Optimization of single mode fibre sensors to detect organic vapours

Sensors and Actuators B 157 (2011) 388–394

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Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb

Optimization of single mode fibre sensors to detect organic vapours Cesar Elosua a,∗ , Candido Bariain a , Asuncion Luquin b , Mariano Laguna b , Ignacio R. Matias a a b

Departamento de Ingeniería Eléctrica y Electrónica, Universidad Pública de Navarra, Campus de Arrosadia s/n, E-31006 Pamplona, Spain Instituto de Ciencia de Materiales de Aragón-CSIC, Universidad de Zaragoza, E-50009 Zaragoza, Spain

a r t i c l e

i n f o

Article history: Received 26 January 2011 Received in revised form 19 April 2011 Accepted 21 April 2011 Available online 29 April 2011 Keywords: Optical fibre sensor Single mode fibre Vapochromic materials Nanostructures Volatile organic compounds

a b s t r a c t The construction of single mode optical fibre (SMF) sensors to handle with volatile organic compounds (VOCs), has been optimized to operate at the third telecommunication window (1550 nm). The main motivation is to take advantage of the photonic devices used in telecommunication systems that makes easier sensors multiplexing. Moreover, the low transmission attenuation at that wavelength offers the possibility of remote sensing. The sensing materials used suffer reversible structural alterations in the presence of VOC, such as colour change, which are detectable with a photonic system. Following the Electrostatic Self Assembly method (ESAm), a nanostructure is constructed onto cleaved ended SMF, which is doped with a sensing material. The fabrication of this type of sensors was focused on multimode fibres (MMF) and in the visible spectral range (VIS) so far. The implementation has been adapted to SMF and to operate around 1550 nm, specifically, by easing the adsorption of the VOCs molecules. It has been observed that the sensing material affects the morphology of the nanostructures as well and so, to the sensors response. The devices implemented show a potential use in the identification of single and complex mixtures of VOCs. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Along the last decades, much researching efforts have been focused on the detection and identification of VOCs [1]. There are several applications based on identifying organic solvents. For instance, in the food industry, the odour given off can be used to control the quality or to follow fermentation/maturing processes [2,3]. These tasks could be performed by human operators, but, if a continuous monitoring is required, the health and smell sense of the workers would get compromised. Electronic technology has been used to develop sensors that detect and identify single or several VOCs at the same time [4,5]. Nevertheless, these devices exhibit some significant drawbacks in these applications such as biasing (explosion risk) or vulnerability to electromagnetic noise. Moreover, arrays of electronic sensors show a size that makes difficult to operate remotely [6,7]. In this background, photonic technology is an interesting alternative. Among all the advantages that optical fibre offers, multiplexing and remote sensing capabilities are very significant features. Excepting the devices based on spectroscopy techniques [8], many sensors use fibre with core/cladding dimensions wider than the ones employed in communications systems [9,10]. Furthermore, the operating spectral range is typically in the VIS. These conditions make harder multiplexing several sensors and the high

∗ Corresponding author. Tel.: +34 948169328; fax: +34 948169720. E-mail address: [email protected] (C. Elosua). 0925-4005/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2011.04.063

transmission losses limit the maximum remote measuring distance as well. There are applications where these both features are desirable, so some researches have designed sensors to work under these conditions [11–13]. In light of the results obtained with sensors based vapochromic materials and MMF [14–16], this study is focused on preparing sensors with SMF to operate at 1550 nm. As a consequence of the spectral and fibre changes, the response of the sensors was initially lowered. This inconvenience was tried to be overcome easing the adsorption of the VOCs molecules by modifying the morphology of the sensing layer. The response of the sensor was analyzed when exposed to individual VOCs, registering the effect of environmental factors as well. Finally, the potential use in applications related with complex mixtures of organic solvents was checked too. 2. Materials and methods 2.1. Polyelectrolytes and sensing materials The sensing materials used in this work show a polymeric structure based on Au–Au bonds, and their generic formula is [Au2 Ag2 (C6 F5 )4 L2 ]n , where L is a ligand molecule specific for each compound [17,18]. A scheme of the chemical structure and a Scanning Electron Microscope (SEM) image of one of these compounds are shown in Fig. 1. These materials suffer reversible changes in their structure in the presence of VOCs (vapochromism): the volatile molecules get coordinated with the metallic atoms, breaking firstly the polymeric chain and secondly, the monomer

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Fig. 1. 3D model obtained with Chem3D software pack of one of the monomers with ammonia ligands (A); it can be appreciated how the polymeric chains get grouped forming a compact block (B).

