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Influences of Al, Pd and Pt additives on the conduction mechanism as well as the surface and bulk properties of SnO2 based polycrystalline thick film gas sensors
Sensors and Actuators B 171–172 (2012) 172–180
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Influences of Al, Pd and Pt additives on the conduction mechanism as well as the surface and bulk properties of SnO2 based polycrystalline thick film gas sensors M. Hübner ∗ , N. Bârsan, U. Weimar Tübingen University, Faculty of Science, Department of Chemistry, Institute of Physical and Theoretical Chemistry, Auf der Morgenstelle 15, 72076 Tübingen, Germany
a r t i c l e
i n f o
Article history: Received 25 November 2011 Received in revised form 23 February 2012 Accepted 26 February 2012 Available online 22 May 2012 Keywords: Doped SnO2 Conduction mechanism CO H2 Oxygen
a b s t r a c t The reasons of the effect of Pd, Pt and Al additives on the sensing and conduction mechanism of SnO2 based thick film porous gas sensing layers are studied by a combination of DC-resistance, work function changes and catalytic conversion measurements. This is done by analyzing the dependence of the DC resistance on the corresponding band bending changes over a large range and the use of previously reported conduction models. The gained information deals with the surface band bending in the absence of ambient atmosphere oxygen, the position of the Fermi level, the concentration of free charge carriers, the Debye length and the width of the surface charge layer in various ambient conditions. Very interestingly, we found that in all cases the “doping” had an impact on both surface and bulk properties even if the additives and the technology of “doping” were targeted towards surface activation, in the case of Pt and Pd, and bulk compensation of donors, in the case of Al. Besides that, the catalytic conversion experiments indicated that the presence of Pt is associated with the reduction of the material in the absence of ambient atmosphere oxygen. © 2012 Elsevier B.V. All rights reserved.
1. Introduction In the field of gas sensors based on semiconducting metal-oxides the ones based on thick, porous layers of SnO2 (in combination with noble metals additives) are still the most used, especially for the detection of reducing gases [1,2]. Surprisingly, even after more than 40 years of research, the base material is still the subject of many investigations and still offers surprises. We recently demonstrated that the conduction mechanism in SnO2 polycrystalline, porous layers changes from one controlled by surface depletion layers to one controlled by surface accumulation layers and the experimental basis was provided by simultaneous DC resistance and work function changes measurements, which were combined with semiconductor calculations [3]. Because we consider that the findings have a considerable impact over the understanding of the sensing with n-type semiconducting oxides we decided to extend our investigation from a model system, undoped SnO2 , towards more practical materials. To improve the lack of selectivity and to reduce the optimum operation temperature of SnO2 based gas sensors, they are generally “doped” with noble metals such as Pd and Pt [4–6]. These materials are normally synthesized in such a way to have the noble
∗ Corresponding author. Tel.: +49 7071 78766; fax: +49 7071 5960. E-mail address: [email protected] (M. Hübner). 0925-4005/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2012.02.080
metal additives on the surface; consequently their influence should be limited to a surface effect which determines the improved performance and this is also the way in which their role is modeled. The chemical state of these “dopants” could be recently clarified by the use of “operando” XAS spectroscopy; both Pd and Pt are present in an oxidized state and they are dispersed at an atomic level [7,8]. To change the electrical properties of SnO2 , the bulk doping method is generally applied. By the addition of donor impurities (Sb and F) into the material, one is able to increase the conductivity because an extra electron is available in the lattice. The opposite effect can be achieved by using an impurity element having one electron less than Sn in the outer shell. By replacing some of the Sn4+ sites by Al3+ , one creates therefore acceptor levels in the band gap. These levels determine an increase of the resistance because the effect of the oxygen vacancy donor levels is compensated by the acceptor levels. This implies that the position of the Fermi level is moving away from the conduction band edge towards the middle of the band gap and one is, consequently, changing the bulk properties of SnO2 . The effect of Al, Pd and Pt additives on the conduction mechanism of SnO2 is in our focus in this contribution. By combining the basic knowledge from the undoped SnO2 with simultaneous DC-resistance and work function changes measurements on the doped materials, we are able to figure out the influences on the conduction mechanism and the properties of SnO2 . Especially, the intended effect of the additives, namely the surface sensitization
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of Pd and Pt and the change of the electric bulk properties by Al is discussed. 2. Experimental details and results 2.1. Sample preparation and experimental conditions The co-precipitation method was used to obtain Al doped SnO2 . Therefore AlCl3 was mixed in the intended weight percentage (0.3 and 3 wt.%) with an aqueous solution of SnCl4 . The drop wise addition of ammonia led to the precipitate which was consequently washed several times with bi-distilled water. Finally the Al doped Sn(OH)4 was calcined at 1000 ◦ C for 8 h to get to the oxide. XRD measurements revealed that no second phase related to Al2 O3 or metallic Al could be found in the diffraction patterns of the doped samples. The crystallite sizes of both Al doped samples were in the range of 82 nm (estimated from XRD). On the contrary, gel-impregnation was used for the addition of Pd and Pt. The gel was obtained by the drop wise addition of ammonia to an aqueous solution of SnCl4 . Afterwards, the Pd and Pt additives were introduced in the nominal weight concentration of 0.2 wt.% in forms of the chlorides (PdCl2 and PtCl4 ). The resulting solution was washed again with bi-distilled water and finally calcined at 1000 ◦ C for 8 h. The average crystallite size – estimated from XRD – is around 60 nm. More details and the full characterization can be found in [9]. Screen printing technique was used to obtain porous thick film layers (50 m thick); the as-synthesized powders were mixed in an appropriate amount with an organic binder (propane diole) until a homogenous, printable paste was obtained. The latter was printed on alumina substrates (25.4 mm × 4.2 mm) provided with interdigitated Pt electrodes – for the electrical readout – and a Pt heater on the backside allowing the operation at well controlled temperatures (300 ◦ C). The as-obtained sensors were dried at room temperature for 12 h before they underwent a final thermal treatment in a moving belt oven (400–600 ◦ C). The following measurements were performed for the Pd and the Pt doped SnO2 sensors, both operated at 300 ◦ C. For the two differently Al doped samples (0.3 and 3 wt.%), only the simultaneous DC-resistance and work function measurements were applied, again at an operation temperature of 300 ◦ C: • Simultaneous DC-resistance (by using a multimeter Keithley DMM 199) and catalytic conversion measurements (by connecting the sensor chamber to a photo acoustic gas analyzer Innova 1312) of two Pd doped SnO2 sensors under exposure to 4 CO pulses (50, 100, 200, 300 ppm) and of two Pt doped SnO2 sensors, exposed to 4 CO pulses (10, 30, 70, 100 ppm), in the absence of oxygen (<3 ppm; below the detection limit of the oxygen analyzer Zirox SGM400). Similar experiments were also performed in different oxygen containing backgrounds like in the case of undoped SnO2 [3]. The sensors were stabilized at 300 ◦ C in dry N2 for 24 h before starting the experiment. Exposure time of CO with a flow of 500 ml/min was set to 3 h (2 h for the Pt doped samples); for recovery 3 h in the corresponding atmosphere were allowed. • Simultaneous DC-resistance (constant voltage mode using an electrometer Keithley EMM 617) and work function changes (˚) measurements (using a Mc Allister KP 6500 Kelvin Probe). Also here all the samples (Pd, Pt and Al doped SnO2 ) were stabilized in the desired background condition for 24 h before starting the measurements. A flow of 470 ml/min was adjusted by a computer controlled gas mixing system. All sensors were exposed to several concentrations of CO (10, 30, 70, 100 ppm and additional 2 and 5 ppm for the Pt doped sample) and H2 (2, 5, 10, 30, 70, 100 for the Pd and the 0.3 wt.% Al doped sample; 1, 3, 7, 10, 30, 70,
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100 ppm for the Pt doped one and 10, 30, 70, 100, 200 ppm for the 3 wt.% Al doped one) for 2 h in different oxygen backgrounds (no oxygen – which was in fact around 12 ppm – 200, 2000 and 22,000 ppm). Further, the influence of oxygen it self was investigated by exposing the samples to stepwise increasing oxygen concentrations (200, 2000 and 22,000 ppm) in a background of N2 . A detailed description about the functioning of the Kelvin probe set-up can be found in [10]. 2.2. Experimental results 2.2.1. Simultaneous DC-resistance and catalytic conversion measurement The sensing mechanism of Pd and Pt doped SnO2 thick film layers was investigated by simultaneous DC-resistance and catalytic conversion measurements upon exposure to CO in the, almost, absence of oxygen (oxygen contamination <3 ppm). The time dependencies of the resistance of two Pd doped samples, both prepared in the same way, upon exposure to four CO pulses (50, 100, 200 and 300 ppm) in a “non” oxygen containing background at an operation temperature of 300 ◦ C are presented in Fig. 1 (left side, lower part). A very large drop in the resistances of the Pd doped SnO2 sensors is observed when exposed to the target gas (more than four orders of magnitude) with the steady sate (equilibrium) being reached quite fast. The allotted 3 h of recovery are enough to get back to the initial baseline. Checking the gas composition after the sensor chamber, no CO2 as a reaction product of CO sensing can be monitored; only CO can be detected by the gas analyzer (see Fig. 1, left side, upper part). This finding indicates, like it was also found for undoped SnO2 [3,11], that the reduction of the material (consumption of lattice oxygen) as a reason for the resistance changes during CO sensing can be ruled out. By increasing the oxygen amount in the background (results not shown here), the same trend, previously reported for the undoped samples [3,11], was also observed for the Pd doped sample, namely a strong decrease of the signal when compared to the situation encountered in (almost) no O2 . It seems that the sensing mechanism of Pd doped SnO2 sensors, exposed to CO in different oxygen backgrounds, is very similar to the one of the corresponding undoped material [3]. On the contrary, similar measurements performed on Pt doped SnO2 , prepared in the same manner, demonstrated a complete different behavior and can be also seen in Fig. 1 (right side). Also there a huge drop in the resistance can be observed upon CO exposure in the absence of oxygen, but no recovery back to the baseline takes place as long as no oxygen was present. Furthermore, the production of CO2 in the exhaust can be identified (see Fig. 1, right side, upper part). This indicates that, probably due to the addition of Pt, also the reduction of SnO2 is playing a role in sensing in the absence of oxygen. 2.2.2. Simultaneous DC resistance and work function measurements of Pd and Pt doped SnO2 Simultaneous DC-resistance and work function changes measurements are used to determine the relationship between the surface band bending and the resistance depending on the surrounding conditions and, therefore, some information about the conduction models [3]. An example for the time dependence of both, the resistance and the CPD (˚ = −CPD), upon exposure to several CO pulses (10, 30, 70 and 100 ppm) and stepwise increasing H2 concentrations (2, 5, 10, 30, 70, and 100 ppm) in the absence of oxygen of a Pd doped SnO2 sensor is depicted in Fig. 2. The decrease in the resistance as well as in the work function for both target gases is pretty small as long as their concentrations are smaller than the one of the residual oxygen in the background (∼12 ppm). In the moment when the reaction takes place at target gas concentrations larger than the residual oxygen amount, one records
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Fig. 1. Time dependencies of the resistance during CO exposure (50, 100, 200 and 300 ppm) in the absence of oxygen at 300 ◦ C for two 0.2 wt.% Pd doped SnO2 sensors (left side) and the time dependencies of the resistance during CO exposure (10, 30, 70 and 100 ppm) in the absence of oxygen at 300 ◦ C for two 0.2 wt.% Pt doped SnO2 sensors (right side). The amount of CO and CO2 is presented in the upper part.
