- Email: [email protected]
Multivariable oxygen sensing based on photoluminescence and photoconductivity of TiO2 nanoparticles
Sensors & Actuators: B. Chemical 303 (2020) 127236
Contents lists available at ScienceDirect
Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
Multivariable oxygen sensing based on photoluminescence and photoconductivity of TiO2 nanoparticles
T
M. Eltermann, V. Kiisk, A. Kikas, S. Lange, R. Jaaniso⁎ Institute of Physics, University of Tartu, W. Ostwald St 1, Tartu 50411, Estonia
A R T I C LE I N FO
A B S T R A C T
Keywords: Oxygen sensor Multivariable gas sensor Drift correction TiO2 Photoluminescence Photoconductivity
We studied sol-gel-prepared crystalline (anatase) TiO2 nanopowder coatings, exhibiting simultaneously three different oxygen-sensitive signals under ultraviolet illumination: photoconductivity, photoluminescence of intrinsic defects, and photoluminescence of Sm3+ impurity ions. The material was subjected to a flow of O2/N2 gas mixture at normal pressure, where the volume fraction of oxygen was precisely controlled. All three signals responded to changes in oxygen content but also exhibited long-term drifts. The signals were fused by a multivariable model, which related the logarithm of oxygen concentration to a polynomial composed of signal logarithms. Extensive testing by the ordinary least squares optimization with randomly generated O2 concentrations was used for training the model. It was demonstrated that the intrinsic luminescence in combination with one of the remaining two signals suppressed the drifts and significantly improved the precision in predicting the actual oxygen levels. The rms errors were improved by five times in the experiments where the oxygen concentration was randomly varied between 0.21% and 21% during two days. The combination of photoconductivity and Sm3+ luminescence resulted in somewhat smaller improvement (2–3 times), because of the mutual dependence of these two signals caused by similarities in the underlying physical mechanisms.
1. Introduction A simple but very fruitful method in solid-state gas sensing is based on the effect of surface adsorption on the electrical conductivity of sensing material. Sensors based on metal oxide semiconductors are most widely studied and commercialized, whereas the nanoscale materials have generally an enhanced sensitivity [1,2]. At the same time, several materials also have other easily measurable properties such as intrinsic photoluminescence (PL), which can be altered by the gas environment [3–6]. Photoluminescence properties can be further modified by doping, e.g. by incorporation of rare earth ions, and such extrinsic PL can also be influenced by ambient gases [7–10]. While having a simple construction, low cost, and high sensitivity, the semiconductor gas sensors also have drawbacks such as relatively low selectivity and large signal drift. Several strategies have been proposed to overcome these deficits, such as using multi-sensor arrays [11] or a multivariable detection scheme with a single sensor [12]. Besides metal oxides [5,13–16], the latter approach has been realized in a variety of materials like porous silicon [3,17–20], Si nanoribbons [21], conductive polymers [12], coated layers of metal nanoparticles [22], carbon nanotubes [23], and graphene [24,25]. At least two physical quantities were recorded either electrically (conductance, ⁎
capacitance, work function), acoustically (shift of resonant frequency or phase), or optically (photoluminescence, reflectance, absorbance). In early works different signals were not recorded simultaneously but in different experiments, which nevertheless demonstrated the potential of multivariable sensing materials. In advanced studies the recording of different sensor signals was performed simultaneously and the possibility of selective gas detection, based on different trajectories in the multidimensional measurand space was demonstrated directly [12,13,19,24,25]. In addition to discrimination of different gases and volatile compounds, the multivariable sensing can also be used for reducing the drift of the sensors thus increasing their overall precision. This problem has been widely investigated in the case of single-output sensor arrays [26–28]. It has not been used, to the best of our knowledge, for improving the accuracy of a single sensor with multiple responses. To demonstrate that the gas sensing accuracy can be improved by fusing different signals from the same material, we have chosen Smdoped TiO2 as multifunctional test material and dioxygen as a test gas. TiO2 is a wide-bandgap semiconducting oxide [29,30], which has commonly n-type conductivity and a conductometric gas response to several gases [31–34]. It also has intrinsic photoluminescence (IPL), depending on the nature of defects and (nano)morphology of the
Corresponding author. E-mail address: [email protected] (R. Jaaniso).
https://doi.org/10.1016/j.snb.2019.127236 Received 11 April 2019; Received in revised form 28 August 2019; Accepted 7 October 2019 Available online 11 October 2019 0925-4005/ © 2019 Elsevier B.V. All rights reserved.
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
material [35–37]. This IPL has been shown to be sensitive to oxygen gas [6,38]. The PL of Sm dopant in TiO2 (anatase) was also shown to be an effective signal for oxygen sensing [8,39]. Consequently, this material has multiple electrical and optical responses to oxygen gas, which have not been studied simultaneously before but can be potentially useful for multivariable sensing. The other reason for using oxygen in our proofof-principle experiments is that O2 is an important gas always present in ambient air, and the knowledge about oxygen response is a precondition for understanding the response mechanisms in the case of reducing gases or volatile compounds. In the present work, the electrical conductivity and photoluminescence responses of Sm-doped, TiO2 nanostructured materials to oxygen gas were recorded simultaneously. Two different PL signals, one originated from the internal defects of anatase and the other from Sm3+ ions, were spectrally resolved and analyzed. The three signals were successfully combined to improve notably the accuracy of the actual O2 concentration measurement. Fig. 2. XPS spectra of the sol-gel prepared TiO2:Sm3+ nanopowder. Lower panel presents an overview spectrum. Upper panels show details in the Sm 3d, O 1s, and Ti 2p regions.