structure [19]. Both structural modifications induce changes in the optical properties of the material, such as the colour, refractive index or the optical absorption. More details about this reaction mechanism are described in [14,16,19]. The ligand molecule defines the physicochemical properties such as the luminescence, the reactivity or the selectivity of the compound, as it is has been reported in previous works [9,16]. The materials used in this work exhibit 1/2 diphenylacetylene, ammonia, pyridine and 2,2 -bipyridine ligands respectively, from the highest to the lowest reactivity. The polymers employed for the ESAm were PAH (poly allylamine hydrochloride) as polycation, and PAA (poly acrylic acid) as the polyanion. They both are weak polyelectrolytes [20], so that the morphology of the sensing layers can be altered by they degree of ionization [20], concentration [21] or ionic strength [22].

reflected power varies, so the sensors are intensity modulated [23]. In this way, an optical coupler guides the interrogating signal from a laser source (port 1) to the sensor (port 3); this coupler guides back its response to an optical power meter (port 2) as well. Another power meter is connected to port 4 to compensate undesired power fluctuations of the light source [24]. Looking for minimizing the effect or residual reflections, all connectors are FC/APC. The set up is plotted in Fig. 2. The sensor head is placed inside a chamber made of steel to avoid ambient light interference. Once the VOC is injected into it, the chamber is hermetically closed. All the measurement is performed at 30 ◦ C by using an external thermal jacket. 3. Implementation of the sensors 3.1. Effect of migration to 1550 nm and use of SMF

2.2. Experimental set up All the sensors are based on reflection configuration, using cleaved ended SMF (9/125 ␮m core/cladding diameters respectively). Depending on the optical properties of the sensing layer, the

The sensors were prepared following the ESAm. In few words, this procedure consists of immersing alternatively into polycationic and polyanionic dissolutions [25]. These steps can be repeated several times, depending on the morphology of the layers fixed. This

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Fig. 2. Experimental set up used to register the response from the sensors.

3.2. Adaptation of the construction process The organic vapours get adsorpted onto the sensing surface and in this manner, react with the sensing material. Thereby, there is a trade off between the sensitivity of the sensor and the morphology of the nanostructure: a high porosity would make easier the diffusion of the VOCs, but the light would be scattered too much. Looking for improving the dynamic range, an increment on the porosity and the roughness of the sensing layer was tried. Thanks to the versatility of the ESAm, these modifications were achieved by altering construction parameters such as the pH of the polymeric solutions and their ionic strength. The construction process is also affected

by the sensing material, so that if it is deposited during it, the nano structure would not grow properly [28]. Because of this, the cavity was build firstly and thereafter, it was immersed into the colloid of the sensing material for 1 h. The effect of the pH and the ionic strength (NaCl concentration) was studied. It was accomplished comparing the temporal response obtained with different construction parameters. Beginning with the ones used to develop the MMF/850 sensor [PAH8 /PAA8 0.0NaCl], the polymers were used with a low ionization degree [PAH8.5 /PAA3.5 -0.0NaCl] and finally, with a higher ionic strength [PAA8 /PAA8 -0.2NaCl] and [PAA8.5 /PAA3.5 -0.2NaCl] (the subscripts denotes the pH of the polyelectrolytes solutions). The responses obtained in each case with ethanol vapours are resumed in Table 1 in terms of recovery time and dynamic range. The best results were obtained when the polyelectrolytes showed a low ionic degree [20]: specifically, setting the pH of the PAH dissolution at 8.5 and the one of the PAA mixture at 3.5. A scheme of the construction process with these parameters is available in Fig. 4. It was observed that increasing the ionic strength altered the optical homogeneity of the sensing layer too much

ΔReflected Optical Power (dB)