huge changes for both parameters (changes in the resistance of several orders of magnitude). In order to cover a huge range for the band bending changes, similar experiments were also performed in different oxygen backgrounds (200 and 2000 ppm) and also the influence of oxygen alone was investigated. An overview of all the measurements is given in Fig. 4 (left side), by plotting the dependence of the resistance on the changes in the band bending – assuming that in the experimental conditions the only reasons for changes in the work function are the one of the band bending – over the whole experimental range. This assumption is made credible by the extremely low concentration of water vapor in the test atmospheres, below 3 ppm (due to our measurements), because the main reason of the changing of the electronic affinity are the surface dipoles associated to the water vapor originating surface hydroxyl groups. The changes in the band bending where directly extracted from the measured changes in the work function like it was recently done for the undoped SnO2 [3]. As a reference point (qV = 0), the situation in dry N2 was chosen. The existence of two regions where a linear fit gives very good results (very small errors), is obvious. This fit indicates an exponential dependency between the resistance and the band bending changes with the slopes 1/[(1 ± 0.05)kT] and 1/[(2.33 ± 0.01)kT] respectively. Comparing the values to the ones obtained from the theoretical modeling given in [3], one region corresponds to the depletion layer model – theoretical value of 1, experimental value 1 ± 0.05 – and the other to the accumulation layer model – theoretical value of 2, experimental value 2.33 ± 0.01 – where the Boltzmann statistics is valid. In the used concentration range of the target gases, one is therefore all the time moving between these two models depending on the surrounding condition. That means that there is a seamless transfer between the depletion layer controlled
model and the accumulation layer controlled one, but the degeneration of the electrons at surface, like in the case of the undoped material [3], could not be observed for the Pd doped samples. Unfortunately, the used conditions did not provide data points located exactly at the switching point between these two regions. Nevertheless the point can be estimated by the crossing point of the two fitting lines (∼−0.43 eV). The raw data from the simultaneous DC-resistance and work function changes measurements of a Pt doped SnO2 sample upon exposure to CO and H2 in the almost absence of oxygen can be found in Fig. 3. The changes of both parameters (CPD and Resistance) are again small at low target gas concentrations (smaller than the residual oxygen amount). At higher ones, enormous changes can be measures. Also for this sample, similar measurements in oxygen containing backgrounds were performed. The overview (dependence of the resistance on the band bending changes) from the measurements of the Pt doped sample is shown in Fig. 4 (right side); the overall picture is completely different when compared to the Pd doped sample one: • at higher resistances, one can identify two regions with a seamless transfer. One region could be fitted by a dependence corresponding to the one of the depletion layer model (theoretical value of 1, experimental value of 1.12 ± 0.01) and the second dependence fitted quite well with the value obtained for the accumulation layer model (theoretical value of 2, experimental value of 2.26 ± 0.02); • in the case when the reaction is dominated by the reducing gas concentration (higher than the one of the residual oxygen concentration), a break in the relationship between resistance and band bending is observed followed by a region in which the
Fig. 2. Simultaneous contact potential differences (CPD) and electrical resistance changes of a Pd doped SnO2 sensor during exposure to CO pulses (10, 30, 70 and 100 ppm) (left) and during exposure to stepwise increasing H2 concentrations (2, 5, 10, 30, 70 and 100 ppm) (right) in the absence of oxygen at 300 ◦ C.
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Fig. 3. Simultaneous contact potential differences (CPD) and electrical resistance changes of a Pt doped SnO2 sensor during exposure to CO pulses (2, 5, 10, 30, 70 and 100 ppm) (left) and during exposure to H2 pulses (1, 3, 7, 10, 30, 70 and 100 ppm) (right) in the absence of oxygen at 300 ◦ C.
relationship is described by an exponential function with a rather low exponent value (1/[(6.41 ± 0.24)kT]). In the catalytic conversion measurements in the concentration range corresponding to the break we recorded the generation of CO2 as an indicator of the reduction of the material. 2.2.3. Simultaneous DC resistance and work function measurements of Al doped SnO2 The influence of different Al doping levels (0.3 and 3 wt.%) on the conduction mechanism of undoped SnO2 was investigated by the use of simultaneous DC-resistance and work function changes measurements upon exposure to CO and H2 in different oxygen backgrounds similar to the ones shown in the latter chapter for the Pd and Pt doped samples. The time dependencies of the resistance as well as the CPD of a 0.3 wt.% Al doped SnO2 sensor during exposure to 4 CO (10, 30, 70 and 100 ppm) and 6 H2 (2, 5, 10, 30, 70 and 100) pulses in the almost absence of oxygen are presented in Fig. 5. One observes the expected decrease of both, the resistance as well as the work function, when exposed to CO and H2 . By comparing the baseline resistance of the Al doped sample (∼1 M) with the one of the undoped SnO2 (∼60 k) [3], both operated in the same conditions, the increase in the baseline resistance due to the presence of Al is obvious. Similar experiments, exposure to CO and H2 in the absence of oxygen, performed for the 3 wt.% Al doped sample are given in Fig. 6. The overall trend is in line with the results recorded for the 0.3 wt.% Al doped sample. The addition of more Al further increases the baseline resistance (∼3 M). To cover a large range of the band bending changes, similar measurements were again done in the presence of different oxygen backgrounds (not shown here). The observations in the presence of oxygen are the expected ones: the baseline resistance increases with increasing oxygen level whereas the signal strength decreases.
An overview of all measurements, presented by the dependence of the resistance and the corresponding band bending in the different conditions, is given for the 0.3 wt.% sample in Fig. 7 on the left side and for the 3 wt.% sample on the right side. Again the situation in dry N2 was taken as a reference point (qV = 0). Also here the changes of the band bending were directly extracted from the measured changes in the work function by assuming that in these conditions one is not expecting any changes of the electron affinity. In the case of 0.3 wt.% Al doped SnO2 a seamless transfer between three regions can be observed. The region where a linear fit is giving the highest slope matches quite well with the one described by a depletion layer model (theoretical value of 1, experimental value of 1.1 ± 0.06). Reaching a band bending change of around −0.1 eV, the slope is decreasing to an experimental value of 1.58 ± 0.35. This region may be described by an accumulation layer model where the Boltzmann statistics are still valid (theoretical value of 2). At a band bending change of around −0.4 eV the slope decreases further (experimental value 3.57 ± 0.51) representing the region with the degeneration of the concentration of the charge carriers for the accumulation layer [3]. The overall picture of the 3 wt.% Al doped SnO2 sensor (Fig. 7, right side) is a bit different. Only two regions where a linear fit gives good results, with a seamless transfer between each other, can be obtained. The tendency of the experimental values in an oxygen containing background (besides the two highest H2 concentrations in 200 ppm of oxygen) fits well with the depletion layer model from the theoretical modeling (theoretical value 1, experimental value 1.19 ± 0.04). At a band bending change of −0.3 eV one moves into a conduction mechanism controlled by the accumulation layer model where the Boltzmann statistics still apply (theoretical value 2, experimental value 2.17 ± 0.39). The degeneration at the surface in the accumulation layer (further decrease of the slope) cannot be observed in the applied gas concentration range.