2. Experimental set-up and characterization The TiO2 nanopowder, activated with Sm3+ ions, was prepared by using a sol-gel route as described in Ref. [8]. According to Raman scattering, the annealed powder contained a well-developed anatase phase (Fig. 1a), with ≈40 nm sized crystallites agglomerated into a hierarchical porous structure (Fig. 1b). The powder was ultrasonically dispersed in distilled water, and the resulting opaque white suspension was drop coated onto a glass substrate carrying interdigitated gold electrodes (Fig. 1c). A solid layer of TiO2 was formed on the electrodes after the water evaporation (Fig. 1d). The content of Sm in the samples was determined by x-ray fluorescence (XRF) to be 1.8 at%. The doping by Sm was confirmed by photoelectron spectra, as presented in Fig. 2. The lower panel shows an overview spectrum,
where photoelectron lines belonging to samarium, oxygen, and titanium can be observed. Traces of indium are also seen because the nanopowder of TiO2:Sm3+ was pressed into In metal for XPS measurements. The upper panel shows detailed spectra of Sm 3d, O 1s, and Ti 2p photoelectron lines. The shape of Ti 2p spectra confirms that the titanium oxidation state is mainly Ti4+. The areas of peaks in these spectra were used to estimate relative contents of the elements in the sample by CasaXPS software, which gave the following results: Ti - 32.1 at%, O - 65.3 at%, and Sm - 1.4 at%. The content of samarium (1.4 %) differs from the value determined by XRF (1.8%), for the reason that the
Fig. 1. a) Raman spectrum of the sol-gel-prepared TiO2:Sm3+ nanopowder. Indicated peaks correspond to the anatase phase of TiO2. b) Scanning electron micrograph of the powder. c) A diagram of the placement of TiO2:Sm3+ powder patch (red circle) on top of interdigitated electrodes. d) An optical image of the powder layer on the interdigitated electrodes. e) Setup of the electrical/optical gas sensing experiment. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 2
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
attributed to recombination of free electrons with trapped holes localized at specific defects, most probably oxygen vacancies and/or Ti3+ ions on the surface of nanocrystals [41,42]. To characterize the temporal responses of the optical and electrical signals, the PL spectra and EC were registered at fixed intervals of 1 s. The Sm3+ PL signal was calculated as an area of the main emission band between 610 and 630 nm after background subtraction (Fig. 3). In Fig. 4a, both EC and Sm3+ PL intensity signals are shown under steady-state conditions and at switching the UV light incident on the sample on and off. In the dark, during the UV light off, the sample had a relatively small EC which was increased by several orders of magnitude under UV irradiation. A transient behavior was observed upon switching the irradiation on or off. When the transient curves were fitted with an exponential function, the obtained time constants were in the order of tens of seconds. Similar responses were observed for the Sm3+ PL intensity. The temporal changes of the two signals were very well correlated: an increase of EC was always associated with a decrease of Sm3+ PL, and vice versa. This correspondence persisted when the composition of the ambient gas was changed: both signals responded systematically, but in an opposite fashion to the changes in oxygen content in the gas flow (Fig. 4b). When considered separately, such responses of the two signals to ambient oxygen are well established. Since the photoconductivity of nanocrystalline anatase is n-type, it is indeed expected to decrease with increasing oxygen pressure because of electron scavenging by surface adsorbed oxygen [43]. At the same time, the increase of PL intensity with increasing oxygen concentration has been explained by a special mechanism where the adsorbed oxygen deactivates the PL-quenching defects through an electron transfer [39].
Sm content may be different on the surface and inside the nano-powder grains. However, this difference is close to the uncertainty of the determination of concentrations (0.2%). The Raman spectrum was recorded on a Renishaw inVia Raman Spectrometer with the excitation wavelength 514 nm. X-ray Photoelectron Spectroscopy (XPS) analysis was performed by using the electron energy analyzer Scienta SES 100. The Mg K-α radiation was used for the excitation. The incident angle of the photon beam was 45°, and the photoelectrons were detected along the surface normal. CasaXPS software was used to estimate the relative content of the sample. XRF analysis was made with spectrometer Rigaku ZSX-400. The gas sensitivity experiments (as illustrated in Fig. 1e) were carried out with the sample placed on a temperature-controlled stage inside an air-tight cell (Linkam THMS350 V). The nitrogen and oxygen gases were both 99.999% pure. A continuous flow of the O2/N2 mixture through the cell at atmospheric pressure was maintained by calibrated mass flow controllers (Brooks SLA5820). The composition of the test gas was fixed by setting the flow rates of source gases individually and keeping the total flow rate at 200 ml/min. Before the gas sensing measurements, the sample was always preheated at 150 °C for at least 10 min in a dry gas flow to ensure a well-defined initial state of the sample and to minimize possible side effects due to condensed water. During the measurements, the samples were held in the dry flowing gas at a stabilized temperature (25 or 50 °C), slightly above the room temperature (20–22 °C). The sample in the cell was excited through a quartz window by a light-emitting diode (Thorlabs M365LP1, dominant wavelength 365 nm). The induced photoluminescence was dispersed with a monochromator (LOMO MDR-23) and detected by a CCD camera (Andor DU240-BU). Simultaneously, the electrical conductance was recorded with a Source Measure Unit instrument (Keithley K2450), by applying a constant voltage 1 V between the interdigitated electrodes.
