1. Universit´e Paris-Diderot Paris 7
M1 Magist`ere de Physique Fondamentale
Internship report
Photoconductive Emitters and Application to
Amino Acid Spectroscopy
University of Leeds
Author
Thanh-Quy Nguyen
Supervisor
Prof. Edmund H. Linfield
Reporter
Dr. Angela Vasanelli
August 30th, 2013
2. Acknowledgements
First and foremost, my thanks go to Dr. Angela Vasanelli who enabled me to spend the three last months in England
by finding this internship for me: it has been a really rewarding trip, as much from a study and work point of view
than as a living exprience. This internship wouldn’t have happened without Professor Edmund Linfield either, who
accepted me for this internship and who always kept an eye on me to make sure my experience to be as rich as
possible and adapted to my needs.
My most sincere thanks go to Dr. Andrew Burnett who supervised the major part of my internship and managed to
be available despite a considerable amount of work of his own: he took the time to explain to me and my internship
mate, Oliver Peel, about the theory and the experiments of spectroscopy... And to remind me a few memories about
chemistry and the amino acids.
I also thank Dr. Joshua Freeman who supervised the last part of my internship, even if it was a little short and I
was a little busy working on my report at this time. I’d like to thank Anthony Stark and Christopher Russel as well
who advised me on the bibliography to help me writing my report.
A few thanks should also go to my internship mate Oliver Peel who helped me during the transition France-England
and whose knowledge were very complementary to mine and whose motivation and personal investment were an
everyday example.
I would like to thank all the PhD students of the Institute of Microwaves and Photonics who all played a role during
this internship: Dong Rui, Victor Doychinov, Reshma Mohandas, Mussa Elsaadi and last but not least, for allowing
my internship mate and I to go in the clean room to attend the fabrication of a photoconductive emitter, Siddhant
Chowdhury.
Finally, I thank Camille Perbost and Audric Husson who took the time to read my report and then helped to really
improve it.
ii
3. Abstract
Terahertz (THz) technology is a rapidly growing field with many applications. A few of them are used in the Terahertz
Laboratory of the Institute of Microwaves and Photonics (IMP) of the University of Leeds where my internship took
place under the supervision of Professor Edmund Linfield, Dr. Andrew Burnett and Dr. Joshua Freeman.
The IMP is carrying out various research programmes such as development of THz quantum cascade lasers, including
their design, growth, fabrication and measurement, development of THz quantum cascade laser based on imaging and
spectroscopy systems, development of on-chip, guided-wave, THz filter systems to investigate, inter alia, biological
systems such as DNA, development of superlattice electron device (SLED) and two-colour laser CW THz sources, use
of broadband THz systems to understand the fundamental interactions of THz radiation with materials of security
interest, such as drugs-of-abuse and explosives, and more.
During my internship I undertook a number of studies including the use of a broadband THz-TDS system to analyse
the THz frequency response of amino acids , the characterisation of newly fabricated photoconductive emitters within
a narrowband THz-TDS system, and engineering with computer assisted design and the fabrication of a pre-amplifier.
This last work will not be presented in this report.
This report is broken down into a number of sections. In the first chapter, terahertz radiation and its applications
are introduced. A non-exhaustive description of the theory is given, as is a description of the narrowband and the
broadband systems used in the following chapters.
Chapter two describes the characterisation of THz photoconductive emitters to understand the origin of unexpected
oscillations in their response to laser excitation in recent wafers that have been grown. The information in this chapter
has also been used to improve both the wafer growth conditions and also to calibrate the recently recomissioned rapid
thermal annealer.
Finally, chapter three describes the major part of my internship work: the THz spectroscopy of amino acids in a
broadband THz-TDS system and the analysis of the results. Several aspects of the spectroscopy have been analysed,
such as the influence of the matrix surrounding the amino acid on the spectra, the influence of sample concentration,
the acidity and the temperature of the sample, by cooling down the sample from 290K to 4K. Thanks to multi-peaks
fits, the absorption peaks of every spectra can also be analysed.
iii
7. LIST OF FIGURES LIST OF FIGURES
3.15 Acid amino acid spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.16 Basic amino acid spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.17 Neutral amino acid spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.18 Influence of the temperature on the potential shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.19 Frequency shift and width of the peaks change for isoleucine (neutral) and arginine (basic) . . . . . . 28
3.20 Average of the frequency shifts and the width changes of every sample . . . . . . . . . . . . . . . . . . 29
vii
8. List of abreviations
A Absorption index
Al Aluminium atomic symbol
Au Gold atomic symbol
DR Dynamic Range
Far-FTIR Far Infra-Red Fourier Transform
FFT Fast Fourier Transform
FIR Far Infra-Red
fs Femtosecond = 10−15
second
GaAs Gallium Arsenide
I/V Current/Voltage
IMP Institute of Microwaves and Photonics, department at the University of Leeds
kHz KiloHertz = 106
Hertz
L- Indicates a “left-orientation” for the stereochemistry of a molecule
LT-GaAs Low temperature grown gallium arsenide
n Refractive index
N Nitrogen atomic symbol
NF Noise floor
PE Polyethylene
PTFE Polytetrafluoroethylene
QCL Quantum Cascade Lasers
R Reflection index or carbon chain, depending on the situation
THz Terahertz = 1012
Hertz
THz-TDS Terahertz Time Domain Spectroscopy
Ti Titanium atomic symbol
TMA Tetramethylammonium
TMA-B Tetramethylammonium bromide
λ/4 plate Quarter-wave plate
viii
9. Chapter 1
Terahertz radiations and applications
1.1 Introduction
1.1.1 Terahertz radiations
Figure 1.1 – Terahertz spectrum frequency and wavelength
Terahertz radiation is a form of electromagnetic waves that falls in between the infrared and the microwave region
of the electromagnetism spectrum and it shares some properties with each of these. It can, for example, penetrate
clothing, paper, cardboard, wood, masonry, plastic and ceramics like microwaves but cannot penetrate liquid water
or metal. Because of the difficulty of both generating and detecting THz radiation, this part of the electromagnetism
spectrum has been largely ignored [1, 2]. This chapter will introduce a number of devices to generate such radiation,
the detection setup for these and show the entire setups applied to spectroscopy.
1.1.2 Sources
As of 2012 [3], viable sources of terahertz radiation are numerous: gyrotrons, backward wave oscillators, far infrared
lasers, free electron lasers, synchrotron light sources, resonant tuneling diodes. The THz sources used in Leeds
include:
1
10. 1.2. PHOTOCONDUCTIVE EMITTERS CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS
ˆ Photomixing setups: photomixing is achieved thanks to two lasers mixed together and focused onto a pho-
tomixer device which generates the terahertz radiation. The advantages of this technique are a large frequency
range (from 300 GHz to 3 THz) and high spectral resolutions (up to 1 MHz). However, the achievable power
is on the order of 10−8
W [4]. These were not part of my internship.
ˆ Quantum cascade lasers (QCLs): they are semiconductor lasers that emit in the mid- to far-infrared in the
electromagnetic spectrum. Unlike typical interband semiconductor lasers that emit electromagnetic radiation
through the recombination of electron–hole pairs across the material band gap, QCLs are unipolar and laser
emission is achieved through the use of an intersubband transition in a repeated stack of semiconductor multiple
quantum well heterostructures. The advantage of this technique compared with the others is the achievable
power (a few Watts) [5].
ˆ Photoconductive (Auston) switches: these are the key system used in this internship and are much laregely
described in section 1.2.