Fig. 4. Dependence of the resistance and the corresponding band bending for the Pd doped SnO2 sensor (left side) and for the Pt doped SnO2 sensor (right side). The reference (qV = 0) is denoted as the situation in N2 .
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Fig. 5. Simultaneous contact potential differences (CPD) and electrical resistance changes of a 0.3 wt.% Al doped SnO2 sensor during exposure to CO pulses (10, 30, 70 and 100 ppm) (left) and during exposure to H2 pulses (2, 5, 10, 30, 70 and 100 ppm) (right) in the absence of oxygen at 300 ◦ C.
Fig. 6. Simultaneous contact potential differences (CPD) and electrical resistance changes of a 3 wt.% Al doped SnO2 sensor during exposure to CO pulses (10, 30, 70 and 100 ppm) (left) and during stepwise increasing H2 concentrations (10, 30, 70, 100 and 200 ppm) (right) in the absence of oxygen at 300 ◦ C.
3. Discussion about the influence of the different dopants The results from the DC-resistance and catalytic conversion measurements of the Pd doped SnO2 sample in the almost absence of oxygen are ruling out the reduction of the material as a reason for the huge resistance changes. In contrast, for a similar sample doped with Pt instead of Pd, we could demonstrate that the consumption of lattice oxygen by CO (CO2 production) in this condition has to be taken into consideration. This fact and the tendency of decreased signals in oxygen containing backgrounds indicates, for the case of Pd, that the basic sensing mechanism – depending on the oxygen amount – seems to be similar to the one proposed for the undoped material [3,11]. The presence of the Pd determines an increase in the baseline resistance and a general increase of the sensor response. By performing simultaneous DC-resistance and work function changes measurements in conditions in which there are no reasons for the change of the surface dipole concentration but which are allowing for scanning a large range of the resistance, one gets a direct access to the relation between the resistance and the
corresponding band bending. These physical parameters determine the conduction processes in the sensing layer and their experimental relationship can be confronted with various theoretically modeled conduction mechanisms. It could be demonstrated by plotting the dependence between these two parameters over a large range that for undoped SnO2 there is a seamless transfer of the conduction mechanism from a depletion layer controlled model to an accumulation layer controlled one [3]. The switch between these two models directly appeared in dry nitrogen, indicating that in this condition the energy band situation is described by a flat band case. To identify the influences of the different additives, this condition was consequently chosen as the reference point for all the other samples. With further increasing band bending changes, its effect on the resistance is getting weaker indicating a degeneration of the electrons at the surface. The behavior in the latter case could be numerically modeled [3]. Quantization effects are expected in the case of narrow accumulation layers and degenerate electron gas conditions at the surface of semiconducting materials [12]. In our experiments, at rather
Fig. 7. Dependence of the resistance and the corresponding band bending for the 0.3 wt.% Al doped SnO2 sensor (left side) and for the 3 wt.% Al doped SnO2 sensor (right side). The reference (qV = 0) is denoted as the situation in N2 .
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high temperatures, it is very unlikely that they would be observed because the thermal energy noise (of the order of magnitude of kT), will make the energy differences between these quantization levels not relevant. Because the effects are to be expected only in the case of degeneration, they need to be taken into account only after the crossing of the Fermi level by the conduction band minimum at the surface. Hence, in conditions where the accumulation layer is described by the analytical solution (Boltzmann statistics valid), the quantization effects cannot be relevant for the relationship between the resistance and the corresponding surface band bending. Coming back to the comparison of the analytical and the numerical solutions that describe how the average electron concentration in the accumulation layer depends on the surface band bending, it was found that both trends are similar (same slope) up to around 0.3 eV after the crossing of the surface conduction band edge with the Fermi level position; with further increasing of the surface band bending, the trends are strongly diverging, which reflects the lack of appropriateness of the Boltzmann approximation. Hence, by knowing the flat band situation (switch from the depletion layer model to the accumulation layer one) and the point at which the Boltzmann statistics start to get invalid, one can estimate at which value the conduction band at the surface crosses the Fermi level and by that the position of the Fermi level in the bulk (EC,b − EF ). Also, a possible initial band bending in N2 can be estimated by this approach. By using this reasoning, the influences of the different additives on the surface (initial band bending) and bulk (position of the Fermi level) properties of SnO2 can be determined. An overview of the surface and bulk influences of the different additives compared to the undoped material in dry nitrogen atmosphere is presented in Fig. 8. The situation for the reference SnO2 material (black lines) in this condition is described by a flat band case. The position of the Fermi level was estimated to be located at around 80 meV below the conduction band. In case of Pd addition (red), a huge initial surface band bending in N2 of around 430 meV can be found from the resistance versus band bending plot, which is not related to ionosorbed oxygen. This fact indicates a considerable surface effect of Pd, probably caused by the existence of surface defects acting as electron traps. This demonstrates the impact on the surface properties of Pd “doping”. The position of the Fermi level lies at a distance of at least 290 meV below the conduction band because one is not observing the change of the slope for the accumulation layer in the applied concentration range. This moving of the Fermi level position shows that the addition of Pd also has a tremendous bulk effect, which is not the intention of the “doping” and it is not taken, generally, into consideration. The surface influence of Pt (green) as a dopant is not that effective. From Fig. 4 one can estimate an initial band bending of approximately 100 meV. Due to the experimentally proven material reduction and, therefore, the change of the material, one cannot estimate the position of the Fermi level in the Pt doped SnO2 sensor. In case of 0.3 wt.% Al (dark blue) a small initial band bending of around 50 meV can be observed. The degeneration of the electrons at the surface starts at a band bending value of approximately −430 meV (see Fig. 7). Consequently, the Fermi level position for this doping level should be located at around 130 meV below the conduction band edge. For the higher value of Al doping, the switch from the depletion layer into the accumulation layer starts at a band bending of around −300 meV which consequently implies a huge initial band bending in N2 of around 300 meV related to surface defects acting as electron traps. Under exposure to 100 ppm of H2 in N2 a band bending change of −870 meV was measured. In this condition the degeneration at the surface did not yet start which implies that the
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Fig. 8. Overview of the influence of the different additives (Pd – red; Pt – green; 0.3 wt.% Al – dark blue; 3 wt.% Al – light blue) on the surface band bending and the position of the Fermi level of the host SnO2 material (black) in nitrogen. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fermi level position should be located at least 270 meV below the conduction band edge. The findings presented above indicate the potential of the investigation method; one does not only get information about the different conduction models, but it is also possible to identify and estimate surface effects in terms of initial band bending in N2 as well as bulk effects (position of the Fermi level) determined by the addition of different additives. In the cases presented here, it could be demonstrated that both “doping” techniques – gel impregnation for Pd and co-precipitation for Al – are influencing the surface as well as the bulk properties of the host material (SnO2 ). After the estimation of the initial band bending in N2 and the position of the Fermi level of the different materials, the next interesting step is to calculate the different Debye lengths to check whether the assumptions made in the modeling about porous thick films with large grains – large enough to have a bulk region unaffected by surface phenomena (d LD ) – are correct or not. The Debye length LD is defined, in the Schottky approximation, as [13]:
LD =
kTεε0 q2 nb
(1)
In order to calculate the different values of LD we need the values for nb representing the electron concentration in the conduction band at a certain temperature (300 ◦ C in our case). The Boltzmann distribution for the calculation of nb can be used as long as the donor concentration ND of the material is much smaller than the
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Table 1 Position of the Fermi level (EC − EF ), the corresponding electron concentration in the conduction band (nb ), the calculated values of the Debye length (LD ) and the calculated values for x0 in N2 (A), upon exposure to 2000 ppm of O2 (B) and upon exposure to 100 ppm of CO in 200 ppm of O2 for undoped and 0.3 wt.% Al doped SnO2 and 100 ppm CO in N2 for Pd and 3 wt.% Al doped SnO2 (C). Material
EC − EF (meV)
nb (cm−3 )
LD (nm)
x0 in A (nm)
x0 in B (nm)
x0 in C (nm)
SnO2 0.3 wt.% Al 3 wt.% Al 0.2 wt.% Pd
80 130 270 290
2.14 × 1018 7.77 × 1017 4.57 × 1016 3.05 × 1016
3.6 6.0 24.4 30.0
0 8.4 85.2 124.9
7.2 12.5 97.1 130.6
4.9 7.6 30.0 39.4
total number of states in the conduction band, NC . This can be easily shown by taking as the donor concentration the measured value from Samson et al. [14] (ND = 9 × 1015 cm−3 ) and the definition of NC [15]:
NC ≡ 2 2mn
3/2 kT h2
(2)
At the operation temperature of 300 ◦ C one obtains for NC a value of 1.08 × 1019 cm−3 which definitely meets the requirements to use the Boltzmann distribution function. Hence we can write: nb = NC exp
E − E F C kT
(3)
With the estimated values for the Position of the Fermi level we get the values for nb and therefore the different Debye lengths. All the values are presented in Table 1. Comparing the calculated values for LD with the measured dimensions of the grains (particle size ∼200 nm and crystallite size ∼70 nm for undoped SnO2 [11], 65 nm for Pd doped [9] and 82 nm for the Al doped samples) indicates that the assumptions made in the modeling are correct. There are two regions for all grains, namely a surface space charge layer and an unaffected bulk region. Another way to calculate the value of nb for undoped SnO2 and, therefore, to prove the validity of the latter calculations is to extract it from the measured DC resistance values. The values obtained in N2 are the most appropriate ones because of the flat band situation, which guarantees an uniform value of the free charge carrier concentration throughout the whole grain. The resistance of a layer with length l and cross section A is defined as: R=
1 1 = A ·A
(4)
where is the resistivity and is the conductivity of the layer. The conductivity can be expressed by the product of the charge q, the effective mobility of the layer and the electron concentration nb [16]: = q · · nb
aspect ratio AR and can be estimated. Consequently, we obtain instead of Eq. (6): R=
1 l = q · · nb · t · AR q · · nb · l · t · AR
(7)
The question is, now, how thick is the contact area t of two square elements which the current has to pass. The maximal value for the overlap between two single grains can be approximated with the Debye length because of the exponential dependency of the DC resistance on the band bending; otherwise, the influence of the surface will not be the decisive factor in the modulation of the sensing layer resistance: That means approximately 3.6 nm. For an average grain size of 200 nm one can place 250 grains in one row in a 50 m thick layer. Consequently, one obtains for the effective layer thickness t 900 nm. The schematic in Fig. 9 helps for the better understanding of the different geometrical parameters. The aspect ratio, representing the total number of squares in between the electrodes was calculated to be around 70. The electron concentration in the conduction band nb can now be easily calculated by using Eq. (8) and the measured value of the DC resistance in N2 : nb =
1 q · · R · t · AR
(8)
For the effective mobility in such thick film porous layers, a quite low value of approximately 0.1 cm2 V−1 s−1 was measured [17]. By using the latter value for the mobility, the elementary charge q = 1.602 × 10(−19) C, the resistance of the sensor in dry N2 – 50 k˝ – and the calculated values for t and AR one obtains: nb = 1.98 × 1017 cm−3 . The calculated value of nb is similar to the one in Table 1 for SnO2 which gives some confirmation of the validity of the calculations.