3.2. Long term multivariable measurements For long term measurements, a special test program was implemented, which generated randomly but continuously changing oxygen concentration between 21% and 0.21% throughout several days. The temporal profile of the concentration is given in Fig. 5. The TiO2:Sm3+ material was exposed to the random gas program, while simultaneously monitoring PL intensities and EC. In addition to the external PL (EPL) of Sm3+ ions, the defect-related broadband internal PL (IPL) was also recorded. The signal of IPL was evaluated as an area of the broad emission band between 530–545 nm, away from Sm3+ emission lines (Fig. 3). The oxygen response of IPL was relatively weak but, as will be shown later, plays a valuable role in the multivariable approach. Large data set (time series) was collected where each data point contained the actual oxygen concentration (determined by the gas program) along with the corresponding EPL and IPL intensities and EC values. The time series of logarithms of the three signals marked as SEPL, SIPL, and SEC, respectively, are depicted in Fig. 6. The logarithms were used because this allowed the linearization of EC and EPL signals, that both had approximately power-law dependencies on oxygen concentration. The dependence of EC on gas concentration obeys the power-law quite generally [44], and approximately powerlaw dependence is justified for the dependence of Sm3+ PL intensity on oxygen concentration [8,39]. It can be seen in Fig. 6 that the Sm3+ PL and EC are very closely correlated. As a result, if the logarithmic signals are plotted against each other, all data points will fall on an almost straight line (Fig. 7), which points to a common physical process responsible for the magnitudes of both the Sm3+ PL and EC. High levels of ambient O2 quench the intrinsic PL, in contrary to Sm3+ PL. It also appears that IPL responds more slowly to the changes in oxygen content. Over the 50 h period of time, all the signals have a drift that is most significantly expressed for IPL because of its weaker oxygen response. Fig. 7 depicts the pairwise correlations between the signals. As already noted, logarithmic EC and EPL signals are almost linearly related. Still, some shift of the straight line occurs in time, which is caused by
3. Results 3.1. Elemental gas responses The PL spectrum of TiO2:Sm3+ nanopowder obtained under UV excitation consisted of sharp spectral lines on top of a broad emission band (Fig. 3). The sharp lines are typical to Sm3+ ion in a regular crystalline surrounding of anatase phase of TiO2 and emerge as a result of crystal field splitting of various 4G5/2→6HJ transitions in the 4f shell [40]. The PL excitation spectra, studied by us before [8], clearly demonstrate the effective excitation of Sm3+ PL by means of electronic energy transfer from the electronic states of the host matrix. The broad intrinsic emission band centered around 500 nm (2.5 eV) is also frequently observed in TiO2 (anatase) nanomaterials. It is usually
Fig. 3. Typical PL emission spectrum of annealed TiO2:Sm3+ nanopowder under UV excitation. The shaded areas were used to evaluate the Sm3+ PL intensity and the defect-related PL intensity. 3
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
Fig. 4. a) The effect of switching on/off the UV excitation on the Sm3+ PL intensity and EC in N2 atmosphere. b) The response of PL intensity and EC to oxygen exposure at different concentrations. The signals were measured under constant UV excitation at T = 50 °C and are normalized relative to the initial signal.
one of the other two signals could, in principle, more reliably predict the ambient O2 concentration. 3.3. Multivariate data analysis We introduce a bivariate function f so that the logarithm of the predicted oxygen concentration is expressed as
ln(p [O2]) = f (S1, S2)
(1)
Where, S1 and S2 are two logarithmic signals from the studied dataset, e.g., S1 can be SIPL and S2 can be SEC or SEPL. Any pairwise combination of the recorded signals can be used. The function f must be sufficiently flexible so that it can be “trained” to fit the data. The bivariate dependences on Fig. 6 are quite close to a linear one; hence it is justified to expand the unknown function into a Taylor series:
Fig. 5. The random temporal profile of O2 concentration used in long-term measurements. The histogram on the right panel illustrates the overall distribution of the concentration values used.
ln(p [O2]) = a0 + a1 S1 + a2 S2 + a11 S12 + a12 S1 S2 + a22 S22 + …
(2)
(truncated after 2nd order terms for clarity). Arbitrarily high order terms could be introduced to fit complex bivariate data. However, a higher-order polynomial would become numerically unstable and unable to yield reasonable results beyond the training data set. The unknown coefficients a 0 , ai , ai, j , etc. can be determined (trained) by minimizing a cost function taken as the residual sum of squared errors (SSE) between the logarithms of the predicted p [O2] and the corresponding true concentrations t [O2], N
SSE =
N
p
2
∑ [ln(pi ) − ln(ti )]2 = ∑ ln ⎛ t i ⎞ ⎜
i=1
i=1
⎟
(3)
⎝ i⎠
where N is the number of data points. The advantage of the approach is that the optimization is an ordinary least squares (OLS) problem, due to the linear dependence of the fitting function (Eq. 2) on the unknown parameters, and the particular form of the cost function. Regardless of the number of variables, the OLS problem leads to a system of linear equations. Therefore, the computation is straightforward and yields a unique solution. To simulate a real-life scenario where the sensor is first calibrated and then put into use, and in order to avoid overfitting with arbitrarily high order polynomials, we split the data set into a training set (first 60% of the data points) and a test set (the last 40% of the data points). The training set was utilized for OLS optimization where the cost function defined by Eq. (3) was minimized. Eq. (3) involves the ratio p [O2]/ t [O2] and is presumably well correlated to the practical figure of merit (FOM), which we define as the root mean square of the relative error,
Fig. 6. The temporal behavior of the signals (logarithms of PL intensities and EC) under the random O2 gas concentration program shown in Fig. 5. The temperature of sample holder was stabilized at 25 °C. The color of data points encodes actual oxygen concentration varied between 0.21% and 21% in this experiment.
the drifts in both signals. The defect-related PL shows a different type of correlation being relatively independent on Sm3+ PL or EC. At any given O2 concentration there is a tendency that an increase of Sm3+ PL signal or decrease of EC would correspond to a decrease of IPL. More importantly, there are almost no occurrences where different O2 concentrations map to the same coordinates in these two-dimensional spaces. Therefore, the defect-related PL intensity in combination with
FOM =
N 2 1 ⎛ pi − ti ⎞ = ∑ N i = 1 ⎝ ti ⎠ ⎜
⎟
N 2 1 ⎛ pi − 1⎞ ∑ N i = 1 ⎝ ti ⎠ ⎜
⎟
(4)
Therefore, the test data set was used to calculate the figure of merit 4
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
Fig. 7. Mutual correlations of three signals - logarithms of intensities of Sm3+ and defect-related PL and EC - plotted pairwise against each other.
Table 1 The figure of merit (by Eq. 4) of the optimized models with different signal combinations and different polynomial orders. Order/Signals
EC
EPL
IPL
EC + EPL
EC + IPL
EPL + IPL
EC + EPL + IPL
1 2 3
0.893 0.908 0.920
0.694 0.710 0.721
2.162 3.511 10.121
0.356 0.292 0.413
0.175 0.213 0.280
0.207 0.223 0.319
0.187 0.223 0.395
fitted but predicted.