technique allows polymer chains at a nanometric scale to be assembled onto different substrates and also to dope them with different sensing materials [26,27]. The sensors prepared with vapochromic compounds fixed by the ESAm operated at in the VIS with MMF [16] so far. The amplitude of the spectra was not so noticeable at higher wavelengths than at the VIS. Furthermore, using SMF implies that the interface core-sensing layer is almost 100 times lower. As a consequence, the sensitivity of the sensor might be reduced drastically if the implementation is unaltered and both the fibre and working wavelength are changed. To check the effect of these changes, two sensors were prepared with MMF and SMF respectively, fixing a sensing material with 1/2 diphenylacetylene (highest reactivity among the compounds used) ligands. In both cases, the PAA and the PAH solutions were 10 mM with pH 8, preparing also a colloid with the vapochromic material. The structure was [PAH+ /PAA− ]5 [PAH+ /PAA− /(ColloidVapochromic )/PAA− ]15 for both sensors [16]. The response of the MMF sensor in an atmosphere saturated with ethanol vapours was registered in these two cases: illuminating it with a LED at 850 nm and with another one centred at 1550 nm. Thereafter, the SMF sensor was interrogated at 1550 nm and exposed to the same atmosphere. These three responses are shown in Fig. 3. The dynamic range observed in the case of MMF/850 nm is close to 3 dB, but it is lowered down to 0.9 dB when the same device is illuminated at 1550 nm. Moreover, the dynamic range falls down to 0.4 dB in the case of SMF/1550 nm, highlighting the decrease of the sensitivity. Therefore, the construction process had to be adapted to overcome it.

MMF (850nm)

0.5

MMF (1550nm)

SMF (1550nm)

0 -0.5

0.4 dB

-1

0.9 dB

-1.5 -2 -2.5 -3

2.8 dB 0

1

2

3

4

5

6

Time (minutes) Fig. 3. Responses of sensors whose structure is [PAH+ /PAA− ]5 [PAH+ /PAA− /(ColloidVapochromic )/PAA− ]15 . Each one operates with a different type of fibre and wavelength, highlight the fall of the dynamic range when working with SMF at 1550 nm.

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Fig. 4. Construction process to increase the sensitivity of the sensors operating at 1550 nm. On the left side, an image obtained from an optical microscope of a sensor head doped with the sensing material that has 1/2 diphenylacetylene ligands.

(increasing the light scattering and making the signal noisy), so it was not modified. Furthermore, the sensing material preserves its luminescent properties along the deposition process.

3.3. Morphology of the depositions In order to check the effect of the sensing material over the morphology of the depositions, the nanostructures were prepared onto glass slides and then, dipped into the vapochromic colloids. Images for each slide obtained from a SEM are shown in Fig. 5; a non doped slide is employed as reference (its roughness is uniform and there are not noticeable porous). On the contrary, the porous structure is much more appreciable in Fig. 5B (1/2 diphenylacetylene ligands), with diameters from 500 to 1000 nm. The porous size in the next image (ammonia ligands) is quite greater, above 1000 nm. These two sensing materials are very reactive, so they get better adsorpted and alter the morphology of the structure. In the case of the compound with pyridine ligands, the porous size is about 1000 nm and they are distributed in a more uniform way. Finally, the morphology of the last deposition (2,2 -bipyridine ligands) is quite similar to the previous one in terms of porous size and distribution. These results demonstrate that the vapochromic materials affect the final morphology of the deposition. The compounds that are better adsorpted (also show the highest reactivity) produce larger porous and a higher roughness that the materials that are less reactive.

Table 1 Comparison between the different construction parameters to enhance the sensitivity of the sensors in terms of dynamic range and recovery response. Construction parameters pHPAH

pHPAA

[NaCl]

8 8.5 8 8.5

8 3.5 8 3.5

0 0 0.2 0.2

Dymanic range (dB)

TRecovery (s)

0.55 5.96 0.46 2.64

5 33 3 24

4. Results and discussion Four sensors were prepared following the steps described in Fig. 4. Each one had a certain sensing material with an specific ligand molecule: Sensor 1 (1/2 diphenylacetylene), Sensor 2 (ammonia), Sensor 3 (pyridine) and Sensor 4 (2,2 -bipyridine). 4.1. Influence of environmental factors The four sensors were inserted into a climatic chamber and there, exposed to cycles of varying relative humidity. Temperature was kept constant at 30 ◦ C (similar to the one set with the thermal jacket), whereas humidity changed from 10% to 90% and down to 15%. Both the high porosity observed in the SEM images and the hydrophilic nature of the polymers, were suppose to produce a high cross correlation with humidity [29]. It can be checked in Fig. 6. Obviously, humidity affects the response of the sensors, although its effect is no so critical in all cases. The reflected power does not change for Sensor 2 and Sensor 3 with a 90% R.H, whereas the signal change is positive for Sensor 3, it is negative in the case of Sensor 2. For this last sensor, the signal suffers a maximum attenuation of 2.70 dB at 60% of relative humidity. In the case of Sensor 3, the greatest variation is registered at this R.H. as well: the signal level is 1.70 dB above its original value. Sensor 4 shows a similar behaviour than Sensor 2 but with an attenuation of 2.32 dB with a 90% R.H. The greatest change observed is 3.75 dB with 65% of humidity. In any case, the device that is affected the most by water vapours is Sensor 1: the maximum attenuation is 7.5 dB with a 90% of R.H. However, the signal slightly changes for humidity values below 65%. The only difference between the construction processes is the vapochromic compound used; thereby, the distinct cross correlations with humidity must be due to the effect of the sensing material over the morphology of the deposition. One possibility to overcome this inconvenience is based on compensating the effect of humidity using the results registered in the previous graph. In applications related to the identification of VOCs mixtures, water vapour could be considered just as another volatile compound that defines the final fingerprint of the odour.