(5)
Combining the latter two expressions one obtains: R=
1 q · · nb · A
(6)
To get rid of the unknown geometric parameters l and A in Eq. (6) one can apply the following reasoning: the layer that gives the resistance can be divided in a number of square shaped layers with the length and width l and the thickness t placed between the electrodes; the thickness t is not the thickness of the layer because the grains are only loosely agglomerated; it is an effective layer thickness that depends on the average overlap between neighboring grains. The cross section for each of the elemental squares can be expressed by the product l multiplied with t. The number of these squares in between the whole electrode structure is called the
Fig. 9. Schematic drawing explaining how one can estimate the contact areas t of two square elements.
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Fig. 10. The energy band situation in N2 (black curve), upon exposure to 2000 ppm of O2 (red curve) and 100 ppm CO in (i) 200 ppm O2 (for the undoped and the 0.3 wt.% Al doped sample) and in (ii) N2 (for the Pd and the 3 wt.% Al doped sample) (green curve) of SnO2 , 0.3 wt.% Al, 3 wt.% Al and 0.2 wt.% Pd doped SnO2 . The conditions in which the flat band situation is reached are shown in blue. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
A further parameter which represents the influence of the different additives and the different models is the thickness of the space charge layer x0 . For its calculation one either has to use Eq. (9) in case of the depletion layer model [13]
x0 =
2VS εr ε0 qnb
(9)
or relation 10 for the accumulation layer controlled one [3]: x0 =
√
2 · LD · 1 − exp
qV S
2kT
(10)
In Table 1 an overview of the calculated x0 values for all materials (besides the Pt doped because in this case one cannot estimate the position of the Fermi level) in nitrogen, upon exposure to 2000 ppm of O2 (depletion layer model) and 100 ppm of CO in 200 ppm O2 (for undoped and 0.3 wt.% Al doped SnO2 ) and in N2 (Pd and 3 wt.% Al doped SnO2 ) (accumulation layer model) is given. To get a better idea about the different values, an schematic overview about how the thickness of the space charge layer x0 and the corresponding band bending changes are related and influencing the energy band situation in N2 (black), upon exposure to 2000 ppm of O2 (red) and 100 ppm CO in 200 ppm O2 and in N2 (green) for undoped, the two Al and the Pd doped SnO2 is presented in Fig. 10. As already mentioned, the situation in N2 for undoped SnO2 is described by a flat band situation (x0 = 0). Upon exposure to 2000 ppm of O2 an upward band bending of 100 meV is measured whereas CO exposure in 200 ppm O2 determines a downward band bending of 340 meV. Although the latter relative change in the band bending is 3 times higher compared to the upward band bending caused by oxygen exposure, the calculated value for x0 in case of the depletion layer is larger (7.2 nm compared to 4.9 nm). This indicates that the space charge layer in case of the accumulation is very thin although the potential difference between the surface (positively charged) and the bulk is quite high [11]. The doping with 0.3 wt.% Al implies an initial band bending of around 50 meV; the effects of 2000 ppm of oxygen and 100 ppm of CO in 200 ppm O2 are reduced and the values for x0 are increased due to the moving of the Fermi level. Upon exposure to 30 ppm of CO in a background of 200 ppm of oxygen one reaches the flat band
situation. The increase of the Al concentration (3 wt.%) dramatically increases the initial band bending and the Fermi level is shifted further towards the middle of the band gap. This leads to much larger values for the thickness of the surface space charge layers. The oxygen influence (2000 ppm) is comparable to the undoped SnO2 ; the effect of 100 ppm CO in N2 is rather big but one is not able to get to the degeneration in this conditions. The flat band situation is reach upon exposure to 30 ppm of CO in N2 . For the Pd doped sample one obtains the highest initial band bending in N2 and the largest value for the Debye length. Hence, also the values for x0 are much higher compared to the undoped SnO2 material. The effect of oxygen exposure is very weak most probably due to the high initial band bending related to the Pd associated surface traps. During exposure to 100 ppm of CO in N2 the largest change in the band bending is measured. For the flat band situation no data point was obtained in the applied combinations of H2 , CO and O2 . Fig. 10 gives a good overview about how the doping with Al and Pd influences the surface and bulk properties of SnO2 . By combining again the theory and the experiment, one is able to estimate different parameters of the materials, like the electron concentration in the conduction band nb , the Debye length and therefore also the thickness of the space charge layer at the surface. 4. Conclusion and outlook Based on recently published theoretical calculations and experimental results on SnO2 , we were able to demonstrate the different influences of Pd, Pt and Al dopants on the sensing and conduction mechanism of the host material. It was found that the sensing of CO in the absence of oxygen in case of the Pd doped sensor is very similar to undoped SnO2 whereas the reduction of the material has to be taken into consideration in case of Pt doping. We were also able, by examining the dependence of the resistance and the corresponding band bending changes and comparing it to the theoretical calculations, to present a new, elegant way not only to identify the conduction mechanism depending on the surrounding conditions, but also to explore the surface and bulk effects of the different additives; we think that the approach we used will prove itself to be a very effective tool for the investigation of both surface and bulk
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properties of semiconducting oxides used as gas sensing materials in operando conditions. In this way we could demonstrate that the co-precipitation as well as the gel-impregnation method used for the addition of the dopants both influences the surface and the bulk properties of SnO2 . In the near future we will focus on sample realized in a way in which the likelihood of surface only effect of additives is much higher, namely the impregnation of already thermally treated oxides powders. Acknowledgments The authors are thankful to the German Research Foundation (DFG) for the financial support provided through the SPP 1299 – “Das Haut-Konzept”.
[8] D. Koziej, M. Huebner, N. Barsan, U. Weimar, M. Sikora, J.D. Grunwaldt, Physical Chemistry Chemical Physics 11 (2009) 8620–8625. [9] A. Cabot, J. Arbiol, J.R. Morante, U. Weimar, N. Bârsan, W. Göpel, Sensors and Actuators B: Chemical 70 (2000) 87–100. [10] A. Oprea, N. Bârsan, U. Weimar, Sensors and Actuators B: Chemical 142 (2009) 470–493. [11] M. Hübner, R. Pavelko, N. Barsan, U. Weimar, Sensors and Actuators B: Chemical 154 (2011) 264–269. [12] H. Lueth, Solid Surfaces, Interfaces and Thin Films, fourth ed., Springer, Berlin Heidelberg, 2001. [13] N. Barsan, U. Weimar, Journal of Electroceramics 7 (2001) 143–167. [14] S. Samson, C.G. Fonstad, Journal of Applied Physics 44 (1973) 4618– 4621. [15] A. Many, Y. Goldstein, N.B. Grover, Semiconductor Surfaces, North-Holland Publishing Company, Amsterdam, 1971. [16] S.M. Sze, Physics of Semiconductor Devices, John Wiley & Sons, 1981. [17] A. Oprea, Personal communication.
Biographies
References [1] K. Ihokura, J. Watson, Stannic Oxide Gas Sensors; Principles and Applications, CRC Press. [2] N. Barsan, M. Schweizer-Berberich, W. Göpel, Fresenius’ Journal of Analytical Chemistry 365 (1999) 287–304. [3] N. Bârsan, M. Hübner, U. Weimar, Sensors and Actuators B: Chemical 157 (2011) 510–517. [4] L. Mädler, T. Sahm, A. Gurlo, J.D. Grunwaldt, N. Barsan, U. Weimar, S. Pratsinis, Journal of Nanoparticle Research 8 (2006) 783–796. [5] A. Tadeev, G. Delabouglise, M. Labeau, Materials Science and Engineering B 57 (1998) 76–83. [6] N. Yamazoe, Sensors and Actuators B: Chemical 5 (1991) 7–19. [7] M. Huebner, D. Koziej, M. Bauer, N. Barsan, K. Kvashnina, M.D. Rossell, U. Weimar, J.D. Grunwaldt, Angewandte Chemie – International Edition 50 (2011) 2841–2844.