of the optimized model, according to Eq. (4). We started from polynomial order n = 1 and increased n until no further reduction of the error was observed. Different combinations of the three signals were tested. Table 1 lists the FOMs produced by various models. It has to be noted that two variables were used in the formulas (1) and (2) for clarity and it is straightforward to generalize the approach for an arbitrary number of signals. In the first three columns of the table, the results for the univariate models are shown and, in the last column of the table, the results are given for the case with all three signals involved. It follows from Table 1 that the multivariable approach resulted in significantly reduced errors. In most cases, except EC + EPL, using Eq. (2) with only first-order terms resulted in the smallest error. The signal combinations with the involvement of IPL gave the best results. Note that this is true in spite of the relatively large error of the IPL signal alone (4th column) due to its relatively small response on the background of large drift (see Fig. 5). Fig. 8 demonstrates the prediction of the model as a continuous colored background superimposed on the experimental data shown in Fig. 7. This presentation allows to visualize the errors in a more detailed way as for an ideal fitting one should not see the experimental trajectories at all in such a plot. One can see that there are fewer discrepancies in the SEC vs. SEPL plot, because of the good correlation of these signals. We will further demonstrate the reduction of measurement error, including the reduction of long-term drift, with the results of another experiment, where the oxygen concentration was varied within a smaller span, between 0.21% and 2.1%. In this example, the signal changes due to variations in oxygen concentrations are significantly smaller as compared to the drifts of the background (Fig. 9). Similar training procedure with OLS minimization was conducted on this dataset. Fig. 10 compares the relative errors of optimized models for a univariate case of electrical conductance, and a bivariate case with internal PL added to conductance signal. As one can see, a significant improvement of the precision was achieved not only in the training set (0–32 h) but also at the later stage when the oxygen concentration is not
4. Discussion The previous studies of oxygen responsive PL of a similar TiO2:Sm3+ powder [8,39] implied that adsorption of oxygen on the nanocrystals has an indirect influence on fluorescence quantum yield of embedded Sm3+ ions while their excitation pathway remains almost unaffected. It was proposed that some defects in TiO2 lattice (such as oxygen vacancies) act as fluorescence quenchers in a certain charge state. These defects can exchange electrons with the adsorbed oxygen species, probably over the conduction band of TiO2. Hence, for a given morphology of the material and at a fixed excitation rate, the fluorescence yield of the Sm3+ ions is determined by the amount of charge trapped on the crystallite surface. The temporal and ambient-sensitive behavior of photoconductivity (Fig. 4b) is in qualitative agreement with some previous studies establishing that photoconductivity of TiO2 (anatase) films relatively quickly stabilized in the air, whereas in vacuum the photoconductivity slowly increased by several orders of magnitude [34,42]. The latter was associated with suppression of electron scavenging, owing to the removal of adsorbed molecular oxygen. We observe highly correlated responses from Sm3+ emission and photoconductivity (Figs. 4, 6 and 7). There is a direct relationship between the two signals at every moment of time, which points to a single controlling parameter. Let’s suppose that this parameter is the density of conduction electrons. While this would naturally lead to increased photoconductivity, the excitation rate of Sm3+ ions via band-to-band excitations should also increase, leading to a stronger PL which is not observed. Also, the Sm3+ PL and photoconductivity cannot be considered to have somehow concurrent relaxation paths, as it would contradict the established gas sensing mechanism of Sm3+ PL, controlled by PL quenching, rather than by excitation rate. Another possibility is that the density of conduction electrons remains approximately constant (independently of gas adsorption), determined by the other, more intense steady-state processes, such as the 5
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
Fig. 8. Visualization of the bivariate approximation. The same signal trajectories as in Fig. 6 are shown in the background of the model functions.
photogeneration of charge carriers which is approximately balanced by recombination through various pathways (incl. excitation of PL centers). In that case, the EC of the powder layer is controlled by the intercrystallite conductivity, which is affected by the space-charge layers (double-Schottky barriers) – induced by gas adsorption on the crystallite surface - a common situation for metal oxide gas sensors [1,2]. Under these assumptions, EC and Sm3+ PL are both uniquely determined by the amount of adsorbed species (and, of course, the intensity of the optical excitation), so that a unique relationship between these two quantities should also exist. Consequently, the same surface oxygen species are active in both cases. It has been shown that at room temperature the negatively charged oxygen molecular ion is the dominant adsorbate on metal oxides, in particular, TiO2 [45,46]. The conductivity of such a semiconductor layer is known to follow a power-law dependence on oxygen pressure [44]. An approximately power law dependence was also established for the TiO2:Sm3+ PL response [8,39]. Therefore, a power law relation should exist between EC and Sm3+ PL (EPL) as well, leading to a straight line in a log-log plot, in agreement to the experimental result (Fig. 7, left graph). Whereas the previous results predict such a linear relation for steady-state signals, the results of the current work show that almost linear relation holds in the dynamic situation as well. The oxygen sensitivity of IPL in TiO2 has been associated with the near-surface oxygen vacancies and Ti3+ centers [6]. It is generally proposed that nonradiative relaxation pathways are created by oxygen absorption, but the response times are generally different for IPL and EC [3,5]. Whatever the exact details of the IPL mechanism in anatase are, the IPL signal is mainly controlled by another parameter(s) as compared to EC and EPL. Because of this different underlying physics, there is a complementary dependency between IPL and the other two signals so that a multivariable function involving IPL can discriminate the drift and thereby predict O2 concentration more accurately (Fig. 10). In contrast, the space spanned by EC and EPL is nearly one-dimensional and cannot so easily distinguish the effect of O2 concentration change from other influences affecting the sensor signals. As already mentioned in the Introduction, different physical phenomena and material properties have been used for transducing the gas pressure into the output signal. There are two materials, porous Si and ZnO, which have shown gas responses for both electrical and optical signals (see Table 2). In both cases, oxygen response has been recorded [3,14], but only in different experiments and not simultaneously. The same is valid for all the data on electro-optical sensors given in Table 2; all previous works show the potential for multivariate sensing with EC and PL signals but it is realized only in the present work. A different
Fig. 9. The temporal behavior of the signals (logarithms of PL intensities and EC), when subjected to the random gas program similar to the one shown in Fig. 5 but with a span ten times smaller. The temperature of the sample holder was stabilized at 25 °C. The color of data points encodes actual oxygen concentration varied between 0.21% and 2.1% in this experiment.