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Fig. 5. Images captured of the depositions with no sensing material (A), and the four ones doped with the materials with different ligands: 1/2 diphenylacetylene (B), ammonia (C), pyridine (D) and 2,2 -bipyridine (E).

4.2. Temporal response of the sensors to single VOCs

Sensor 1

ΔReflected Optical Power (dB)

2

Sensor 2

Sensor 3

Sensor 4

RH

100

1

90

0

80

-1

70

-2

60

-3

50

-4

40

-5 -6

30

-7

20

-8

10

-9 0

50

100

150

200

250

0 300

Time (minutes) Fig. 6. Influence of the relative humidity registered from the sensors.

Relative Humidity (%)

Each sensor was exposed to vapours of methanol, isopropanol and acetone. These VOCs were chosen because they show a different polar behaviour and so, should react in a different way with each sensing material [14]. A volume of 1 mL was injected in each case, exposing the sensors for 4 min to the every organic vapour. The parameters used to evaluate the devices were the dynamic range (considering the maximum signal change) and the recovery time (from the lowest signal level once the chamber is released until it reaches the base line) [30,31]. Results are plotted in Fig. 7 and resumed in Table 2. Sensor 1 exhibits the highest sensitivity, which matches with the fact that also showed a high porosity. The shape of the response for methanol vapours is similar registered for ethanol. In the case of isopropanol and acetone, the signal does not increase its level until the vapours disappear: that is a consequence of the adsorption of the VOCs and their reaction with the sensing material. In any case, the VOCs can be distinguished by the dynamic range and the recovery time. For Sensor 2, the dynamic range is always above 5 dB in every case. The response for methanol vapours looks alike the one from Sensor 1, but smoother. When the chamber is opened, the signal

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Table 2 Dynamic range and recovery time of the optimized sensors with distinct sensing materials for three different organic vapours.

Sensor 1 Sensor 2 Sensor 3 Sensor 4

Dynamic range (dB)

1/2 diphenylacetylene Amonnia Pyridine 2,2 -Bipyridine

TRecovery (s)

MetOH

IsoPropOH

Acetone

MetOH

IsoPropOH

7.46 5.84 0.74 1.38

5.52 5.9 2.08 1.76

6.62 6.77 2.36 1.3

27 61 18 44

31 72 66 21

reaches the base line exponentially for methanol vapours; on the contrary for the other VOCs, the signal changes abruptly to a level over the base line and then, decays to it (this change is the one considered to estimate the recovery time). These differences would allow the distinct VOCs to be identified. The responses of Sensor 3 show lower dynamic ranges: for instance, in the case of methanol vapours, the signal level falls just 0.74 dB, which is the lowest change. Nevertheless, the recovery response for isopropanol and acetone vapours is typical for this device: first, it increases up to a certain level, then it falls and thereafter, it reaches the baseline. These shapes make methanol easily distinguishable from the other vapours because it does not show this behaviour. It is evident that Sensor 4 has a lower dynamic range than the first two sensors: it is a consequence of the lower roughness and therefore, the reactivity of the sensing material. The behaviour is also different because the signal always decreases in the presence of the vapour, reaching a typical level for each VOC. The response is also smoother and shows no noisy ripple: in fact, it is the most stable. Regarding to the recovery time, it can be used together with the dynamic range to identify the vapours unequivocally. 4.3. Applications related with complex mixtures of VOCs In spite of the influence of water vapours, the sensors could be used with samples that include water and organic solvents. Actually, this type of devices have been already probed to handle with wine samples [16] and with water polluted with kerosene [32]. To check this feature, the sensor that shows the highest cross correlation with water vapours (Sensor 1) was exposed to a sample of 10 mL of pure water and to another one of 10 mL mixed with 0.10 mL of kerosene (see Fig. 8). Although the shapes of the signals are similar, the two samples can be distinguished in terms of the power stabilization level and the recovery time. This last parameter is longer in case of the water polluted with kerosene: this fuel contents hundreds of VOCs that interact with the vapochromic material