Michael Hübner received his diploma in Chemistry in 2008 and his Ph.D. in the field of metal oxide based gas sensors in 2012 from the University of Tübingen. Nicolae Bârsan received his diploma in Physics in 1982 from the Faculty of Physics of the Bucharest University and in 1993 his Ph.D. in Solid State Physics from the Institute of Atomic Physics, Bucharest, Romania. Since 1995 he is a senior researcher at the Institute of Physical Chemistry of the University of Tuebingen, where he is leading, together with Udo Weimar, the Gas Sensors Group. UdoWeimar received his diploma in Physics in 1989, his Ph.D. in Chemistry in 1993 and his Habilitation in 2002 from the University of Tübingen. Since 2010 he is a full professor at the Department of Chemistry of the University of Tuebingen. His research interest focuses on chemical sensors as well as on multicomponent analysis and pattern recognition.
Contents lists available at SciVerse ScienceDirect
Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
Influences of Al, Pd and Pt additives on the conduction mechanism as well as the surface and bulk properties of SnO2 based polycrystalline thick film gas sensors M. Hübner ∗ , N. Bârsan, U. Weimar Tübingen University, Faculty of Science, Department of Chemistry, Institute of Physical and Theoretical Chemistry, Auf der Morgenstelle 15, 72076 Tübingen, Germany
a r t i c l e
i n f o
Article history: Received 25 November 2011 Received in revised form 23 February 2012 Accepted 26 February 2012 Available online 22 May 2012 Keywords: Doped SnO2 Conduction mechanism CO H2 Oxygen
a b s t r a c t The reasons of the effect of Pd, Pt and Al additives on the sensing and conduction mechanism of SnO2 based thick film porous gas sensing layers are studied by a combination of DC-resistance, work function changes and catalytic conversion measurements. This is done by analyzing the dependence of the DC resistance on the corresponding band bending changes over a large range and the use of previously reported conduction models. The gained information deals with the surface band bending in the absence of ambient atmosphere oxygen, the position of the Fermi level, the concentration of free charge carriers, the Debye length and the width of the surface charge layer in various ambient conditions. Very interestingly, we found that in all cases the “doping” had an impact on both surface and bulk properties even if the additives and the technology of “doping” were targeted towards surface activation, in the case of Pt and Pd, and bulk compensation of donors, in the case of Al. Besides that, the catalytic conversion experiments indicated that the presence of Pt is associated with the reduction of the material in the absence of ambient atmosphere oxygen. © 2012 Elsevier B.V. All rights reserved.
1. Introduction In the field of gas sensors based on semiconducting metal-oxides the ones based on thick, porous layers of SnO2 (in combination with noble metals additives) are still the most used, especially for the detection of reducing gases [1,2]. Surprisingly, even after more than 40 years of research, the base material is still the subject of many investigations and still offers surprises. We recently demonstrated that the conduction mechanism in SnO2 polycrystalline, porous layers changes from one controlled by surface depletion layers to one controlled by surface accumulation layers and the experimental basis was provided by simultaneous DC resistance and work function changes measurements, which were combined with semiconductor calculations [3]. Because we consider that the findings have a considerable impact over the understanding of the sensing with n-type semiconducting oxides we decided to extend our investigation from a model system, undoped SnO2 , towards more practical materials. To improve the lack of selectivity and to reduce the optimum operation temperature of SnO2 based gas sensors, they are generally “doped” with noble metals such as Pd and Pt [4–6]. These materials are normally synthesized in such a way to have the noble
∗ Corresponding author. Tel.: +49 7071 78766; fax: +49 7071 5960. E-mail address: [email protected] (M. Hübner). 0925-4005/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2012.02.080
metal additives on the surface; consequently their influence should be limited to a surface effect which determines the improved performance and this is also the way in which their role is modeled. The chemical state of these “dopants” could be recently clarified by the use of “operando” XAS spectroscopy; both Pd and Pt are present in an oxidized state and they are dispersed at an atomic level [7,8]. To change the electrical properties of SnO2 , the bulk doping method is generally applied. By the addition of donor impurities (Sb and F) into the material, one is able to increase the conductivity because an extra electron is available in the lattice. The opposite effect can be achieved by using an impurity element having one electron less than Sn in the outer shell. By replacing some of the Sn4+ sites by Al3+ , one creates therefore acceptor levels in the band gap. These levels determine an increase of the resistance because the effect of the oxygen vacancy donor levels is compensated by the acceptor levels. This implies that the position of the Fermi level is moving away from the conduction band edge towards the middle of the band gap and one is, consequently, changing the bulk properties of SnO2 . The effect of Al, Pd and Pt additives on the conduction mechanism of SnO2 is in our focus in this contribution. By combining the basic knowledge from the undoped SnO2 with simultaneous DC-resistance and work function changes measurements on the doped materials, we are able to figure out the influences on the conduction mechanism and the properties of SnO2 . Especially, the intended effect of the additives, namely the surface sensitization
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of Pd and Pt and the change of the electric bulk properties by Al is discussed. 2. Experimental details and results 2.1. Sample preparation and experimental conditions The co-precipitation method was used to obtain Al doped SnO2 . Therefore AlCl3 was mixed in the intended weight percentage (0.3 and 3 wt.%) with an aqueous solution of SnCl4 . The drop wise addition of ammonia led to the precipitate which was consequently washed several times with bi-distilled water. Finally the Al doped Sn(OH)4 was calcined at 1000 ◦ C for 8 h to get to the oxide. XRD measurements revealed that no second phase related to Al2 O3 or metallic Al could be found in the diffraction patterns of the doped samples. The crystallite sizes of both Al doped samples were in the range of 82 nm (estimated from XRD). On the contrary, gel-impregnation was used for the addition of Pd and Pt. The gel was obtained by the drop wise addition of ammonia to an aqueous solution of SnCl4 . Afterwards, the Pd and Pt additives were introduced in the nominal weight concentration of 0.2 wt.% in forms of the chlorides (PdCl2 and PtCl4 ). The resulting solution was washed again with bi-distilled water and finally calcined at 1000 ◦ C for 8 h. The average crystallite size – estimated from XRD – is around 60 nm. More details and the full characterization can be found in [9]. Screen printing technique was used to obtain porous thick film layers (50 m thick); the as-synthesized powders were mixed in an appropriate amount with an organic binder (propane diole) until a homogenous, printable paste was obtained. The latter was printed on alumina substrates (25.4 mm × 4.2 mm) provided with interdigitated Pt electrodes – for the electrical readout – and a Pt heater on the backside allowing the operation at well controlled temperatures (300 ◦ C). The as-obtained sensors were dried at room temperature for 12 h before they underwent a final thermal treatment in a moving belt oven (400–600 ◦ C). The following measurements were performed for the Pd and the Pt doped SnO2 sensors, both operated at 300 ◦ C. For the two differently Al doped samples (0.3 and 3 wt.%), only the simultaneous DC-resistance and work function measurements were applied, again at an operation temperature of 300 ◦ C: • Simultaneous DC-resistance (by using a multimeter Keithley DMM 199) and catalytic conversion measurements (by connecting the sensor chamber to a photo acoustic gas analyzer Innova 1312) of two Pd doped SnO2 sensors under exposure to 4 CO pulses (50, 100, 200, 300 ppm) and of two Pt doped SnO2 sensors, exposed to 4 CO pulses (10, 30, 70, 100 ppm), in the absence of oxygen (<3 ppm; below the detection limit of the oxygen analyzer Zirox SGM400). Similar experiments were also performed in different oxygen containing backgrounds like in the case of undoped SnO2 [3]. The sensors were stabilized at 300 ◦ C in dry N2 for 24 h before starting the experiment. Exposure time of CO with a flow of 500 ml/min was set to 3 h (2 h for the Pt doped samples); for recovery 3 h in the corresponding atmosphere were allowed. • Simultaneous DC-resistance (constant voltage mode using an electrometer Keithley EMM 617) and work function changes (˚) measurements (using a Mc Allister KP 6500 Kelvin Probe). Also here all the samples (Pd, Pt and Al doped SnO2 ) were stabilized in the desired background condition for 24 h before starting the measurements. A flow of 470 ml/min was adjusted by a computer controlled gas mixing system. All sensors were exposed to several concentrations of CO (10, 30, 70, 100 ppm and additional 2 and 5 ppm for the Pt doped sample) and H2 (2, 5, 10, 30, 70, 100 for the Pd and the 0.3 wt.% Al doped sample; 1, 3, 7, 10, 30, 70,