1.1.3 Terahertz spectroscopy
Over the past three decades, a new spectroscopic technique using these terahertz sources with applications in many
fields such as security [6, 7], art conservation [8, 9, 10, 11, 12] and pharmacology [13, 14, 15, 16, 17] has emerged. The
development of the femtosecond laser in the 1980s enabled what is now known as terahertz time-domain spectroscopy
(THz-TDS) of which the method was first described in 1989 by Grischkowsky et al., using optical excitation of
photoconductive dipole antennas [18, 19]. The work done during this internship will use THz-TDS to characterise
photoconductive switches (chapter 2) that are described in section 1.2 and to make an analysis of optical properties
of amino acids (chapter 3).
More details of the principle of THz spectroscopy can be found within the description of the detection (section 1.3)
and the terahertz spectroscopy setups (section 1.4).
1.2 Photoconductive emitters
1.2.1 Principle
Figure 1.2 – A photoconductive emitter [20]
A photoconductive emitter is made of two electrodes separated by a few microns at the top of a photoconducting
material forming a photoconductive switch (figure 1.2). When biased, the electrode contacts can be momentarily
closed by a short (<150 fs) excitation laser pulse such that an intense transient current is generated and a subpi-
cosecond electromagnetic pulse with frequency components in the THz range is transmitted into free space from the
switch [21].
2
11. CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS 1.3. DETECTION
1.2.2 Fabrication by photolithography
The photoconductive emitters are fabricated by photolithography in a clean room to avoid the impurities. Figure 1.3
describes such a fabrication step by step.
Figure 1.3 – Fabrication of a photoconductive emitter by photolithography
1.3 Detection
1.3.1 Electro-optic crystal
The electro-optic crystal is the first part of the detection setup that is common to our narrowband (section 1.4.1)
and broadband (section 1.4.2) systems. It is a crystal that when hit by a THz electromagnetic pulse (playing the
role of an electric field) coming from the photoconductive emitter, a birefringence is exhibited and the probe beam
going through at the same time gets its polarisation modified. This is referred to as the Pockels effect [22].
The choice of the electro-optic crystal in the setup must be done depending on whether we want to obtain the widest
bandwidth, the highest frequency resolution, or something in between [23]. Thickness of crystal is the important
thing for bandwidth due to the phase matching.
1.3.2 Lock-in detection scheme
Figure 1.4 describes the detection setup and the evolution of the polarisation of the beam [24, 25].
3
12. 1.4. THZ SPECTROSCOPY SETUPS CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS
Figure 1.4 – Lock-in detection scheme
After getting its polarisation modified by the electro-optic crystal, the polarisation of the incident beam is still linear.
The quarter-wave plate then transforms this linear polarisation into an ellipsoid one, and the Wollaston prism (which
is actually not a “triangle”, but two of them called calcite prisms cemented together on their base [26]) then splits
the beam into two of them to measure variable values I1 and I2 as described in figure 1.4. I2 − I1 is then calculated
thanks to the balanced photodiodes to get back to the terahertz signal as:
I2 − I1 ∝ ET Hz (1.1)
Where ET Hz is the electric field of the terahertz wave [27]. The electric-field of the THz wave changes as a result
of delaying the pump beam, the part of the following ellipsoid polarisation changes too, and so do the values of I1
and I2. By controlling which part of the THz pulse hits the electro-optic crystal at the same time that our incident
probe beam, the full electric field of the THz pulse can be obtained.
1.4 THz spectroscopy setups
Two spectroscopy setups using a photoconductive emitter and electro-optic detection system (described in section 1.3)
are used in the project: the narrowband and the broadband systems. This section will describe both of these
(sections 1.4.1 and 1.4.2) before comparing them (section 1.4.3). Then, the data that can be obtained from them will
be introduced in (section 1.4.4).
1.4.1 Narrowband system
Figure 1.5 shows a schematic representation of the narrowband system that is used for photoconductive emitter
characterisation (chapter 2).
4
13. CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS 1.4. THZ SPECTROSCOPY SETUPS
Figure 1.5 – Scheme of the narrowband system
In order to create the pulse laser to excite the THz emitter, a Ti:sapphire pump laser is excited by a 532 nm
wavelength diode laser and provides near-infrared pulses of ∼100 fs duration at a centre wavelength of 788 nm with
a repetition rate of 76 MHz. The output beam is split into a pump beam (∼ 400 mW) and a probe beam (∼ 40
mW) by a beam splitter. The pump beam is then focused onto the gap of a photoconductive emitter to excite it.
The THz emitter is biased with a 10 kHz, bipolar (± 100 V) square wave. In practice, after dispersion by optical
components, the pulse width of the laser incident on the emitter is ∼150 fs.
For this system, the THz radiation generated from the emitter is collected in a frontwards geometry (see figure 1.5),
even though this is not the best choice. It is then collected by parabolic mirrors and focused onto the electro-optic
crystal which composes the first part of the detection scheme (cf. section 1.3.2). To explore the entire width of the
terahertz pulse, the motorised optical delay stage is here to control the delay line between the probe beam and the
terahertz wave generated from the pump beam [23].
The photoconductive emitter and therefore, the terahertz waves emitted from it are placed in a box purged with N2
during the measurements to avoid the reflection due to the water vapour in the air. An electrical input is also here
to provide the bias voltage to the THz emitter.
1.4.2 Broadband system
Figure 1.6 shows a schematic of the broadband system used for the amino acid spectroscopy (chapter 3) [23].
Using the same fundamental configuration, the broadband system is equipped with a different laser: the Ti:sapphire
laser which provides ∼12 fs duration pulses (instead of 100 for the narrowband). On the other hand, the pump beam
now has a 330 mW power.
Besides, two important things are important to notice: first, because this system is used to obtain spectra with a
high frequency bandwidth, the THz radiation generated from the emitter is now collected in backwards geometry
(i.e., from the same surface of the emitter that is excited by the laser) to avoid absorption and dispersion in the
undoped GaAs substrate and to observe the high frequency components more efficiently. Second, because this system
is also used to analyse samples, the THz wave is focused on a sample place that can be cooled down to 4K thanks to
a helium continuous flow cryostat. Also, to avoid thermal conductivity from the air to the sample, the N2 port has
also been replaced by a purging port from which a vacuum can be made in the box.
5
14. 1.4. THZ SPECTROSCOPY SETUPS CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS
Figure 1.6 – Scheme of the broadband system
1.4.3 Comparison of systems
The main difference between the two systems is that of the laser that can provide a larger frequency bandwidth
produced by the THz emitter for the broadband.
The second major difference is the choice of the electro-optic crystal that both changes the detectable frequency
bandwidth and also effects the achievable frequency resolution as well: for broadband THz spectroscopy studies, a
150 µm sthick GaP crystal is used, leading to the results hereabove. Still for the broadband system, to observe finer
changes in spectral features, a 0.5 mm thick ZnTe wedged crystal mounted on a 4 mm thick ZnTe substrate can be
used. This crystal would provide a 40 GHz resolution with a bandwidth of 0.3–4 THz.
Considering the laser and the crystal used for each system, table 1.1 gives the characteristics of the two systems:
Bandwidth Resolution
Narrowband system 0.1–2.5 THz 30 GHz
Broadband system 0.3–7.5 THz 160 GHz
Table 1.1 – Narrowband/broadband system comparison
To obtain a better resolution below 2.5 GHz, it would therefore be better to choose the narrowband system. To
obtain a large bandwidth spectrum – for example to analyse the amino acids – the broadband system will be favored.
1.4.4 Data analysis
From these systems, both amplitude and phase information of the transmitted THz electric field are measured
simultaneously depending on the frequency ν. Intensity I(ν) is automatically calculated as the square of amplitude.
