!1!CHAPTER 1 –Key 1. A transducer is a device that converts chemical or physical information into an electrical signal or the reverse. The most common input transducers convert chemical or physical information into a current, voltage, or charge, and the most common output transducers convert electrical signals into some numerical form. 3. The detector in a spectrograph is a photographic film or plate. 5. A data domain is one of the modes in which data may be encoded. Examples of data domain classes are the analog, digital and time domains. Examples of data domains are voltage, current, charge, frequency, period, number. 7. Output Transducer Use LCD display Alphanumeric information Computer monitor Alphanumeric information, text, graphics Laser printer Alphanumeric and graphical information Motor Rotates to change position of attached elements 10. (a) Slope, m = 0.0701, intercept, b = 0.0083 !"#$%&$'()*!+,!-%*.#/0)%.1(!2%1(3*$*4!5.6!)78! 961'.)#!:!!!;! cx<=;85 >8?>>!0@ >8>=AB!CD!E8>> :> !C;B8EF>8?>>!0@!G!=?8>!0@H F=;85 =?8>!0@Huu uu!!:I:>8!!J6)!#)*/(.*!1#)!*6+K%!$%!.6)!*'#)17*6)).!L)(+K8!!F1H! M(+')4!m!D!>8>B>:4!$%.)#&)'.4!b!D!>8>>A;!FLH! N#+0!@-OPMJ!#)*/(.*4!MQ!*(+')4!sm!D!>8>>>B4!MQ!$%.)#&)'.4!sb!D!>8>><>!F&H! E?R!9-!,+#!*(+')!m!$*!m r!t*m!K6)#)!t!$*!.6)!M./7)%.!t!S1(/)!,+#!E?R!'#+L1L$($.3!1%7!N –!=!D!<!7)T#))*!+,!,#))7+0!D!=8BA!! ! E?R!9-!,+#!m!D!>8>B>:!r!=8BA!u!>8>>>B!D!>8>B>:!r!>8>>:E!+#!>8>B>!r!>8>>=!! N+#!$%.)#&)'.4!E?R!9-!D!b!r!tsb!D!>8>>A;!r!=8BA!u!>8>><!D!>8>>A;!r!>8>::!+#!>8>A!r!>8>:!F7H! cu!D!<8AB!r!>8>A5!0C!+#!<8AB!r!>8>E!0C!!!2! (b) From LINEST results, SD slope, sm = 0.0007, SD intercept, sb = 0.0040 (c) 95% CI for slope m is m ± tsm where t is the Student t value for 95% probability and N –1 = 5 degrees of freedom = 2.57 95% CI for m = 0.0701 ± 2.57 × 0.0007 = 0.0701 ± 0.0018 or 0.070 ± 0.002 For intercept, 95% CI = b ± tsb = 0.0083 ± 2.57 × 0.004 = 0.0083 ± 0.010 or 0.08 ± 0.01 (d) cu =4.87±0.086 mM or 4.87±0.09 mM!3!CHAPTER 5 – Key 1. Frequency dependent noise sources: flicker and environmental noise. Frequency independent sources: thermal and shot noise. 2. (a) Thermal noise. (b) Certain types of environmental noise. (c) Thermal and shot noise. 4. At the high impedance of a glass electrode, shielding is vital to minimize induced currents from power lines which can be amplified and can disturb the output. 6. We estimate the maximum and the minimum in the recorded signal (0.9 × 10-15 A) to be 1.5 × 10-15 and 0.4 × 10-15 A. The standard deviation of the signal is estimated to be one- fifth of the difference or 0.22 × 10-15 A. Thus, 7. (a)! Hence, S/N = 358 for these 9 measurements. (b) Skoog/Holler/Crouch Chapter 5Principles of Instrumental Analysis, 6th ed. Instructor’s Manual 1 CHAPTER 5 5-1. Frequency dependent noise sources: flicker and environmental noise. Frequency independent sources: thermal and shot noise. 5-2. (a) Thermal noise. (b) Certain types of environmental noise. (c) Thermal andshot noise. 5-3. 103 to 105 Hz and 106 to 107 Hz, Enviromental noise is at a minimum in these regions (see Figure 5-3). 5-4. At the high impedance of a glass electrode, shielding is vital to minimize induced currents from power lines which can be amplified and can disturb the output. 5-5. (a) High-pass filters are used to remove low frequency flicker noise from higher frequency analytical signals. (b) Low-pass filters are used to remove high frequency noise from dc analytical signals. 5-6. We estimate the maximum and theminimum in the recorded signal (0.9 u 10–15 A) to be 1.5 u 10–15 and 0.4 u 10–15 A. The standard deviation of the signal is estimated to be one- fifth of the difference or 0.22 u 10–15 A. Thus, 15150.9 10 A40.22 10 ASNu u Principles of Instrumental Analysis, 6th ed. Chapter 5 25-7. (a) Hence, S/N = 358 for these 9 measurements (b) nnSSnNN (Equation 5-11). For the nine measurements, 358 9nnSN For the S/N to be 500 requires nx measurements. That is, 500nxnSnN Dividing the second equation by the first gives, after squaring and rearranging, 2500317.6 or 18 measurements358xn§· u ¨¸©¹!4! 10. To increase the S/N by a factor of 10 requires 102 more measurements. So n = 100. 12. The magnitudes of the signals and the noise in the spectra in Figure 5-15 may be estimated directly from the plots. The results from our estimates are given in the table below. Baselines for spectra A and D are taken from the flat retions on the right side of the figure. Noise is calculated from one-fifth of the peak-to-peak excursions of the signal. Note that the difference in S/N for the two peaks is due only to the difference in the peak heights. So, at 255 nm, (S/N)D =67/17(S/N)A = 3.9(S/N)A; at 425 nm, (S/N)D =79/18(S/N)A = 3.9(S/N)A Principles of Instrumental Analysis, 6th ed. Chapter 5 25-7. (a) Hence, S/N = 358 for these 9 measurements (b) nnSSnNN (Equation 5-11). For the nine measurements, 358 9nnSN For the S/N to be 500 requires nx measurements. That is, 500nxnSnN Dividing the second equation by the first gives, after squaring and rearranging, 2500317.6 or 18 measurements358xn§· u ¨¸©¹ Principles of Instrumental Analysis, 6th ed. Chapter 5 4The bottom spectrum S/N is improved by a factor of 200 = 14.1 over the top spectrum. The bottom spectrum is the result of 200/50 = 4 times as many scans so the S/N should be improved by a factor of 4 = 2 over the middle spectrum 5-12. The magnitudes of the signals and the noise in the spectra in Figure 5-15 may be estimated directly from the plots. The results from our estimates are given in the table below. Baselines for spectra A and D are taken from the flat retions on the right side of the figure. Noise is calculated from one-fifth of the peak-to-peak excursions of the signal. A255 A425 Ab(peak) Ab(valley) Ab(mean) Spectrum A 0.550 0.580 0.080 –0.082 0.001 Spectrum D 1.125 1.150 0.620 0.581 0.600 S255 S425 N = [Ab(peak) – Ab(valley)]/5 (S/N)255 (S/N)425 Spectrum A 0.549 0.579 0.0324 17 18 Spectrum D 0.525 0.550 0.0078