Fig. 10. The relative errors of different calibration functions. Only the points to the left of the red line were used for training the calibration function by minimizing the residual SSE (Eq. 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
6
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
References
Table 2 Examples of multivariable sensor materials with electrical and optical output signals. Material
Sensor signals
Gases used
Reference
Porous Si
EC, PLI
CH4, CO, O2, RH NO2, EtOH, RH EtOH EtOH, DMF, RH CO2 NO2 O2 H2, CO, NO2 O2
[3]
ZnO nanowires ZnO nanocrystals ZnO thin film with Au or Pt Sm:TiO2 nanopowder
EC, PLI, Δλ EC, PLI, Δλ C, Δλ EC, PLI EC, PLI EC, PLI EC, OA EC, PLI(EPL), PLI (IPL)
[1] R. Jaaniso, O.K. Tan (Eds.), Semiconductor Gas Sensors, 2nd ed., Elsevier, 2019. [2] N. Barsan, K. Schierbaum (Eds.), Gas Sensors Based on Conducting Metal Oxides: Basic Understanding, Technology and Applications, Elsevier, 2018. [3] G.D. Francia, V.L. Ferrara, L. Quercia, G. Faglia, Sensitivity of porous silicon photoluminescence to low concentrations of CH4 and CO, J. Porous Mater. 7 (2000) 287–290, https://doi.org/10.1023/A:1009640316318. [4] G. Faglia, C. Baratto, S. Sberveglieri, M. Zha, A. Zappettini, Adsorption effects of NO2 at ppm level on visible photoluminescence response of SnO2 nanobelts, Appl. Phys. Lett. 86 (2005) 011923, https://doi.org/10.1063/1.1849832. [5] E. Comini, C. Baratto, G. Faglia, M. Ferroni, G. Sberveglieri, Single crystal ZnO nanowires as optical and conductometric chemical sensor, J. Phys. D Appl. Phys. 40 (2007) 7255, https://doi.org/10.1088/0022-3727/40/23/S08. [6] D.K. Pallotti, L. Passoni, F. Gesuele, P. Maddalena, F. Di Fonzo, S. Lettieri, Giant O2Induced photoluminescence modulation in hierarchical titanium dioxide nanostructures, ACS Sens. 2 (2017) 61–68, https://doi.org/10.1021/acssensors.6b00432. [7] W. Di, X. Wang, X. Ren, Nanocrystalline CePO4:Tb as a novel oxygen sensing material on the basis of its redox responsive reversible luminescence, Nanotechnology 21 (2010) 075709, https://doi.org/10.1088/0957-4484/21/7/075709. [8] M. Eltermann, K. Utt, S. Lange, R. Jaaniso, Sm3+ doped TiO2 as optical oxygen sensor material, Opt. Mater. 51 (2016) 24–30, https://doi.org/10.1016/j.optmat. 2015.11.020. [9] W. Tang, Y. Sun, S. Wang, B. Du, Y. Yin, X. Liu, B. Yang, W. Cao, M. Yu, Pr3+-doped (K0.5Na0.5)NbO3 as a high response optical oxygen sensing agent, J. Mater. Chem. C 4 (2016) 11508–11513, https://doi.org/10.1039/C6TC04216C. [10] V. Kiisk, L. Puust, H. Mändar, P. Ritslaid, M. Rähn, I. Bite, D. Jankovica, I. Sildos, R. Jaaniso, Phase stability and oxygen-sensitive photoluminescence of ZrO2:Eu,Nb nanopowders, Mater. Chem. Phys. 214 (2018) 135–142, https://doi.org/10.1016/j. matchemphys.2018.04.090. [11] S.-W. Chiu, K.-T. Tang, Towards a chemiresistive sensor-integrated electronic nose: a review, Sensors 13 (2013) 14214–14247, https://doi.org/10.3390/s131014214. [12] R.A. Potyrailo, Multivariable sensors for ubiquitous monitoring of gases in the era of internet of things and industrial internet, Chem. Rev. 116 (2016) 11877–11923, https://doi.org/10.1021/acs.chemrev.6b00187. [13] M.K. Nomani, D. Kersey, J. James, D. Diwan, T. Vogt, R.A. Webb, G. Koley, Highly sensitive and multidimensional detection of NO2 using In2O3 thin films, Sens. Actuators B 160 (2011) 251–259, https://doi.org/10.1016/j.snb.2011.07.044. [14] V.S. Nguyen, Y. Champouret, M.L. Kahn, V. Jubera, H. Debéda, Simultaneously measured photoluminescent and conductive properties of printed ZnO nanocrystals under Ar/O2 mixtures, Procedia Eng. 120 (2015) 993–997, https://doi.org/10. 1016/j.proeng.2015.08.641. [15] L. Giancaterini, C. Cantalini, M. Cittadini, M. Sturaro, M. Guglielmi, A. Martucci, A. Resmini, U. Anselmi-Tamburini, Au and Pt nanoparticles effects on the optical and electrical gas sensing properties of sol-gel-Based ZnO thin-film sensors, IEEE Sens. J. 15 (2015) 1068–1076, https://doi.org/10.1109/JSEN.2014.2356252. [16] M. Bektas, T. Stöcker, A. Mergner, G. Hagen, R. Moos, Combined resistive and thermoelectric oxygen sensor with almost temperature-independent characteristics, J. Sens. Sens. Syst. 7 (2018) 289–297, https://doi.org/10.5194/jsss-7-289-2018. [17] C. Baratto, G. Faglia, G. Sberveglieri, Z. Gaburro, L. Pancheri, C. Oton, L. Pavesi, Multiparametric porous silicon sensors, Sensors 2 (2002) 121–126, https://doi.org/ 10.3390/s20300121. [18] H.-J. Kim, Y.-Y. Kim, K.-W. Lee, Multiparametric sensor based on DBR porous silicon for detection of ethanol gas, Curr. Appl. Phys. 10 (2010) 181–183, https://doi. org/10.1016/j.cap.2009.04.020. [19] T. Jalkanen, J. Tuura, E. Mäkilä, J. Salonen, Electro-optical porous silicon gas sensor with enhanced selectivity, Sens. Actuators B 147 (2010) 100–104, https:// doi.org/10.1016/j.snb.2010.03.005. [20] E. Kayahan, Porous silicon based CO2 sensors with high sensitivity, Optik 164 (2018) 271–276, https://doi.org/10.1016/j.ijleo.2018.03.024. [21] Y. Chang, Z. Hui, X. Wang, H. Qu, W. Pang, X. Duan, Dual-mode gas sensor composed of a silicon nanoribbon field effect transistor and a bulk acoustic wave resonator: a case study in Freons, Sensors 18 (343) (2018) 1–12, https://doi.org/10. 3390/s18020343. [22] R.A. Potyrailo, Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors, Chem. Soc. Rev. 46 (2017) 5311–5346, https://doi.org/10.1039/c7cs00007c. [23] E.S. Snow, F.K. Perkins, Capacitance and conductance of single-walled carbon nanotubes in the presence of chemical vapors, Nano Lett. 5 (2005) 2414–2417, https://doi.org/10.1021/nl051669c. [24] Y. Chen, H. Zhang, Z. Feng, H. Zhang, R. Zhang, Y. Yu, J. Tao, H. Zhao, W. Guo, W. Pang, X. Duan, J. Liu, D. Zhang, Chemiresistive and gravimetric dual-mode gas sensor toward target recognition and differentiation, ACS Appl. Mater. Interfaces 8 (2016) 21742–21749, https://doi.org/10.1021/acsami.6b02682. [25] Y. Yu, E. Wu, Y. Chen, Z. Feng, S. Zheng, H. Zhang, W. Pang, J. Liu, D. Zhang. Volatile organic compounds discrimination based on dual mode detection, Nanotechnology 29 (1–8) (2018) 245502, https://doi.org/10.1088/1361-6528/ aab29b. [26] T. Artursson, T. Eklöv, I. Lundström, P. Mårtensson, M. Sjöström, M. Holmberg, Drift correction for gas sensors using multivariate methods, J. Chemom. 14 (2000) 711–723, https://doi.org/10.1002/1099-128X(200009/12)14:5/6<711::AIDCEM607>3.0.CO;2-4. [27] J.E. Haugen, O. Tomic, K. Kvaal, A calibration method for handling the temporal drift of solid state gas sensors, Anal. Chim. Acta 407 (2000) 23–39, https://doi.org/ 10.1016/S0003-2670(99)00784-9.