The refractive index n(ν), the reflection coefficient R(ν) and the absorption coefficient α(ν) can then be calculated
thanks to the formulae from literature. In order to perform these calculations, we will also need to consider d, the
thickness of the analysed sample, c, the speed of light in the vacuum, I0(ν), which is the reference intensity measured
in free space and ϕ(ν) that will give the relative phase shift between the reference and sample measurements.
Thus, Jepsen and Fischer [29] came with one formula for α(ν) (equation 1.2):
6
15. CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS 1.4. THZ SPECTROSCOPY SETUPS
α(ν) = −
2
d
· ln
I(ν)
I0(ν)
·
(1 + n(ν)) 2
4n(ν)
(1.2)
And one formula for n(ν) (equation 1.3):
n(ν) = 1 +
c
2πνd
· ϕ(ν) (1.3)
Fan et al. [23], from their calculations, considered the reflection coefficient R(ν) (equation 1.4):
R(ν) =
n(ν) − nair
n(ν) + nair
2
(1.4)
Considering nair 1, equation can then be approximated as equation 1.5:
R(ν) =
n(ν) − 1
n(ν) + 1
2
(1.5)
Fan et al. [23] also presented an alternative calculation of the absorption coefficient α(ν) (equations 1.6, 1.7 and 1.8):
I(ν) = I0(ν) ·
(1 − R(ν))
2
e−α(ν)d
1 − R(ν)2e−2α(ν)d
(1.6)
Considering that the reflection coefficient of the samples measured was typically small (3-7%):
I(ν) = I0(ν) ·
(1 − R(ν))
2
e−α(ν)d
1
(1.7)
Hence:
α(ν) = −
2
d
· ln
I(ν)
I0(ν)
· (1 − R(ν)) (1.8)
The equations considered for the amino acid spectroscopy (chapter 3) will be equation 1.3 for the refractive index,
equation 1.5 for the reflection coefficient and equation 1.8 for the absorption coefficient which will be the data we will
mostly be interested in. These formulae have been highlighted with boxes. To be totally accurate, we will need to
calculate the maximum relevant absorption αmax(ν) (also called Amax in chapter 3) above which the absorption data
won’t be relevant anymore. It is calculated from the noise floor NF which is equivalent to the signal measured by
the detector in the absence of excitation by the antenna and is present at all frequencies. A good practical example
of how the noise floor can be reached can be found in chapter 3, figure 3.14.
Jepsen and Fischer [29] calculate the value αmax above which the information is corrupted with noise by first
calculating the dynamic range DR of the terahertz radiation by normalising the amplitude with respect to the noise
floor:
DR =
I(ν)
NF(ν)
(1.9)
The value of the maximum absorption is then directly given by Jepsen and Fischer [29] by equation 1.10:
αmax(ν) =
2
d
· ln DR(ν) ·
4n(ν)
(1 + n(ν)) 2
(1.10)
7
16. 1.4. THZ SPECTROSCOPY SETUPS CHAPTER 1. TERAHERTZ RADIATIONS AND APPLICATIONS
However, neither Jepsen and Fischer nor Fan et al. have taken the reflections (cf. figure 2.1) into account in these
calculations. Therefore, it will be necessary to truncate the spectra we get from the measurements before calculating
the FFTs to get the amplitude and the phase necessary to calculate all these optical properties.
Finally, when analysing the absorption spectra, we will fit the absorption peaks with lorentzian curves: indeed,
according to respectively A.G. Davies et al. [30] and M. A. Startsev and A. Y. Elezzabi [31], the spectral fingerprints
of the absorptions that come from the combinations of Morse potentials between each atom in the molecule are
expected to be unique and exhibit a lorentzian distribution of absorption against the frequency.
8
17. Chapter 2
Emitters characterisation
Introduction
When I arrived to the lab, one of the most important and recurrent problem discussed during the meetings was
an oscillation problem with the photoconductive emitters: for several months, the few THz emitters made showed
unusual oscillations we tried to explain. In order to bring some answers to this problem, I have been assigned to
work on the narrowband system (described in section 1.4.1) to make a few measurements.
Even if the system was already set up, I was trained to master it and acquired experience in optical alignment.
2.1 Oscillations from an unknown origin
Contrary to what the theory would predict (cf. section 1.2) and what the photoconductive emitters fabricated over
previous years have shown, the new photoconductive emitters do not show a positive peak, a negative peak and then
nothing else: we observe oscillations from an unknown origin after these two expected peaks (figure 2.1).
0 5 10 15 20 25 30 35
-80
-60
-40
-20
0
20
40
Amplitude
Time (ps)
Oscillations
Truncation point
for the FFT
First reflection Second reflection
Peakdifference
Positive
peak
Negative peak
Figure 2.1 – Terahertz emitters signal in the time-domain
9
18. 2.2. SAMPLES CHAPTER 2. EMITTERS CHARACTERISATION
Figure 2.1 also shows the truncation point for the FFTs (cf. section 1.4.4) and a very important characteristic of the
emitter: the peak difference. Besides, it is important to notice that the measured amplitude for all these spectra is
in an arbitrary dimension.
The goal of this chapter is to find the factor(s) of these oscillations that disturb our measurements in other experi-
ments, or, at least, find some factors that would be able to change their amplitude or their decrease speed. Therefore,
we made six samples made in different growth conditions and made measurements on these to find or eliminate what
could have an influence on these oscillations from an unknown origin.
Besides, as the rapid thermal annealer was replaced (the annealing step consists on purging the samples with nitrogen
in order to improve the mobility of the electrons, cf. figure 1.3), the calibration has to be done again to find the
best annealing and growth temperatures to make the best THz emitters. This chapter will show the influence of the
growth conditions and the bias voltage on the obtained THz pulses.
2.2 Samples
The influence of the growth conditions is based on the studies of different samples listed and described below:
ˆ L1069 525: LT-GaAs / 50 nm AlAs / 1µm LT-GaAs // Tg = 175 °C, Ta = 525 °C
ˆ L1069 575: LT-GaAs / 50 nm AlAs / 1µm LT-GaAs // Tg = 175 °C, Ta = 575 °C
ˆ L1069 edge: LT-GaAs / 50 nm AlAs /1µm LT-GaAs // Tg = 175 °C, Ta = 550 °C
ˆ L1092: LT-GaAs / 50 nm AlAs / 1µm LT-GaAs // Tg = 175 °C, Ta = 550 °C
ˆ L1093: LT-GaAs // Tg = 155 °C, Ta = 550 °C
ˆ L1094: LT-GaAs // Tg = 175 °C, Ta = 550 °C
Where Tg is the growth temperature and Ta stands for annealing temperature.
The samples are also labelled centre and edge (when unlabelled, they are from the centre of the wafer). There will
be slight differences in growth temperatures (the edge is likely to be at least 5°C cooler than the centre) and growth
thickness and we are interested to see if these differences significantly affect the THz signal.
To summarise this list of samples:
ˆ L1069 and L1092 are grown under the same conditions and should therefore behave exactly the same way.
ˆ The different versions of the L1069 samples are there to test different annealing temperatures to find the optimal
one.
ˆ L1092 and L1094 are made at the same temperature and are differentiated by the layers of AlAs and GaAs:
this is to see if the additional layers were responsible for the oscillations.
ˆ The L1093 and L1094 are made with the same layer and are differentiated by the growth temperature: the
L1094 growth temperature is a little higher.
