png;base64,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"></span></p>
</div>
<div class="specification">
<p><span style="background-color: #ffffff;">In one experiment the wire has a uniform diameter of <em>d</em> = 0.500 mm. The graph shows data obtained for the variation of <em>R</em> with <em>L</em>.</span></p>
<p><span style="background-color: #ffffff;"><img src="data:image/png;base64,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"></span></p>
<p><span style="background-color: #ffffff;"><span style="background-color: #ffffff;">The gradient of the line of best fit is 6.30 Ω m<sup>–1</sup>.</span></span></p>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p><span style="background-color: #ffffff;">Estimate the resistivity of the material of the wire. Give your answer to an appropriate number of significant figures.</span></p>
<div class="marks">[2]</div>
<div class="question_part_label">a(i).</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p><span style="background-color: #ffffff;">Explain, by reference to the power dissipated in the wire, the advantage of the fixed resistor connected in series with the wire for the measurement of<em> R</em>.</span></p>
<div class="marks">[3]</div>
<div class="question_part_label">a(ii).</div>
</div>
<div class="question" style="padding-left: 20px; padding-right: 20px;">
<p><span style="background-color: #ffffff;">The experiment is repeated using a wire made of the same material but of a larger diameter than the wire in part (a). On the axes in part (a), draw the graph for this second experiment.</span></p>
<div class="marks">[2]</div>
<div class="question_part_label">b.</div>
</div>
<h2 style="margin-top: 1em">Markscheme</h2>
<div class="question" style="padding-left: 20px;">
<p><span style="background-color: #ffffff;">evidence of use of<em> ρ</em> = given gradient × wire area<br><em><strong>OR</strong></em><br>substitution of values from a single data point with wire area ✔</span></p>
<p><span style="background-color: #ffffff;"><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>ρ</mi><mo>=</mo><mo>«</mo><mo>=</mo><mn>6</mn><mo>.</mo><mn>30</mn><mo>×</mo><mi mathvariant="normal">π</mi><mo>×</mo><msup><mfenced><mfrac><mrow><mn>0</mn><mo>.</mo><mn>500</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>-</mo><mn>3</mn></mrow></msup></mrow><mn>2</mn></mfrac></mfenced><mn>2</mn></msup><mo>=</mo><mo>»</mo><mn>1</mn><mo>.</mo><mn>24</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>-</mo><mn>6</mn></mrow></msup><mo> </mo><mo>«</mo><mtext>Ω m</mtext><mo>»</mo></math><span style="display: inline !important;float: none;background-color: #ffffff;color: #000000;font-family: Verdana,Arial,Helvetica,sans-serif;font-size: 14px;font-style: normal;font-variant: normal;font-weight: 400;letter-spacing: normal;text-align: left;text-decoration: none;text-indent: 0px;white-space: normal;">✔</span></span></p>
<p><em><span style="background-color: #ffffff;">NOTE: Check POT is correct. <br>MP2 must be correct to exactly 3 s.f.</span></em></p>
<div class="question_part_label">a(i).</div>
</div>
<div class="question" style="padding-left: 20px;">
<p><span style="background-color: #ffffff;">measurement should be performed at a constant temperature<br><em><strong>OR</strong></em><br>resistance of wire changes with temperature ✔</span></p>
<p><span style="background-color: #ffffff;">series resistance prevents the wire from overheating<br><em><strong>OR</strong></em><br>reduces power dissipated in the wire ✔</span></p>
<p><span style="background-color: #ffffff;">by reducing voltage across/current through the wire ✔</span></p>
<div class="question_part_label">a(ii).</div>
</div>
<div class="question" style="padding-left: 20px;">
<p><span style="background-color: #ffffff;">ANY straight line going through the origin if extrapolated ✔<br>ANY straight line below existing line with smaller gradient ✔</span></p>
<div class="question_part_label">b.</div>
</div>
<h2 style="margin-top: 1em">Examiners report</h2>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">a(i).</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">a(ii).</div>
</div>
<div class="question" style="padding-left: 20px;">
[N/A]
<div class="question_part_label">b.</div>
</div>
<br><hr><br>