[17] [18] [19] [20] [5] [14] [15] This work
EC – electrical conductivity; PLI – photoluminescence intensity; Δλ – wavelength shift of PL or reflectance band maximum; OA – optical absorbance; RH – relative humidity; DMF - dimethylformamide.
type of dual but only electrical oxygen sensor has been demonstrated with a combined resistive and thermoelectric response [16]. In our previous study, the Sm-doped TiO2 oxygen sensor had a very wide span ranging from less than 100 ppm to 100% of oxygen at normal pressure [39]. This is in contrast to the other type of room temperature oxygen sensors based on collisional luminescence quenching by dioxygen [47], where the range is more limited and has to be adjusted to trace or atmospheric levels by material selection. The price for a large concentration range is decreased sensitivity, leading to quite large errors in the case of a single output signal (as exemplified by EC signal in Fig. 10). Evidently, the multivariable sensing is very important for the TiO2 based gas sensors, allowing to achieve both the wide concentration span and reasonable accuracy.
5. Conclusions We have shown that three different signals from a single material, all responding to changes in the ambient oxygen concentration, can be simultaneously recorded and fused for significantly improved determination of oxygen concentration. These electrical and optical signals originated from UV-induced photoconductivity, and two photoluminescence bands of the nanopowders of Sm-doped anatase TiO2. An algorithm was introduced for signal fusion, which related the logarithm of oxygen concentration to the logarithms of signals. This relation was trained by ordinary least squares method using the sequence of oxygen concentrations changed pseudorandomly between 0.21% and 21%. It was shown that even when the simplest, linear relation between the logarithms was used, the multivariate approach with photoconductivity and internal photoluminescence signals can yield five times higher accuracy in predicting oxygen concentrations, as compared to univariate case. When photoconductivity was supplemented by the Sm3+ luminescence, the positive effect was somewhat smaller (accuracy was improved 2–3 times), which is probably caused by the mutual dependence of these two signals, due to a common governing factor in the underlying physical mechanism. The TiO2 nanomaterials with different crystal structures and morphologies are known to possess several additional intrinsic or extrinsic luminescence emissions over visible and NIR spectral ranges, providing further possibilities to develop promising multivariable sensor materials [6,48–50].
Acknowledgments This work was supported by institutional research funding (IUT3427) of the Estonian Ministry of Education and Research. The authors are grateful to Peeter Ritslaid for XRF measurements and Indrek Renge for constructive remarks. 7
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
[28] S. Marco, A. Gutierrez-Galvez, Signal and data processing for machine olfaction and chemical sensing: a review, IEEE Sens. J. 12 (2012) 3189–3214. [29] X. Chen, S.S. Mao, Titanium dioxide nanomaterials: synthesis, properties, modifications, and applications, Chem. Rev. 107 (2007) 2891–2959, https://doi.org/10. 1021/cr0500535. [30] N. Rahimi, R.A. Pax, E.M. Gray, Review of functional titanium oxides. I: TiO2 and its modifications, Prog. Sol. State Chem. 44 (2016) 86–105, https://doi.org/10.1016/j. progsolidstchem.2016.07.002. [31] C. Garzella, E. Comini, E. Tempesti, C. Frigeri, G. Sberveglieri, TiO2 thin films by a novel sol–gel processing for gas sensor applications, Sens. Actuators B 68 (2000) 189–196, https://doi.org/10.1016/S0925-4005(00)00428-7. [32] R. Rella, J. Spadavecchia, M.G. Manera, S. Capone, A. Taurino, M. Martino, A.P. Caricato, T. Tunno, Acetone and ethanol solid-state gas sensors based on TiO2 nanoparticles thin film deposited by matrix assisted pulsed laser evaporation, Sens. Actuators B 127 (2007) 426–431, https://doi.org/10.1016/j.snb.2007.04.048. [33] M. Enachi, O. Lupan, T. Braniste, A. Sarua, L. Chow, Y.K. Mishra, D. Gedamu, R. Adelung, I. Tiginyanu, Integration of individual TiO2 nanotube on the chip: nanodevice for hydrogen sensing, Phys. Status Solidi RRL 9 (2015) 171–174, https://doi.org/10.1002/pssr.201409562. [34] N. Ababiia, M. Hoppe, S. Shree, A. Vahl, M. Ulfa, T. Pauporté, B. Viana, V. Cretu, N. Magariu, V. Postica, V. Sontea, M.