2.3 Peak difference
The peak difference (cf. figure 2.1) is an intrinsic property of the emitter, completely independant of the oscillations
that characterise its quality: indeed, the bigger the peak difference is, the bigger the signal will be so the contribution
10
19. CHAPTER 2. EMITTERS CHARACTERISATION 2.4. CHANGES IN THE FREQUENCY-DOMAIN
of the noise will decrease. Figure 2.2 represents the peak difference of the THz emitters signal with a bias voltage of
100 V depending on the sample.
L1069_525L1069_575L1069_Edge
L1092_Center
L1092_Edge
L1093_Center
L1093_Edge
L1094_Center
L1094_Edge
0
20
40
60
80
100
120
Samples
Peaksdifference
Figure 2.2 – Peak difference depending on the sample
The first thing we noticed when making these measurements was that oscillations were observed for all the THz
emitters, so the the 50 nm AlAs/1µm LT-GaAs layers couldn’t be held responsible for the oscillations. Then, we can
see that there is a little difference between the edge and the centre samples for each wafer. However, this difference
is not significant: the little difference of growth temperature and growth thickness isn’t a significant factor for the
quality of a photoconductive emitter.
Besides, the comparison of the L1069 samples shows that the 550°C annealing temperature that used to be the
optimal one for the replaced rapid thermal annealer isn’t for the new one: at this scale (525 to 575 °C), the bigger
the annealing temperature is, the bigger the peak difference is. The annealing system needs to be recalibrated.
As L1094 has a much bigger peak difference than L1093, we can also conclude that the quality of a THz emitter
improves with the growth temperature at this scale (around 150–200 °C).
When comparing the L1092 sample (made with the 50 nm AlAs/1µm LT-GaAs) results with the L1094’s (sample
made without), we can see that the additional layers seem to improve the quality of the emitter. However, as the
margin of error on the growth temperature is big and as the two samples were made at different times, this difference
may simply be because the temperature of the wafer wasn’t very accurate. We do not believe the AlAs and the
LT-GaAs responsible for this difference.
Finally, the L1069 and L1092 samples almost have the same characteristics, suggesting reproducibility of growth is
good.
2.4 Changes in the frequency-domain
After analysing the spectra in the time-domain, we wanted to see if the characteristics of the emitter signals could
be emphasised in the frequency-domain. Figure 2.3 shows all the normalised FFTs stacked in order to compare their
shapes. Thus, the Y-axis doesn’t have a real dimension.
11
20. 2.5. DECREASE SPEED OF THE OSCILLATIONS CHAPTER 2. EMITTERS CHARACTERISATION
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.67
NormalizedFFTamplitude
Frequency (THz)
1.10
Peaks
Figure 2.3 – Stack of all the normalised FFTs
We can observe different decay rates between the two peaks and different increase rates after the second peak: the
faster the decay is, the slower the rise is. Thus, the fastest decay (and slowest rise) is observed for L1069 575 when
the slowest decay (and fastest rise) is observed for L1094 edge. The decay and increase rates of these samples are
listed in table 2.1. Notice that because the FFTs have been normalised, there is no real dimension for the decay and
increase rate in that table.
Decay rate Increase rate
L1069 575 0, 425 0, 01
L1094 0, 16 0, 60
Table 2.1 – Decay and increase rates of L1069 and L1094
The shapes of the FFTs change a little, but not much: two peaks are still recognisable. Their positions don’t change
much depending on the growth conditions. Besides, the faster the decay between the two peaks is, the slower the
rise is. But we don’t have any explanation about it yet and for now, the FFTs don’t seem to be a good option to
analyse the oscillations.
2.5 Decrease speed of the oscillations
Back to the time-domain, as the oscillations seemed to be in an exponential envelope, we decided to fit them, taking
the maxima of every spectrum, to study the decay rate. Figure 2.4 shows an example of an exponential envelope fit
(with the maximum points) of a photoconductive emitter signal.
3 4 5 6 7 8 9
-60
-40
-20
0
20
40
Amplitude
Time (ps)
(a) Selected points for the fit
4.5 5.0 5.5 6.0 6.5 7.0 7.5
0
20
40
Amplitude
Time (ps)
(b) Exponential fit
Figure 2.4 – Exponential fit of the L1094 sample
12
21. CHAPTER 2. EMITTERS CHARACTERISATION 2.5. DECREASE SPEED OF THE OSCILLATIONS
The results are given in the form of:
y = y0 + A1 exp(−
x − x0
t1
) (2.1)
In order to study the decrease speed of the oscillations, we gathered the values of A1 and t1 for the samples with a
bias voltage of 100 V in table 2.2. This table also includes the margins of error when they exist.
A1 t1
L1069 525 33, 0 0, 41 ± 0, 08
L1069 edge 36, 7 0, 52 ± 0, 10
L1069 575 39, 2 0, 44 ± 0, 11
L1092 edge 31, 1 0, 66 ± 0, 11
L1093 edge 12, 5 ± 3, 776 ∗ 107
0, 51 ± 0, 10
L1094 edge 41, 7 0, 52 ± 0, 11
Table 2.2 – Characteristic numbers for the study of the decay rate depending on the sample
L1093 edge shows an unusual value for A1 compared with the others with a tremendous error bar that is not even
defined for the other samples. We think that the software was unable to calculate a correct fit on that one even
though graphically, the fit matched correctly.
Despite the fact that the t1values are different for the L1069 edge and the L1092 edge samples when they were
prepared under the same conditions, the decay rates match when comparing the time-domain traces (figure 2.5).
This is explained by the significant error bars. The decay rate is therefore almost the same for every sample and
even more when considering the standard error.
2 3 4 5 6 7 8
-80
-60
-40
-20
0
20
40
Amplitude
Time (ps)
L1092
L1069
Figure 2.5 – Comparison of L1069 with L1092 in the time-domain
From these data, two things have been noticed: first, the amplitude of the oscillations increases with the peak
difference, suggesting that the oscillations are amplified by the main peaks. Second: the growth conditions (an-
nealing temperature, growth temperature and AlAs/GaAs layers) don’t have any influence on the decay rate of the
oscillations. So these data analyses didn’t bring any answer to the origin of the oscillations.
13
22. 2.6. INFLUENCE OF THE BIAS VOLTAGE CHAPTER 2. EMITTERS CHARACTERISATION
2.6 Influence of the bias voltage
Figure 2.6a represents the characteristics of the L1094 edge sample with different bias voltages and figure 2.6b shows
the FFTs of the L1094 sample with different bias voltages (100 V to 300 V, just as before).
100 150 200 250 300
0
25
50
75
100
125
150
Peaksdifference
Bias voltage (V)
(a) Peak difference of the L1094 sample depending on the bias
voltage
0.0 0.5 1.0 1.5 2.0 2.5 3.0
0.0
0.5
1.0
1.5
Amplitude
Frequency (THz)
300V bias
100V bias
(b) FFTs with varying bias voltage for L1094
Figure 2.6 – Influence of the bias voltage
Table 2.3 gives the values of A1and t1as defined in equation 2.1 for the L1094 sample with different bias voltages.
A1 t1
100V 41, 7 0, 52 ± 0, 11
150V 54, 5 0, 50 ± 0, 10
200V 73, 0 0, 51 ± 0, 09
250V 90, 4 0, 52 ± 0, 10
300V 111, 3 0, 45 ± 0, 08
Table 2.3 – Characteristic numbers for the study of the decay rate depending on the bias voltage
From the increase of the bias voltage, figure 2.6a and table 2.3, we can deduce that the oscillation amplitude increases
with the peak difference just like in section 2.5. However, once again, the decay rate doesn’t seem to be affected: it
is still the same regardless of the bias voltage applied.