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100 ppm for the Pt doped one and 10, 30, 70, 100, 200 ppm for the 3 wt.% Al doped one) for 2 h in different oxygen backgrounds (no oxygen – which was in fact around 12 ppm – 200, 2000 and 22,000 ppm). Further, the influence of oxygen it self was investigated by exposing the samples to stepwise increasing oxygen concentrations (200, 2000 and 22,000 ppm) in a background of N2 . A detailed description about the functioning of the Kelvin probe set-up can be found in [10]. 2.2. Experimental results 2.2.1. Simultaneous DC-resistance and catalytic conversion measurement The sensing mechanism of Pd and Pt doped SnO2 thick film layers was investigated by simultaneous DC-resistance and catalytic conversion measurements upon exposure to CO in the, almost, absence of oxygen (oxygen contamination <3 ppm). The time dependencies of the resistance of two Pd doped samples, both prepared in the same way, upon exposure to four CO pulses (50, 100, 200 and 300 ppm) in a “non” oxygen containing background at an operation temperature of 300 ◦ C are presented in Fig. 1 (left side, lower part). A very large drop in the resistances of the Pd doped SnO2 sensors is observed when exposed to the target gas (more than four orders of magnitude) with the steady sate (equilibrium) being reached quite fast. The allotted 3 h of recovery are enough to get back to the initial baseline. Checking the gas composition after the sensor chamber, no CO2 as a reaction product of CO sensing can be monitored; only CO can be detected by the gas analyzer (see Fig. 1, left side, upper part). This finding indicates, like it was also found for undoped SnO2 [3,11], that the reduction of the material (consumption of lattice oxygen) as a reason for the resistance changes during CO sensing can be ruled out. By increasing the oxygen amount in the background (results not shown here), the same trend, previously reported for the undoped samples [3,11], was also observed for the Pd doped sample, namely a strong decrease of the signal when compared to the situation encountered in (almost) no O2 . It seems that the sensing mechanism of Pd doped SnO2 sensors, exposed to CO in different oxygen backgrounds, is very similar to the one of the corresponding undoped material [3]. On the contrary, similar measurements performed on Pt doped SnO2 , prepared in the same manner, demonstrated a complete different behavior and can be also seen in Fig. 1 (right side). Also there a huge drop in the resistance can be observed upon CO exposure in the absence of oxygen, but no recovery back to the baseline takes place as long as no oxygen was present. Furthermore, the production of CO2 in the exhaust can be identified (see Fig. 1, right side, upper part). This indicates that, probably due to the addition of Pt, also the reduction of SnO2 is playing a role in sensing in the absence of oxygen. 2.2.2. Simultaneous DC resistance and work function measurements of Pd and Pt doped SnO2 Simultaneous DC-resistance and work function changes measurements are used to determine the relationship between the surface band bending and the resistance depending on the surrounding conditions and, therefore, some information about the conduction models [3]. An example for the time dependence of both, the resistance and the CPD (˚ = −CPD), upon exposure to several CO pulses (10, 30, 70 and 100 ppm) and stepwise increasing H2 concentrations (2, 5, 10, 30, 70, and 100 ppm) in the absence of oxygen of a Pd doped SnO2 sensor is depicted in Fig. 2. The decrease in the resistance as well as in the work function for both target gases is pretty small as long as their concentrations are smaller than the one of the residual oxygen in the background (∼12 ppm). In the moment when the reaction takes place at target gas concentrations larger than the residual oxygen amount, one records
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Fig. 1. Time dependencies of the resistance during CO exposure (50, 100, 200 and 300 ppm) in the absence of oxygen at 300 ◦ C for two 0.2 wt.% Pd doped SnO2 sensors (left side) and the time dependencies of the resistance during CO exposure (10, 30, 70 and 100 ppm) in the absence of oxygen at 300 ◦ C for two 0.2 wt.% Pt doped SnO2 sensors (right side). The amount of CO and CO2 is presented in the upper part.
huge changes for both parameters (changes in the resistance of several orders of magnitude). In order to cover a huge range for the band bending changes, similar experiments were also performed in different oxygen backgrounds (200 and 2000 ppm) and also the influence of oxygen alone was investigated. An overview of all the measurements is given in Fig. 4 (left side), by plotting the dependence of the resistance on the changes in the band bending – assuming that in the experimental conditions the only reasons for changes in the work function are the one of the band bending – over the whole experimental range. This assumption is made credible by the extremely low concentration of water vapor in the test atmospheres, below 3 ppm (due to our measurements), because the main reason of the changing of the electronic affinity are the surface dipoles associated to the water vapor originating surface hydroxyl groups. The changes in the band bending where directly extracted from the measured changes in the work function like it was recently done for the undoped SnO2 [3]. As a reference point (qV = 0), the situation in dry N2 was chosen. The existence of two regions where a linear fit gives very good results (very small errors), is obvious. This fit indicates an exponential dependency between the resistance and the band bending changes with the slopes 1/[(1 ± 0.05)kT] and 1/[(2.33 ± 0.01)kT] respectively. Comparing the values to the ones obtained from the theoretical modeling given in [3], one region corresponds to the depletion layer model – theoretical value of 1, experimental value 1 ± 0.05 – and the other to the accumulation layer model – theoretical value of 2, experimental value 2.33 ± 0.01 – where the Boltzmann statistics is valid. In the used concentration range of the target gases, one is therefore all the time moving between these two models depending on the surrounding condition. That means that there is a seamless transfer between the depletion layer controlled
model and the accumulation layer controlled one, but the degeneration of the electrons at surface, like in the case of the undoped material [3], could not be observed for the Pd doped samples. Unfortunately, the used conditions did not provide data points located exactly at the switching point between these two regions. Nevertheless the point can be estimated by the crossing point of the two fitting lines (∼−0.43 eV). The raw data from the simultaneous DC-resistance and work function changes measurements of a Pt doped SnO2 sample upon exposure to CO and H2 in the almost absence of oxygen can be found in Fig. 3. The changes of both parameters (CPD and Resistance) are again small at low target gas concentrations (smaller than the residual oxygen amount). At higher ones, enormous changes can be measures. Also for this sample, similar measurements in oxygen containing backgrounds were performed. The overview (dependence of the resistance on the band bending changes) from the measurements of the Pt doped sample is shown in Fig. 4 (right side); the overall picture is completely different when compared to the Pd doped sample one: • at higher resistances, one can identify two regions with a seamless transfer. One region could be fitted by a dependence corresponding to the one of the depletion layer model (theoretical value of 1, experimental value of 1.12 ± 0.01) and the second dependence fitted quite well with the value obtained for the accumulation layer model (theoretical value of 2, experimental value of 2.26 ± 0.02); • in the case when the reaction is dominated by the reducing gas concentration (higher than the one of the residual oxygen concentration), a break in the relationship between resistance and band bending is observed followed by a region in which the
Fig. 2. Simultaneous contact potential differences (CPD) and electrical resistance changes of a Pd doped SnO2 sensor during exposure to CO pulses (10, 30, 70 and 100 ppm) (left) and during exposure to stepwise increasing H2 concentrations (2, 5, 10, 30, 70 and 100 ppm) (right) in the absence of oxygen at 300 ◦ C.
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Fig. 3. Simultaneous contact potential differences (CPD) and electrical resistance changes of a Pt doped SnO2 sensor during exposure to CO pulses (2, 5, 10, 30, 70 and 100 ppm) (left) and during exposure to H2 pulses (1, 3, 7, 10, 30, 70 and 100 ppm) (right) in the absence of oxygen at 300 ◦ C.
relationship is described by an exponential function with a rather low exponent value (1/[(6.41 ± 0.24)kT]). In the catalytic conversion measurements in the concentration range corresponding to the break we recorded the generation of CO2 as an indicator of the reduction of the material. 2.2.3. Simultaneous DC resistance and work function measurements of Al doped SnO2 The influence of different Al doping levels (0.3 and 3 wt.%) on the conduction mechanism of undoped SnO2 was investigated by the use of simultaneous DC-resistance and work function changes measurements upon exposure to CO and H2 in different oxygen backgrounds similar to the ones shown in the latter chapter for the Pd and Pt doped samples. The time dependencies of the resistance as well as the CPD of a 0.3 wt.% Al doped SnO2 sensor during exposure to 4 CO (10, 30, 70 and 100 ppm) and 6 H2 (2, 5, 10, 30, 70 and 100) pulses in the almost absence of oxygen are presented in Fig. 5. One observes the expected decrease of both, the resistance as well as the work function, when exposed to CO and H2 . By comparing the baseline resistance of the Al doped sample (∼1 M) with the one of the undoped SnO2 (∼60 k) [3], both operated in the same conditions, the increase in the baseline resistance due to the presence of Al is obvious. Similar experiments, exposure to CO and H2 in the absence of oxygen, performed for the 3 wt.% Al doped sample are given in Fig. 6. The overall trend is in line with the results recorded for the 0.3 wt.% Al doped sample. The addition of more Al further increases the baseline resistance (∼3 M). To cover a large range of the band bending changes, similar measurements were again done in the presence of different oxygen backgrounds (not shown here). The observations in the presence of oxygen are the expected ones: the baseline resistance increases with increasing oxygen level whereas the signal strength decreases.