-I. Terasa, O. Polonskyi, F. Faupel, R. Adelung, O. Lupan, Effect of noble metal functionalization and film thickness on sensingproperties of sprayed TiO2ultra-thin films, Sens. Actuators A 293 (2019) 242–258, https://doi.org/10.1016/j.sna.2019.04.017. [35] H. Tang, H. Berger, P.E. Schmid, F. Lévy, G. Burri, Photoluminescence in TiO2 anatase single crystals, Solid State Commun. 87 (1993) 847–850, https://doi.org/ 10.1016/0038-1098(93)90427-O. [36] Y. Lei, L.D. Zhang, G.W. Meng, G.H. Li, X.Y. Zhang, C.H. Liang, W. Chen, S.X. Wang, Preparation and photoluminescence of highly ordered TiO2 nanowire arrays, Appl. Phys. Lett. 78 (2001) 1125–1127, https://doi.org/10.1063/1.1350959. [37] T. Sekiya, M. Tasaki, K. Wakabayashi, S. Kurita, Relaxation process in anatase TiO2 single crystals with different colors, J. Lumin. 108 (2004) 69–73, https://doi.org/ 10.1016/j.jlumin.2004.01.013. [38] D. Palotti, E. Orabona, S. Amoruso, P. Maddalena, S. Lettieri, Modulation of mixedphase titania photoluminescence by oxygen adsorption, Appl. Phys. Lett. 105 (2014) 031903, https://doi.org/10.1063/1.4891038. [39] M. Eltermann, V. Kiisk, A. Berholts, L. Dolgov, S. Lange, K. Utt, R. Jaaniso, Modeling of luminescence-based oxygen sensing by redox-switched energy transfer in nanocrystalline TiO2:Sm3+, Sens. Actuators B Chem. 265 (2018) 556–564, https://doi. org/10.1016/j.snb.2018.03.034. [40] G.H. Dieke, Spectra and Energy Levels of Rare Earth Ions in Crystals, John Wiley, New York, 1968. [41] J. Shi, J. Chen, Z. Feng, T. Chen, Y. Lian, X. Wang, C. Li, Photoluminescence characteristics of TiO2 and their relationship to the photoassisted reaction of Water/ Methanol mixture, J. Phys. Chem. C 111 (2007) 693–699, https://doi.org/10.1021/ jp065744z. [42] D.K. Pallotti, L. Passoni, P. Maddalena, F. Di Fonzo, S. Lettieri, Photoluminescence
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
Mechanisms in Anatase and Rutile TiO2, J. Phys. Chem. C 121 (2017) 9011–9021, https://doi.org/10.1021/acs.jpcc.7b00321. J. Nelson, A.M. Eppler, I.M. Ballard, Photoconductivity and charge trapping in porous nanocrystalline titanium dioxide, J. Photochem. Photobiol. A 148 (2002) 25–31, https://doi.org/10.1016/S1010-6030(02)00035-7. N. Yamazoe, K. Shimanoe, Theory of power laws for semiconductor gas sensors, Sens. Actuators B 128 (2008) 566–573, https://doi.org/10.1016/j.snb.2007.07. 036. S. Lenaerts, J. Roggen, G. Maes, FT-IR characterization of tin dioxide gas sensor materials under working conditions, Spectrochim. Acta A 51 (1995) 883–894, https://doi.org/10.1016/0584-8539(94)01216-4. E. Konstantinova, J. Weidmann, Th. Dittrich, Influence of adsorbed water and oxygen on the photoluminescence and EPR of por-TiO2 (Anatase), J. Porous Mater. 7 (2000) 389–392, https://doi.org/10.1023/A:1009699324013. X. Wang, O.S. Wolfbeis, Optical methods for sensing and imaging oxygen: materials, spectroscopies and applications, Chem. Soc. Rev. 43 (2014) 3666–3761, https:// doi.org/10.1039/C4CS00039K. D.K. Pallotti, S. Amoruso, E. Orabona, P. Maddalena, S. Lettieri, Biparametric optical sensing of oxygen by titanium dioxide, Sens. Actuators B 221 (2015) 515–520, https://doi.org/10.1016/j.snb.2015.06.116. A. Chakraborty, G.H. Debnath, N.R. Saha, D. Chattopadhyay, D.H. Waldeck, P. Mukherjee, Identifying the correct host−Guest combination to sensitize trivalent lanthanide (Guest) luminescence: titanium dioxide nanoparticles as a model host system, J. Phys. Chem. C 120 (2016) 23870–23882, https://doi.org/10.1021/acs. jpcc.6b08421. V. Kiisk, K. Akulitš, M. Kodu, T. Avarmaa, H. Mändar, J. Kozlova, M. Eltermann, L. Puust, R. Jaaniso, Oxygen-sensitive photoluminescence of rare earth ions in TiO2 thin films, J. Phys. Chem. C 123 (2019) 17908–17914, https://doi.org/10.1021/ acs.jpcc.9b04588.
Marko Eltermann received his PhD in physics from the University of Tartu in 2019. His research is focused on the gas sensing properties of doped TiO2 nanomaterials. Valter Kiisk received his PhD in solid-state physics from the University of Tartu in 2006. His research activities mainly involve spectroscopic studies and applications of intrinsic and rare earth luminescence in various metal-oxide nanomaterials. Currently he is a senior research fellow at the University of Tartu, exploring rare earth activated oxide materials for luminescent gas sensing. Raivo Jaaniso received his PhD in solid-state physics from the Institute of Physics at the Estonian Academy of Sciences in 1988. His research interests have covered a wide area from laser spectroscopy and spectral hole burning to pulsed laser deposition of thin films and, most recently, development of luminescent, semiconductor, and graphene-based gas sensor materials. He is the head of the laboratory of sensor technologies at the University of Tartu.