Then, the bias voltage doesn’t significantly change the shape of the spectra in the frequency domain. We still don’t
know if there is a better way to process the data with the FFTs.
2.7 I/V curves
A last parameter that could be related to the oscillations is the bias voltage applied to the photoconductive emitter.
Therefore, we decided to make quick measurements of the I/V curves to see if it could be easier to work with these
and to investigate to see if there could be a relation between these and the oscillations.
All the I/V curves of the different samples between -100 V and 100 V are displayed figure 2.7. The dark-labelled
measurements have been done without the laser hitting the THz emitter when the light-labelled measurements have
been done with the laser hitting it.
14
23. CHAPTER 2. EMITTERS CHARACTERISATION 2.7. I/V CURVES
-100 -50 0 50 100
-4
-3
-2
-1
0
1
2
3
4
Current(mA)
Bias voltage (V)
525
edge
575
(a) Dark measurements of the L1069 samples
-100 -50 0 50 100
-300
-200
-100
0
100
200
300
Current(mA)
Bias voltage (V)
525
edge
575
(b) Light measurements of the L1069 samples
-100 -50 0 50 100
-4
-3
-2
-1
0
1
2
3
4
Current(mA)
Bias voltage (V)
L1069
L1092
(c) Dark measurements of L1069 vs L1092
-100 -50 0 50 100
-300
-200
-100
0
100
200
300
Current(mA)
Bias voltage (V)
L1069
L1092
(d) Light measurements of L1069 vs L1092
-100 -50 0 50 100
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Current(mA)
Bias voltage (V)
L1092
L1093
L1094
(e) Dark measurements of the L1090s samples
-100 -50 0 50 100
-300
-200
-100
0
100
200
300
Current(mA)
Bias voltage (V)
L1092
L1093
L1094
(f) Light measurements of the L1090s samples
Figure 2.7 – I/V curves of the THz emitters
These data haven’t been very conclusive and did not bring much more information: from all these figures, we could
only deduce a few things: first, the absolute value of the intensity increases much when the THz emitter is excited
by the laser. This is the expected behavior of the emitter as described in section 1.2: the semiconductive contact
with the GaAs becomes a close circuit with a conductive contact (the gold gap being close) when biased.
Besides, L1093 which shows a very high resistivity also has a very low peak difference compared with the other
samples. One may explain the other. However, the three L1069 samples have approximatively the same I/V curves
when L1069 575 has a much better peak difference than the other ones: there must be other factors implied in the
peak difference.
15
24. 2.8. CONCLUSION CHAPTER 2. EMITTERS CHARACTERISATION
2.8 Conclusion
Thanks to the narrowband system resolution, detailed measurements were made on photoconductive emitters made
from LT-GaAs in which the growth conditions were systematically changed. With the samples tested, an optimum
performance was found with the additional AlAs/GaAs layers, Tg = 175°C and Ta = 575°C, but more tests have to
be done to find the optimum performance growth conditions. Besides reproducibility was seen between two wafers
grown on different days.
In all wafers, an unexpected oscillation was observed. The origin of this is still unknown, but much work was done
trying to trace the cause. It could be a result of the laser, the optical components or a change in the molecular beam
epitaxy procedure. Additional trails have also been considered by analysing the FFTs of the spectra and the I/V
curves to see is there was any remarkable correlation with the oscillations.
Further work is needed to discern the origin of this effect, for example by using the emitters tested in a completely
different THz system, although there wasm’t the opportunity to complete this during the internship.
16
25. Chapter 3
Amino acid spectroscopy
Introduction
Amino acid spectroscopy are recorded using broadband system (described in section 1.4.2). The amino acid sample
is placed in a cryostat with polyethylene (PE) windows and then the sample temperature can be easily controlled.
Eventually, the purpose of such measurements would be the understanding of how proteins, peptides and polypeptides
fold and interact. Indeed, the amino acids are the elementary components of these molecules implied in many functions
in the human body. Their folding (or misfolding) have been discovered to be a phenomenon at the origin of diseases
such as diabetes, Huntington’s, Parkinson’s or Alzheimer’s diseases [32, 33].
Yet, these phenomena and the formation of proteins is not yet fully understood. The broadband spectroscopy is one
possible method to verify the results of a protein folding or unfolding model [34] and that is why we are studying
the amino acids. But we are still far from fully understanding the folding, especially because for the spectroscopy
measurements to be relevant, they must be done in a liquid environment, like in a human body. And as powerful as
it is, the THz-TDS method does not enable us to do such measurements because of the high reflection coefficient of
the water in the THz range, and that is why our measurements are always done in the vacuum.
However, even if the data obtained thanks to our measurements won’t give an immediate answer to the folding model
question, it will at least contribute to literature by filling a little the broadband spectral catalogue of biological
molecules.
3.1 Samples
3.1.1 Amino acid general points
The amino acids are a class of chemical compounds having a common carbon atom surrounded by an amine group
(-NH2) and a carboxylic acid group (-COOH ). The side chain R, connected to the same carbon atom, distinguishes
one molecule from another (figure 3.1).
Figure 3.1 – Standard structure of an amino acid
17
26. 3.1. SAMPLES CHAPTER 3. AMINO ACID SPECTROSCOPY
We classify the studied amino acids in three categories – by acidity – we will compare, but we will also compare the
spectra of the amino acids that have similar chemical side chains. The three categories are described as follows:
ˆ “Neutral” amino acids (figure 3.2): they are characterised by the lack of any other acid or basic functional
group.
(a) L-Alanine (b) L-Valine (c) L-Leucine (d) L-Isoleucine
Figure 3.2 – Neutral amino acids
ˆ “Acid”amino acids (figure 3.3): they are characterised by the presence of at least another acid functional group,
which would be a carboxyl group (-COOH ) for our two acids.
(a) L-Aspartic acid (b) L-Glutamic acid
Figure 3.3 – Acid amino acids
ˆ “Basic” amino acids (figure 3.4): they are characterised by the presence of at least another basic functional
group, most of the time an amino group (-NH2).
(a) L-Lysine (b) L-Arginine (c) L-Histidine
Figure 3.4 – Basic amino acids
Please notice that the acidity of the amino acids have been put in inverted commas because the acidity we are talking
of isn’t absolute, but is given by the additional functional group(s) of the amino acids.
18
27. CHAPTER 3. AMINO ACID SPECTROSCOPY 3.2. DATA PROCESSING
3.1.2 Analysed samples
Figure 3.5 – Sample of an amino acid scheme
The analysed samples (figure 3.5) are amino acid crystals in a matrix, prepared from powder. The dimensions of a
sample (copper excluded) are 8 mm in diameter with a thickness of approximatively 0.5 mm. Since the concentrations
of the amino acids can be very low (down to 5% in mass concentration), the powder amino acid + matrix has to
be carefully mixed. Then, we put the powder under a high pressure (7-8 tons.m−2
) during 6 to 12 minutes at the
centre of a copper ring that will be fixed directly into the cryostat. The fact that the cryostat is in copper too should
enable a maximum thermal conduction. Therefore, it will be reasonable to assume that the temperature reached and
measured by the cryostat will be the same as the sample’s.
It would be important to notice that as the concentrations of the amino acids are very low, one of the difficulty was
to insure the concentration we wanted is the one we obtained.