Sensor 1

ΔReflected Optical Power (dB)

2

Sensor 2

Sensor 3

Sensor 4

1 0 -1 -2 -3 -4 -5

Pure Water

4

ΔReflected Optical Power (dB)

Ligand

12 83 115 33

3.60 dB

Polluted Water

2

Acetone

2.06 dB

0 -2 -4 -6 T recovery = 49.8s

-8

T recovery = 99.6s

-10 -12 0

1

2

3

4

5

6

7

Time (minutes) Fig. 8. Response of Sensor 1 in the presence of water and water polluted with kerosene.

and they need time to get deadsorpted. The recovery time obtained with water is shorter because there are no VOCs to interact with the sensing material.

5. Conclusions In order to take the chance of multiplexing sensors and use them remotely, SMF has to be used to develop them. Moreover, they have to operate at 1550 nm. These changes produce a sensitivity loss when the sensors are fabricated in a similar way than with MMF and to work at 850 nm. This problem is overcome easing the adsorption of the VOCs by increasing the porosity of the sensing layer. Using the polyelectrolytes involved in the ESAm with a low degree of ionization guaranties a porosity that enhances the sensitivity without increasing too much the light scattering. As a result, the dynamic range is enhanced but the cross correlation with humidity is increased perceptibly. In any case, each sensor responds in a distinct way for single VOCs. The devices can be characterized in terms of the stabilization power level during the vapours exposure and the recovery time. Although the sensors are affected by water vapours, they can be used in applications related with the identification of complex mixtures of VOCs in less than 5 min. This offers many possibilities, such as the monitoring of pollutants or fermentation processes in situ and on line. Finally, it is important to remark that the sensors are ready to be multiplexed and also to operate remotely.

-6 -7

Acknowledgements

-8

MetOH

IsoPropOH

Acetone

-9 0

5

10

15

20

Time (minutes) Fig. 7. Temporal responses from the sensors to atmospheres saturated with methanol, isopropanol and acetone vapours respectively.

The authors would like to acknowledge the financial support from the Spanish Ministerio de Educación y Ciencia through projects TEC2010-17805 and TEC2010-20224-C02-01. The collaboration of Professor Lopez-Amo and Dr. Perez-Herrera is acknowledged as well.

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Biographies Cesar Elosua received his M.S. degree in electrical and electronic engineering from the Public University of Navarra (UPNA, Pamplona, Spain) in 2004. In the same year, he obtained a scholarship from the Science and Technology Spanish Ministry and he joined the optical fibre sensor group at the Department of Electrical and Electronic Engineering of the UPNA. During 2008, he was a visiting Ph.D. student at the University of Limerick and at the City University of London, working on Artificial Neural Networks algorithms. He became a lecturer of this department in 2009, receiving his PhD degree in the next year. His research interests include optical fibre sensors and networks, organometallic chemistry and data mining techniques. Candido Bariain received his MS degree in telecommunication from the Polytechnic University of Madrid (UPM, Madrid, Spain) in 1990. He received his Ph.D. degree in Communications from the Public University of Navarra (UPNA, Pamplona, Spain) in 2002. He is currently in the Department of Electrical and Electronic Engineering at the Public University of Navarra. His research interests include fibre optic sensors and optical networks. Asunción Luquin received her B.Sc. in chemistry from the University of Zaragoza, Spain, in 2000. She joined the inorganic chemistry department at the University of Zaragoza and the Applied Chemistry Department at the Public University of Navarre. There, she obtained the Ph.D. degree in chemistry in 2004. Her research interests include synthesis of organometallic and coordination compounds as inorganic materials. Mariano Laguna was awarded the B.Sc. in chemistry (1971) and his Ph.D. (1974) by the University of Zaragoza. He accepted a senior researcher position at the CSIC in 1987, and in 1990 he was promoted to professor. He is the author of more than 150 papers related to organometallic and coordination properties of several transition metals, mainly coinage 11 metals group. He has been a visiting professor at several foreign universities. His current interests are polyfunctional thiolate complexes and the study of their properties as electrical conductors, luminescent behaviour, and VOC recognition. Ignacio R. Matias is a Professor in the Electrical and Electronic Engineering Department at the Public University of Navarra, Spain. He received his M.S. (1992) and Ph.D. (1996) in electrical and electronic engineering from the Polytechnic University of Madrid. He has coauthored more than 200 book chapters, journal and conference papers related to optical fibre sensors and electronic applications.