An overview of all measurements, presented by the dependence of the resistance and the corresponding band bending in the different conditions, is given for the 0.3 wt.% sample in Fig. 7 on the left side and for the 3 wt.% sample on the right side. Again the situation in dry N2 was taken as a reference point (qV = 0). Also here the changes of the band bending were directly extracted from the measured changes in the work function by assuming that in these conditions one is not expecting any changes of the electron affinity. In the case of 0.3 wt.% Al doped SnO2 a seamless transfer between three regions can be observed. The region where a linear fit is giving the highest slope matches quite well with the one described by a depletion layer model (theoretical value of 1, experimental value of 1.1 ± 0.06). Reaching a band bending change of around −0.1 eV, the slope is decreasing to an experimental value of 1.58 ± 0.35. This region may be described by an accumulation layer model where the Boltzmann statistics are still valid (theoretical value of 2). At a band bending change of around −0.4 eV the slope decreases further (experimental value 3.57 ± 0.51) representing the region with the degeneration of the concentration of the charge carriers for the accumulation layer [3]. The overall picture of the 3 wt.% Al doped SnO2 sensor (Fig. 7, right side) is a bit different. Only two regions where a linear fit gives good results, with a seamless transfer between each other, can be obtained. The tendency of the experimental values in an oxygen containing background (besides the two highest H2 concentrations in 200 ppm of oxygen) fits well with the depletion layer model from the theoretical modeling (theoretical value 1, experimental value 1.19 ± 0.04). At a band bending change of −0.3 eV one moves into a conduction mechanism controlled by the accumulation layer model where the Boltzmann statistics still apply (theoretical value 2, experimental value 2.17 ± 0.39). The degeneration at the surface in the accumulation layer (further decrease of the slope) cannot be observed in the applied gas concentration range.
Fig. 4. Dependence of the resistance and the corresponding band bending for the Pd doped SnO2 sensor (left side) and for the Pt doped SnO2 sensor (right side). The reference (qV = 0) is denoted as the situation in N2 .
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Fig. 5. Simultaneous contact potential differences (CPD) and electrical resistance changes of a 0.3 wt.% Al doped SnO2 sensor during exposure to CO pulses (10, 30, 70 and 100 ppm) (left) and during exposure to H2 pulses (2, 5, 10, 30, 70 and 100 ppm) (right) in the absence of oxygen at 300 ◦ C.
Fig. 6. Simultaneous contact potential differences (CPD) and electrical resistance changes of a 3 wt.% Al doped SnO2 sensor during exposure to CO pulses (10, 30, 70 and 100 ppm) (left) and during stepwise increasing H2 concentrations (10, 30, 70, 100 and 200 ppm) (right) in the absence of oxygen at 300 ◦ C.
3. Discussion about the influence of the different dopants The results from the DC-resistance and catalytic conversion measurements of the Pd doped SnO2 sample in the almost absence of oxygen are ruling out the reduction of the material as a reason for the huge resistance changes. In contrast, for a similar sample doped with Pt instead of Pd, we could demonstrate that the consumption of lattice oxygen by CO (CO2 production) in this condition has to be taken into consideration. This fact and the tendency of decreased signals in oxygen containing backgrounds indicates, for the case of Pd, that the basic sensing mechanism – depending on the oxygen amount – seems to be similar to the one proposed for the undoped material [3,11]. The presence of the Pd determines an increase in the baseline resistance and a general increase of the sensor response. By performing simultaneous DC-resistance and work function changes measurements in conditions in which there are no reasons for the change of the surface dipole concentration but which are allowing for scanning a large range of the resistance, one gets a direct access to the relation between the resistance and the
corresponding band bending. These physical parameters determine the conduction processes in the sensing layer and their experimental relationship can be confronted with various theoretically modeled conduction mechanisms. It could be demonstrated by plotting the dependence between these two parameters over a large range that for undoped SnO2 there is a seamless transfer of the conduction mechanism from a depletion layer controlled model to an accumulation layer controlled one [3]. The switch between these two models directly appeared in dry nitrogen, indicating that in this condition the energy band situation is described by a flat band case. To identify the influences of the different additives, this condition was consequently chosen as the reference point for all the other samples. With further increasing band bending changes, its effect on the resistance is getting weaker indicating a degeneration of the electrons at the surface. The behavior in the latter case could be numerically modeled [3]. Quantization effects are expected in the case of narrow accumulation layers and degenerate electron gas conditions at the surface of semiconducting materials [12]. In our experiments, at rather
Fig. 7. Dependence of the resistance and the corresponding band bending for the 0.3 wt.% Al doped SnO2 sensor (left side) and for the 3 wt.% Al doped SnO2 sensor (right side). The reference (qV = 0) is denoted as the situation in N2 .
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high temperatures, it is very unlikely that they would be observed because the thermal energy noise (of the order of magnitude of kT), will make the energy differences between these quantization levels not relevant. Because the effects are to be expected only in the case of degeneration, they need to be taken into account only after the crossing of the Fermi level by the conduction band minimum at the surface. Hence, in conditions where the accumulation layer is described by the analytical solution (Boltzmann statistics valid), the quantization effects cannot be relevant for the relationship between the resistance and the corresponding surface band bending. Coming back to the comparison of the analytical and the numerical solutions that describe how the average electron concentration in the accumulation layer depends on the surface band bending, it was found that both trends are similar (same slope) up to around 0.3 eV after the crossing of the surface conduction band edge with the Fermi level position; with further increasing of the surface band bending, the trends are strongly diverging, which reflects the lack of appropriateness of the Boltzmann approximation. Hence, by knowing the flat band situation (switch from the depletion layer model to the accumulation layer one) and the point at which the Boltzmann statistics start to get invalid, one can estimate at which value the conduction band at the surface crosses the Fermi level and by that the position of the Fermi level in the bulk (EC,b − EF ). Also, a possible initial band bending in N2 can be estimated by this approach. By using this reasoning, the influences of the different additives on the surface (initial band bending) and bulk (position of the Fermi level) properties of SnO2 can be determined. An overview of the surface and bulk influences of the different additives compared to the undoped material in dry nitrogen atmosphere is presented in Fig. 8. The situation for the reference SnO2 material (black lines) in this condition is described by a flat band case. The position of the Fermi level was estimated to be located at around 80 meV below the conduction band. In case of Pd addition (red), a huge initial surface band bending in N2 of around 430 meV can be found from the resistance versus band bending plot, which is not related to ionosorbed oxygen. This fact indicates a considerable surface effect of Pd, probably caused by the existence of surface defects acting as electron traps. This demonstrates the impact on the surface properties of Pd “doping”. The position of the Fermi level lies at a distance of at least 290 meV below the conduction band because one is not observing the change of the slope for the accumulation layer in the applied concentration range. This moving of the Fermi level position shows that the addition of Pd also has a tremendous bulk effect, which is not the intention of the “doping” and it is not taken, generally, into consideration. The surface influence of Pt (green) as a dopant is not that effective. From Fig. 4 one can estimate an initial band bending of approximately 100 meV. Due to the experimentally proven material reduction and, therefore, the change of the material, one cannot estimate the position of the Fermi level in the Pt doped SnO2 sensor. In case of 0.3 wt.% Al (dark blue) a small initial band bending of around 50 meV can be observed. The degeneration of the electrons at the surface starts at a band bending value of approximately −430 meV (see Fig. 7). Consequently, the Fermi level position for this doping level should be located at around 130 meV below the conduction band edge. For the higher value of Al doping, the switch from the depletion layer into the accumulation layer starts at a band bending of around −300 meV which consequently implies a huge initial band bending in N2 of around 300 meV related to surface defects acting as electron traps. Under exposure to 100 ppm of H2 in N2 a band bending change of −870 meV was measured. In this condition the degeneration at the surface did not yet start which implies that the
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Fig. 8. Overview of the influence of the different additives (Pd – red; Pt – green; 0.3 wt.% Al – dark blue; 3 wt.% Al – light blue) on the surface band bending and the position of the Fermi level of the host SnO2 material (black) in nitrogen. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fermi level position should be located at least 270 meV below the conduction band edge. The findings presented above indicate the potential of the investigation method; one does not only get information about the different conduction models, but it is also possible to identify and estimate surface effects in terms of initial band bending in N2 as well as bulk effects (position of the Fermi level) determined by the addition of different additives. In the cases presented here, it could be demonstrated that both “doping” techniques – gel impregnation for Pd and co-precipitation for Al – are influencing the surface as well as the bulk properties of the host material (SnO2 ). After the estimation of the initial band bending in N2 and the position of the Fermi level of the different materials, the next interesting step is to calculate the different Debye lengths to check whether the assumptions made in the modeling about porous thick films with large grains – large enough to have a bulk region unaffected by surface phenomena (d LD ) – are correct or not. The Debye length LD is defined, in the Schottky approximation, as [13]:
LD =
kTεε0 q2 nb
(1)
In order to calculate the different values of LD we need the values for nb representing the electron concentration in the conduction band at a certain temperature (300 ◦ C in our case). The Boltzmann distribution for the calculation of nb can be used as long as the donor concentration ND of the material is much smaller than the