3.2 Data processing
3.2.1 Origin C script
1 2 3 4 5 6
0
5
10
15
20
Absorbtion(mm
-1
)
Frequency (THz)
1,0
1,5
2,0
2,5
RefractiveIndex(n)
Figure 3.6 – Result of the script written for the data processing (arginine spectrum)
19
28. 3.2. DATA PROCESSING CHAPTER 3. AMINO ACID SPECTROSCOPY
Every sample and reference has been recorded 10 times in the broadband system in order to improve the signal-to-
noise ratio by calculating an average. This was particularly important when, sometimes, the stage wasn’t accurate
enough and jumped from one position to the next one or when the software crashed and gave a constant result at
zero, so we could delete these data. The script used for the data processing was written by my internship mate,
Oliver Peel. This is what it does:
ˆ Truncate the reference data before the first reflection
ˆ Pad the reference data table until line 4096 = 212
(this choice of number being justified in section 3.2.3) with
its last value to prepare the FFTs
ˆ Perform an FFT on each of the 10 reference traces and put all the amplitudes and the phases calculated in a
different table
ˆ Truncate the sample data before the first reflection
ˆ Pad the sample data table until line 4096
ˆ Perform an FFT on each of the 10 sample traces and put all the amplitudes and the phases calculated in a
different table
ˆ Calculate the different indexes thanks to the formulae from the litterature: n, R, α, αmax (respectively equa-
tions 1.3, 1.5, 1.8 and 1.10) and their error bars
ˆ Make averages of them
ˆ Plot every data in a reasonable frame: frequency between 0.3 THz (below, the signal-to-noise ratio is very poor)
and 6 THz (then, the absorption spectrum goes above the noise floor defined by αmax); absorption α between
0 and 21 mm−1
(our noise floors never go above); refraction index n between 1 and 2.5 (this is where we find
our refraction indexes).
Figure 3.6 gives an example of the plotting done by this script applied to the arginine data. For such an example,
the window of the refractive index would be zoomed at around 1.4.
3.2.2 Lorentzian fits
1 2 3 4 5
0
2
4
6
8
10
Absorption(mm
-1
)
Frequency (THz)
Arginine spectrum
Fit
Figure 3.7 – Example of a fitted spectrum: arginine
20
29. CHAPTER 3. AMINO ACID SPECTROSCOPY 3.3. MATRIX AND THE CONCENTRATION
Figure 3.7 shows how every absorption peak can be fitted with a lorentzian distribution in compliance with the
theory [30, 31]. The formula of the peak fits are given in the form:
y(x) = y0 + y1 × x + A
1
2π
w
(x − xc)2 + (1
2 w)2
(3.1)
Where w is the full width at half maximum, xc is the frequency of the peak and A is the “area” or the amplitude of
the lorentzian function. The error bars were provided automatically by Origin.
Notice that so it can be adapted to a background slope, y0 +y1 ×x has been added to the original lorentzian formula.
3.2.3 FFT resolution
In order to perform the FFTs of our spectra, we chose to pad the references and the samples until 4096 = 212
. The
choice of this number (the FFT being done automatically until the next power of two if it is not already the case)
can change the resolution of our spectrum at the end. To justify this choice, we decided to choose an example of
a narrow peak – so the difference between the resolutions is more explicit – the narrowness of the function being
defined by: N = H
w , with H the height of a peak defined as H = y(xc) = 2A
πw .
The peak we chose for that example is an isoleucine peak that has a narrowness of N = 17, 82 ± 1, 82 mm−1
.THz−1
.
Figure 3.8 shows an example of an FFT of that same peak with different resolutions.
1 2 3 4 5 6
0
5
10
15
20
Absorbtion(mm
-1
)
Frequency (THz)
2
15
2
14
2
13
2
12
2
11
(a) The isoleucine spectrum with its noise floor
4,31 4,32 4,33 4,34 4,35 4,36 4,37 4,38 4,39 4,40 4,41 4,42
8,4
8,5
8,6
8,7
Absorbtion(mm
-1
)
Frequency (THz)
2
15
2
14
2
13
2
12
2
11
(b) Zoom on the 4.35 THz peak
Figure 3.8 – An isoleucine peak with different resolutions
Of course, 212
doesn’t give the best resolution, but it provides a good compromise between data processing speed
and resolution. This is what justifies our choice.
3.3 Matrix and the concentration
3.3.1 Influence of the matrix
In order to analyse our samples, we mix our amino acids with a very high concentration of a matrix (cf. section 3.1.2).
This is meant to avoid the recurring problem of the data processing: saturation due to a too low transmission of the
amino acid – and therefore an absorption above the noise floor. This is thanks to the very low absorption property
of the matrices that will drastically lower the absorption of the samples. However, if the influence of a matrix is fully
understood, we will be able to get back to an absolute absorption. This is the point of the calculations made by Dr.
Andrew Burnett, and some of the results of this section will be published in a future article.
21
30. 3.3. MATRIX AND THE CONCENTRATION CHAPTER 3. AMINO ACID SPECTROSCOPY
For this study, the matrices we considered are PTFE (polytetrafluoroethene, figure 3.9b), Al2O3 (aluminium oxide,
figure 3.9c) and PE (polyethylene, figure 3.9d). In order to verify the theoretical calculations of Dr. Burnett, we also
wanted to see the influence of these matrices on a simple molecule, the TMA-B (tetramethylammonium bromide,
figure 3.9a, that will be simply referred as TMA in the rest of this report), but we will speculate about why the
measurements did not work for this molecule.
(a) TMA (b) PTFE (c) Al2O3 (d) PE
Figure 3.9 – Molecules considered for the study of the matrices
One consequence of the matrices absorbing no particular frequency is its independence of the temperature: indeed,
they have no strong absorption peaks that can be modified by the temperature in the frequency range of interest.
Only the dielectric response to the THz wave that explains the absorption background differenciates one matrix from
another. Figure 3.10a illustrates the independence of the PTFE with the temperature, but we observed the same
things for the two other matrices.
Figure 3.10b shows the spectra of all the considered matrices. We can that even if Al2O3 and PE show no particular
absorption frequency, they present an absorption background – particularly visible on the Al2O3 spectra – that
PTFE does not have. This is why we decided to choose PTFE as our matrix for all the amino acid measurements,
which is almost totally transparent under 5.5 THz.
1 2 3 4 5 6
0
5
10
15
20
Absorption(mm
-1
)
Frequency (THz)
Amax
4K PTFE
290K PTFE
(a) Independence of a matrix of the temperature
1 2 3 4 5 6 7
0
2
4
6
8
10
12
14
16
18
20
Absorption(mm
-1
)
Frequency (THz)
Amax PE
PTFE Al2O3
(b) Spectra of the three used matrices (PTFE noise floor)
Figure 3.10 – Spectra of the matrices
We then wanted to see if we could confirm Dr. Burnett’s calculations on the influence of a matrix on the simple
molecule TMA. However, the measurements failed, and we suspect a degradation of the TMA to be responsible.
Indeed, figure 3.11a shows two spectra of TMA+PTFE with the same concentration, namely 5%: our spectra (“New
data”, black line) versus older data (“Old data”, red line). However, our data have been normalised: the entire
spectrum has been multiplied by 1.96 so the two spectra have the same height, but even with this normalisation, we
can already see that the peaks are less contrasting so less visible even though we made these measurements the day
we received the TMA. We think that even if the TMA peaks are recognisable, it already began to degrade.
22
31. CHAPTER 3. AMINO ACID SPECTROSCOPY 3.3. MATRIX AND THE CONCENTRATION
Figure 3.11b additionally shows our spectra of TMA + PE and TMA + Al2O3 from measurements done 3 days
later. It was then impossible to recognise any of the TMA peaks.
1 2 3 4 5 6
0
2
4
6
8
10
12
Absorption(mm
-1
)
Frequency (THz)
New data
Old data
(a) TMA + PTFE: our data vs. older data
1 2 3 4 5 6
0
5
10
15
20
Absorption(mm
-1
)
Frequency (THz)
TMA + PTFE
TMA + PE
TMA + Al2O3
(b) Our TMA + matrices spectra
Figure 3.11 – TMA + matrices spectra
The sample may have degraded as it is hydroscopic and the weather conditions at the time we ordered and made our
TMA measurements have been very hot and humid. The TMA may have been exposed and started to degrade then.
However, to study the experimental versus theoretical spectra, we still have Dr. Burnett’s data we can compare with
the spectra obtained from his calculations. Figure 3.12 shows the difference between these two spectra. At the time
the theoretical curve was traced, the theory couldn’t give an absolute result of the absorption, but we can see that
when normalised as it is on this figure, the global shape of the spectra are the same.
The calculations, based on the crystal structure of the considered molecules, have become more accurate since then
and it was my internship mate Oliver Peel who was assigned to automate the data processing.
1 2 3 4 5
0
2
4
6
8
10
12
Absorption(mm
-1
)
Frequency (THz)
Exp
Theory
Figure 3.12 – Experiment vs calculations
23
32. 3.3. MATRIX AND THE CONCENTRATION CHAPTER 3. AMINO ACID SPECTROSCOPY
Dr. Burnett also made some calculations to see if a spectrum would change with the dielectric permittivity (the
square of the refractive index) of the matrix and discovered that it does (figure 3.13): the black curve is the theoretical
spectrum TMA with a matrix with a permittivity of 2, which would be similar to PTFE, when the red curve is a
simulation with a matrix with a permittivity of 3 which is similar to Al2O3 . However, these simulations do not take
into account the dielectric response background of the matrices.
Figure 3.13 – theoretical calculations for different matrices dependent on their refractive index
3.3.2 Influence of the concentration
Leucine + PTFE mixtures spectra and their noise floor is shown figure 3.14 with the different concentrations of
leucine indicated in the legend.
1 2 3 4 5 6
0
5
10
15
20
25
30
Absorption(mm
-1
)
Frequency (THz)
Amax
12.5%
25%
50%
Figure 3.14 – Leucine + PTFE spectra with different concentrations
24
33. CHAPTER 3. AMINO ACID SPECTROSCOPY 3.4. TEMPERATURE AND ACIDITY
In theory, the absorption peaks of the spectra are not supposed to move with a different concentration of the matrix.
In order to verify this, the samples have been prepared with higher concentrations of amino acid than usually so the
differences are more accentuated. We will give the positions of the leucine’s peaks using the fitting method introduced
in section 3.2.2, and we will give the other characteristics of the peaks and discuss them as well.
Table 3.1 shows the characteristics of the four peaks of these samples, using the notations of equation 3.1, reminded
here:
y(x) = y0 + y1 × x + A
1
2π
w
(x − xc)2 + (1
2 w)2
We also used the height H and the narrowness N defined in section 3.2.2.
xc (THz) w (THz) A (THz.mm−1
) H (mm−1
) N (mm−1
.THz−1
)
12.5% leucine 0.784 ± 0.001 0.370 ± 0.011 0.196 ± 0.018 0.337 ± 0.027 0.908 ± 0.098
25% leucine 0.839 ± 0.003 0.361 ± 0.029 0.291 ± 0.049 0.513 ± 0.082 1.423 ± 0.343
50% leucine 0.797 ± 0.001 0.423 ± 0.011 0.715 ± 0.040 1.077 ± 0.057 2.549 ± 0.200
(a) Peak 1
xc (THz) w (THz) A (THz.mm−1
) H (mm−1
) N (mm−1
.THz−1
)
12.5% leucine 1.436 ± 0.003 0.297 ± 0.017 0.187 ± 0.025 0.401 ± 0.048 1.352 ± 0.241
25% leucine 1.431 ± 0.001 0.268 ± 0.010 0.280 ± 0.022 0.665 ± 0.050 2.482 ± 0.282
50% leucine 1.422 ± 0.001 0.268 ± 0.004 0.431 ± 0.014 1.023 ± 0.031 3.814 ± 0.174
(b) Peak 2
xc (THz) w (THz) A (THz.mm−1
) H (mm−1
) N (mm−1
.THz−1
)
12.5% leucine 1.691 ± 0.002 0.278 ± 0.018 0.229 ± 0.031 0.524 ± 0.067 1.886 ± 0.368
25% leucine 1.686 ± 0.001 0.254 ± 0.010 0.307 ± 0.023 0.770 ± 0.056 3.029 ± 0.340
50% leucine 1.676 ± 0.001 0.249 ± 0.008 0.399 ± 0.026 1.022 ± 0.063 4.110 ± 0.382
(c) Peak 3
xc (THz) w (THz) A (THz.mm−1
) H (mm−1
) N (mm−1
.THz−1
)
12.5% leucine 2.102 ± 0.003 0.287 ± 0.023 0.358 ± 0.052 0.794 ± 0.115 2.771 ± 0.626
25% leucine 2.109 ± 0.002 0.338 ± 0.015 0.680 ± 0.060 1.280 ± 0.107 3.783 ± 0.481
50% leucine 2.091 ± 0.001 0.329 ± 0.004 1.017 ± 0.024 1.970 ± 0.043 5.997 ± 0.197
(d) Peak 4
Table 3.1 – Peaks of leucine samples with different concentrations
As expected, the peak positions are not affected by the proportion of amino acid in the matrix. The insignificant
changes between every of them can be explained by small differences between samples. The width of each peak doesn’t
seem to depend on the concentration either. However, the area A, the height of the peak H and its narrowness N,
which make the peaks more visible, all increase very much with the concentration. This is the interest of working
with a high concentration of amino acid. However, the higher the concentration is, the faster the spectrum goes
above the noise floor making the data worthless.
Therefore, the best way to process would be to work with the highest concentration possible as long as the spectrum
stays below αmax for every sample and then to normalise the spectra to get back to an absolute absorption, so the
concentration does not matter and we can compare the spectra with each other.
3.4 Temperature and acidity
This part is meant to show the influence of the temperature and to give a first observation of the influence of the
acidity (if there is any) on a spectrum.
25
34. 3.4. TEMPERATURE AND ACIDITY CHAPTER 3. AMINO ACID SPECTROSCOPY
3.4.1 Global results
Figures 3.15, 3.16, 3.17 respectively show the acid, basic and neutral amino acid analysed sample spectra truncated
when they reach the maximum absorption. After trying several concentrations, the concentrations chosen to make
these spectra were 5% of the amino acid acid for the acid and the neutral ones and 10% for the basic ones.
1 2 3 4 5 6 7
0
2
4
6
8
10
12
14
16
18
20
Absorption(mm
-1
)
Frequency (THz)
290K aspartic acid
4K aspartic acid
290K glutamic acid
4K glutamic acid
Figure 3.15 – Acid amino acid spectra
5% amino acid, 95% PTFE - An offset of 2 mm−1
has been added between each spectrum
1 2 3 4 5 6 7
0
5
10
15
20
25
30
35
40
45
Absorption(mm
-1
)
Frequency (THz)
290K histidine
4K histidine
4K arginine
290K arginine
290K lysine
4K lysine
Figure 3.16 – Basic amino acid spectra
10% amino acid, 90% PTFE - An offset of 5 mm−1
between each spectrum
26
35. CHAPTER 3. AMINO ACID SPECTROSCOPY 3.4. TEMPERATURE AND ACIDITY
1 2 3 4 5 6 7
0
5
10
15
20
25
30
35
40
45
Absorption(mm
-1
)
Frequency (THz)
290K alanine
4K alanine
290K valine
4K valine
290K isoleucine
4K isoleucine
290K leucine
4K leucine
Figure 3.17 – Neutral amino acid spectra
5% amino acid, 95% PTFE - An offset of 5 mm−1
between each spectrum
3.4.2 Influence of the temperature
Three factors have an influence on the appearance of the absorption spectra:
ˆ The potential wells become more and more symetric with the temperature decrease: the anharmonic Morse
potentials become more and more harmonic, changing the transitions levels: the energy levels rise, moving the
characteristic peaks to the higher frequencies which corresponds to a blue shift.