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Table 1 Position of the Fermi level (EC − EF ), the corresponding electron concentration in the conduction band (nb ), the calculated values of the Debye length (LD ) and the calculated values for x0 in N2 (A), upon exposure to 2000 ppm of O2 (B) and upon exposure to 100 ppm of CO in 200 ppm of O2 for undoped and 0.3 wt.% Al doped SnO2 and 100 ppm CO in N2 for Pd and 3 wt.% Al doped SnO2 (C). Material
EC − EF (meV)
nb (cm−3 )
LD (nm)
x0 in A (nm)
x0 in B (nm)
x0 in C (nm)
SnO2 0.3 wt.% Al 3 wt.% Al 0.2 wt.% Pd
80 130 270 290
2.14 × 1018 7.77 × 1017 4.57 × 1016 3.05 × 1016
3.6 6.0 24.4 30.0
0 8.4 85.2 124.9
7.2 12.5 97.1 130.6
4.9 7.6 30.0 39.4
total number of states in the conduction band, NC . This can be easily shown by taking as the donor concentration the measured value from Samson et al. [14] (ND = 9 × 1015 cm−3 ) and the definition of NC [15]:
NC ≡ 2 2mn
3/2 kT h2
(2)
At the operation temperature of 300 ◦ C one obtains for NC a value of 1.08 × 1019 cm−3 which definitely meets the requirements to use the Boltzmann distribution function. Hence we can write: nb = NC exp
E − E F C kT
(3)
With the estimated values for the Position of the Fermi level we get the values for nb and therefore the different Debye lengths. All the values are presented in Table 1. Comparing the calculated values for LD with the measured dimensions of the grains (particle size ∼200 nm and crystallite size ∼70 nm for undoped SnO2 [11], 65 nm for Pd doped [9] and 82 nm for the Al doped samples) indicates that the assumptions made in the modeling are correct. There are two regions for all grains, namely a surface space charge layer and an unaffected bulk region. Another way to calculate the value of nb for undoped SnO2 and, therefore, to prove the validity of the latter calculations is to extract it from the measured DC resistance values. The values obtained in N2 are the most appropriate ones because of the flat band situation, which guarantees an uniform value of the free charge carrier concentration throughout the whole grain. The resistance of a layer with length l and cross section A is defined as: R=
1 1 = A ·A
(4)
where is the resistivity and is the conductivity of the layer. The conductivity can be expressed by the product of the charge q, the effective mobility of the layer and the electron concentration nb [16]: = q · · nb
aspect ratio AR and can be estimated. Consequently, we obtain instead of Eq. (6): R=
1 l = q · · nb · t · AR q · · nb · l · t · AR
(7)
The question is, now, how thick is the contact area t of two square elements which the current has to pass. The maximal value for the overlap between two single grains can be approximated with the Debye length because of the exponential dependency of the DC resistance on the band bending; otherwise, the influence of the surface will not be the decisive factor in the modulation of the sensing layer resistance: That means approximately 3.6 nm. For an average grain size of 200 nm one can place 250 grains in one row in a 50 m thick layer. Consequently, one obtains for the effective layer thickness t 900 nm. The schematic in Fig. 9 helps for the better understanding of the different geometrical parameters. The aspect ratio, representing the total number of squares in between the electrodes was calculated to be around 70. The electron concentration in the conduction band nb can now be easily calculated by using Eq. (8) and the measured value of the DC resistance in N2 : nb =
1 q · · R · t · AR
(8)
For the effective mobility in such thick film porous layers, a quite low value of approximately 0.1 cm2 V−1 s−1 was measured [17]. By using the latter value for the mobility, the elementary charge q = 1.602 × 10(−19) C, the resistance of the sensor in dry N2 – 50 k˝ – and the calculated values for t and AR one obtains: nb = 1.98 × 1017 cm−3 . The calculated value of nb is similar to the one in Table 1 for SnO2 which gives some confirmation of the validity of the calculations.
(5)
Combining the latter two expressions one obtains: R=
1 q · · nb · A
(6)
To get rid of the unknown geometric parameters l and A in Eq. (6) one can apply the following reasoning: the layer that gives the resistance can be divided in a number of square shaped layers with the length and width l and the thickness t placed between the electrodes; the thickness t is not the thickness of the layer because the grains are only loosely agglomerated; it is an effective layer thickness that depends on the average overlap between neighboring grains. The cross section for each of the elemental squares can be expressed by the product l multiplied with t. The number of these squares in between the whole electrode structure is called the
Fig. 9. Schematic drawing explaining how one can estimate the contact areas t of two square elements.
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Fig. 10. The energy band situation in N2 (black curve), upon exposure to 2000 ppm of O2 (red curve) and 100 ppm CO in (i) 200 ppm O2 (for the undoped and the 0.3 wt.% Al doped sample) and in (ii) N2 (for the Pd and the 3 wt.% Al doped sample) (green curve) of SnO2 , 0.3 wt.% Al, 3 wt.% Al and 0.2 wt.% Pd doped SnO2 . The conditions in which the flat band situation is reached are shown in blue. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
A further parameter which represents the influence of the different additives and the different models is the thickness of the space charge layer x0 . For its calculation one either has to use Eq. (9) in case of the depletion layer model [13]
x0 =
2VS εr ε0 qnb
(9)
or relation 10 for the accumulation layer controlled one [3]: x0 =
√
2 · LD · 1 − exp
qV S
2kT
(10)
In Table 1 an overview of the calculated x0 values for all materials (besides the Pt doped because in this case one cannot estimate the position of the Fermi level) in nitrogen, upon exposure to 2000 ppm of O2 (depletion layer model) and 100 ppm of CO in 200 ppm O2 (for undoped and 0.3 wt.% Al doped SnO2 ) and in N2 (Pd and 3 wt.% Al doped SnO2 ) (accumulation layer model) is given. To get a better idea about the different values, an schematic overview about how the thickness of the space charge layer x0 and the corresponding band bending changes are related and influencing the energy band situation in N2 (black), upon exposure to 2000 ppm of O2 (red) and 100 ppm CO in 200 ppm O2 and in N2 (green) for undoped, the two Al and the Pd doped SnO2 is presented in Fig. 10. As already mentioned, the situation in N2 for undoped SnO2 is described by a flat band situation (x0 = 0). Upon exposure to 2000 ppm of O2 an upward band bending of 100 meV is measured whereas CO exposure in 200 ppm O2 determines a downward band bending of 340 meV. Although the latter relative change in the band bending is 3 times higher compared to the upward band bending caused by oxygen exposure, the calculated value for x0 in case of the depletion layer is larger (7.2 nm compared to 4.9 nm). This indicates that the space charge layer in case of the accumulation is very thin although the potential difference between the surface (positively charged) and the bulk is quite high [11]. The doping with 0.3 wt.% Al implies an initial band bending of around 50 meV; the effects of 2000 ppm of oxygen and 100 ppm of CO in 200 ppm O2 are reduced and the values for x0 are increased due to the moving of the Fermi level. Upon exposure to 30 ppm of CO in a background of 200 ppm of oxygen one reaches the flat band
situation. The increase of the Al concentration (3 wt.%) dramatically increases the initial band bending and the Fermi level is shifted further towards the middle of the band gap. This leads to much larger values for the thickness of the surface space charge layers. The oxygen influence (2000 ppm) is comparable to the undoped SnO2 ; the effect of 100 ppm CO in N2 is rather big but one is not able to get to the degeneration in this conditions. The flat band situation is reach upon exposure to 30 ppm of CO in N2 . For the Pd doped sample one obtains the highest initial band bending in N2 and the largest value for the Debye length. Hence, also the values for x0 are much higher compared to the undoped SnO2 material. The effect of oxygen exposure is very weak most probably due to the high initial band bending related to the Pd associated surface traps. During exposure to 100 ppm of CO in N2 the largest change in the band bending is measured. For the flat band situation no data point was obtained in the applied combinations of H2 , CO and O2 . Fig. 10 gives a good overview about how the doping with Al and Pd influences the surface and bulk properties of SnO2 . By combining again the theory and the experiment, one is able to estimate different parameters of the materials, like the electron concentration in the conduction band nb , the Debye length and therefore also the thickness of the space charge layer at the surface. 4. Conclusion and outlook Based on recently published theoretical calculations and experimental results on SnO2 , we were able to demonstrate the different influences of Pd, Pt and Al dopants on the sensing and conduction mechanism of the host material. It was found that the sensing of CO in the absence of oxygen in case of the Pd doped sensor is very similar to undoped SnO2 whereas the reduction of the material has to be taken into consideration in case of Pt doping. We were also able, by examining the dependence of the resistance and the corresponding band bending changes and comparing it to the theoretical calculations, to present a new, elegant way not only to identify the conduction mechanism depending on the surrounding conditions, but also to explore the surface and bulk effects of the different additives; we think that the approach we used will prove itself to be a very effective tool for the investigation of both surface and bulk
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properties of semiconducting oxides used as gas sensing materials in operando conditions. In this way we could demonstrate that the co-precipitation as well as the gel-impregnation method used for the addition of the dopants both influences the surface and the bulk properties of SnO2 . In the near future we will focus on sample realized in a way in which the likelihood of surface only effect of additives is much higher, namely the impregnation of already thermally treated oxides powders. Acknowledgments The authors are thankful to the German Research Foundation (DFG) for the financial support provided through the SPP 1299 – “Das Haut-Konzept”.
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Biographies
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Michael Hübner received his diploma in Chemistry in 2008 and his Ph.D. in the field of metal oxide based gas sensors in 2012 from the University of Tübingen. Nicolae Bârsan received his diploma in Physics in 1982 from the Faculty of Physics of the Bucharest University and in 1993 his Ph.D. in Solid State Physics from the Institute of Atomic Physics, Bucharest, Romania. Since 1995 he is a senior researcher at the Institute of Physical Chemistry of the University of Tuebingen, where he is leading, together with Udo Weimar, the Gas Sensors Group. UdoWeimar received his diploma in Physics in 1989, his Ph.D. in Chemistry in 1993 and his Habilitation in 2002 from the University of Tübingen. Since 2010 he is a full professor at the Department of Chemistry of the University of Tuebingen. His research interest focuses on chemical sensors as well as on multicomponent analysis and pattern recognition.