ˆ Transitions under THz excitation only happen at the fundamental level: we don’t have transitions at any higher
levels that used to happen at high energy and, therefore, at higher frequencies. The peaks which were wider
“on the right” (higher frequencies) get more symmetric and thinner at low temperature.
ˆ The amino acid crystals are 3-dimensional: depending on the vibration modes of the molecules in the crystal,
some peaks (that correspond to a certain direction) will move more than others leading sometimes to a super-
position of peaks and, therefore, to an apparent disappearance of some peaks. As a result, the shape of some
peaks may look different.
The change of shape of the potentials with the decrease of the temperature is described in figure 3.18.
27
36. 3.4. TEMPERATURE AND ACIDITY CHAPTER 3. AMINO ACID SPECTROSCOPY
Figure 3.18 – Influence of the temperature on the potential shape
However, in practice, we don’t observe the peaks to be much more symmetrical. Besides, the resolution can be
very misleading (figure 3.8). In addition, this time, the height of the peaks doesn’t change. This why we only are
interested in how the position and the half width at half maximum w of the peaks change. Figure 3.19 shows how
these peaks can move for a neutral amino acid (isoleucine) and a basic one (arginine). The shifts and the width
changes have been respectively calculated as Fshift = xc,290K −xc,4K and Wchange = w290K −w4K so a negative shift
means the peak moved to the high frequencies with the temperature decrease and a positive width change means the
peak became thinner.
So what we expect is to observe negative shifts and positive width changes.
0 1 2 3 4 5 6
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Widthchange(THz)
Frequencyshift(THz)
Frequency of the peak at 290K (THz)
Frequency shift
Width change
(a) Isoleucine
0 1 2 3 4 5 6
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Widthchange(THz)
Frequencyshift(THz)
Frequency of the peak at 290K (THz)
Frequency shift
Width change
(b) Arginine
Figure 3.19 – Frequency shift and width of the peaks change for isoleucine (neutral) and arginine (basic)
We can see that at higher values of frequency, the shift and the width change values tend to be bigger. This is
especially remarkable with the acid and the basic amino acids. However, what we expected with the theory does not
always happen. It seems that there are other factors that play a role in the peak positions and the peak widths when
the temperature decreases.
3.4.3 Study of the amino acids by acidity
To see if our expectations are averagely met by our measurements, figure 3.20 shows the shift and width change
averages of the peaks of every sample.
28
37. CHAPTER 3. AMINO ACID SPECTROSCOPY 3.5. CONCLUSION
A
la
n
in
e
V
a
lin
e
L
e
u
c
in
e
Is
o
le
u
c
in
eA
s
p
a
rtic
a
c
id
G
lu
ta
m
ic
a
c
id
L
y
s
in
e
A
rg
in
in
e
H
is
tid
in
e
-0.4
-0.2
0.0
0.2
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0.6
0.8
1.0
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Widthchangeaverage(THz)
Frequency shift average
Width change average
Frequencyshiftaverage(THz)
Figure 3.20 – Average of the frequency shifts and the width changes of every sample
It seems that in average, probably because of the poor resolution of the broadband system, the width doesn’t change.
However, the frequency shifts mostly meet our expectations: they are indeed negative in average most of the time or
very slightly positive (isoleucine, aspartic acid). It would also seem that acid and basic amino acids can have bigger
shifts than neutral amino acids, but this may be due to the choice of the amino acids.
Yet, a big problem and something we cannot explain yet is the unique and big red shift for the lysine sample. The
molecule has nothing particular that could explain such a behavior, its additional functional group simply being an
amino group. The most plausible explanation would be a confusion in the identification of the peaks. A way to verify
the identification is right and if the peaks really moved in the wrong direction would be to make measurements of
the lysine at intermediate temperatures: if they really do, it would then be interesting to look if they first move to
the higher frequencies – as it should “normally” do – and then to the lower frequencies, or juste slowly to the lower
frequencies. A study of the crystallography of the lysine may then help to explain such an uncommon behavior:
indeed, it happens that the crystal structure of a molecule changes with the temperature. New peaks would therefore
appear, and others would disappear.
3.5 Conclusion
A detailed and systematic study of the amino acid absorption was made. The effect of sample preparation through
the choice of the matrix or the concentration were investigated: PTFE was found to be the best choice of matrix
when the concentration has to be reajusted according to the amino acid to obtain the most accurate results.
Peaks were fitted so a better analysis of the temperature and acidity influence could be made: a list of all the peaks
has been done and when it was possible, tracked between their 290 and 4K spectra: blue shifts or neglectable red
shifts were observed for most of them, as expected from the theory. Though, one amino acid showed significant red
shifts: repeating the measurements at different temperatures or with new samples may help to confirm these shifts.
It may be a result of the crystal changes.
Eventually, we would like to associate each absorption peak with an atom bond, but we are still far for achieving
this: indeed, results can vary because it is not only the atoms in the molecules that vibrate and absorb frequencies;
there are also interactions between the molecules and differences between samples which can explain slight differences
between each measurement.
29
38. Conclusion
During this three-month internship, I’ve been directly involved into active fields of research: first, thanks to the
training I got from Dr. Burnett, I was able to work alone on the characterisation of photoconductive emitters which
was a huge concern at the IMP. Even if we couldn’t figure out the origins of these oscillations, we could at least
exclude some possibilities and considered and gathered different data, such as I/V curves and FFTs. My work also
helped to improve the growth conditions to build the most efficient emitters. The experimental work for it required
a lot of patience due to the fine optical alignment needed to make the measurements on the narrowband system.
The amino acid spectroscopy I participated to is part of something much bigger. Preparing the samples, manipulating
the broadband system with the cryostat, processing the data with all the parameters: this is something to do for every
amino acid and reporting these results, even without any conclusion for the understanding of the protein folding, will
at least contribute to the literature. Unfortunately, I learnt from the practice that sometimes, in experimental physics,
things don’t happen as you expect, and we were not able to make the measurements that would have confirmed the
theoretical calculations of Dr. Burnett on the influence of a matrix because of the TMA degradation.
Finally, even if it happened at the very end, I was introduced to Computer Assisted Design and rediscovered a little
basic electronics thanks to Joshua Freeman.
I really enjoyed the whole internship: being this much independent in active fields of research was a whole new and
all-time interesting experience. Besides, everybody at the lab was just great and I’d like to give my last and special
thanks to Dr. Andrew Burnett thanks to whom I’ve really learnt the most, not only from the physics with the
experimental and the data processing experience he shared, but for the bibliographic methodology and his huge help
on my report as well.
30
39. Software credit
ˆ Composition: LYX 2.0 / WinEdt 8
ˆ Schemes: Adobe Fireworks CS4
ˆ Molecules: Accelrys Draw 4.1
ˆ Spectra / Data processing: OriginPro 9.0
ˆ Data processing: Microsoft Excel 2010
ix
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