8
Contents lists available at ScienceDirect
Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
Multivariable oxygen sensing based on photoluminescence and photoconductivity of TiO2 nanoparticles
T
M. Eltermann, V. Kiisk, A. Kikas, S. Lange, R. Jaaniso⁎ Institute of Physics, University of Tartu, W. Ostwald St 1, Tartu 50411, Estonia
A R T I C LE I N FO
A B S T R A C T
Keywords: Oxygen sensor Multivariable gas sensor Drift correction TiO2 Photoluminescence Photoconductivity
We studied sol-gel-prepared crystalline (anatase) TiO2 nanopowder coatings, exhibiting simultaneously three different oxygen-sensitive signals under ultraviolet illumination: photoconductivity, photoluminescence of intrinsic defects, and photoluminescence of Sm3+ impurity ions. The material was subjected to a flow of O2/N2 gas mixture at normal pressure, where the volume fraction of oxygen was precisely controlled. All three signals responded to changes in oxygen content but also exhibited long-term drifts. The signals were fused by a multivariable model, which related the logarithm of oxygen concentration to a polynomial composed of signal logarithms. Extensive testing by the ordinary least squares optimization with randomly generated O2 concentrations was used for training the model. It was demonstrated that the intrinsic luminescence in combination with one of the remaining two signals suppressed the drifts and significantly improved the precision in predicting the actual oxygen levels. The rms errors were improved by five times in the experiments where the oxygen concentration was randomly varied between 0.21% and 21% during two days. The combination of photoconductivity and Sm3+ luminescence resulted in somewhat smaller improvement (2–3 times), because of the mutual dependence of these two signals caused by similarities in the underlying physical mechanisms.
1. Introduction A simple but very fruitful method in solid-state gas sensing is based on the effect of surface adsorption on the electrical conductivity of sensing material. Sensors based on metal oxide semiconductors are most widely studied and commercialized, whereas the nanoscale materials have generally an enhanced sensitivity [1,2]. At the same time, several materials also have other easily measurable properties such as intrinsic photoluminescence (PL), which can be altered by the gas environment [3–6]. Photoluminescence properties can be further modified by doping, e.g. by incorporation of rare earth ions, and such extrinsic PL can also be influenced by ambient gases [7–10]. While having a simple construction, low cost, and high sensitivity, the semiconductor gas sensors also have drawbacks such as relatively low selectivity and large signal drift. Several strategies have been proposed to overcome these deficits, such as using multi-sensor arrays [11] or a multivariable detection scheme with a single sensor [12]. Besides metal oxides [5,13–16], the latter approach has been realized in a variety of materials like porous silicon [3,17–20], Si nanoribbons [21], conductive polymers [12], coated layers of metal nanoparticles [22], carbon nanotubes [23], and graphene [24,25]. At least two physical quantities were recorded either electrically (conductance, ⁎
capacitance, work function), acoustically (shift of resonant frequency or phase), or optically (photoluminescence, reflectance, absorbance). In early works different signals were not recorded simultaneously but in different experiments, which nevertheless demonstrated the potential of multivariable sensing materials. In advanced studies the recording of different sensor signals was performed simultaneously and the possibility of selective gas detection, based on different trajectories in the multidimensional measurand space was demonstrated directly [12,13,19,24,25]. In addition to discrimination of different gases and volatile compounds, the multivariable sensing can also be used for reducing the drift of the sensors thus increasing their overall precision. This problem has been widely investigated in the case of single-output sensor arrays [26–28]. It has not been used, to the best of our knowledge, for improving the accuracy of a single sensor with multiple responses. To demonstrate that the gas sensing accuracy can be improved by fusing different signals from the same material, we have chosen Smdoped TiO2 as multifunctional test material and dioxygen as a test gas. TiO2 is a wide-bandgap semiconducting oxide [29,30], which has commonly n-type conductivity and a conductometric gas response to several gases [31–34]. It also has intrinsic photoluminescence (IPL), depending on the nature of defects and (nano)morphology of the
Corresponding author. E-mail address: [email protected] (R. Jaaniso).
https://doi.org/10.1016/j.snb.2019.127236 Received 11 April 2019; Received in revised form 28 August 2019; Accepted 7 October 2019 Available online 11 October 2019 0925-4005/ © 2019 Elsevier B.V. All rights reserved.
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
material [35–37]. This IPL has been shown to be sensitive to oxygen gas [6,38]. The PL of Sm dopant in TiO2 (anatase) was also shown to be an effective signal for oxygen sensing [8,39]. Consequently, this material has multiple electrical and optical responses to oxygen gas, which have not been studied simultaneously before but can be potentially useful for multivariable sensing. The other reason for using oxygen in our proofof-principle experiments is that O2 is an important gas always present in ambient air, and the knowledge about oxygen response is a precondition for understanding the response mechanisms in the case of reducing gases or volatile compounds. In the present work, the electrical conductivity and photoluminescence responses of Sm-doped, TiO2 nanostructured materials to oxygen gas were recorded simultaneously. Two different PL signals, one originated from the internal defects of anatase and the other from Sm3+ ions, were spectrally resolved and analyzed. The three signals were successfully combined to improve notably the accuracy of the actual O2 concentration measurement. Fig. 2. XPS spectra of the sol-gel prepared TiO2:Sm3+ nanopowder. Lower panel presents an overview spectrum. Upper panels show details in the Sm 3d, O 1s, and Ti 2p regions.
2. Experimental set-up and characterization The TiO2 nanopowder, activated with Sm3+ ions, was prepared by using a sol-gel route as described in Ref. [8]. According to Raman scattering, the annealed powder contained a well-developed anatase phase (Fig. 1a), with ≈40 nm sized crystallites agglomerated into a hierarchical porous structure (Fig. 1b). The powder was ultrasonically dispersed in distilled water, and the resulting opaque white suspension was drop coated onto a glass substrate carrying interdigitated gold electrodes (Fig. 1c). A solid layer of TiO2 was formed on the electrodes after the water evaporation (Fig. 1d). The content of Sm in the samples was determined by x-ray fluorescence (XRF) to be 1.8 at%. The doping by Sm was confirmed by photoelectron spectra, as presented in Fig. 2. The lower panel shows an overview spectrum,
where photoelectron lines belonging to samarium, oxygen, and titanium can be observed. Traces of indium are also seen because the nanopowder of TiO2:Sm3+ was pressed into In metal for XPS measurements. The upper panel shows detailed spectra of Sm 3d, O 1s, and Ti 2p photoelectron lines. The shape of Ti 2p spectra confirms that the titanium oxidation state is mainly Ti4+. The areas of peaks in these spectra were used to estimate relative contents of the elements in the sample by CasaXPS software, which gave the following results: Ti - 32.1 at%, O - 65.3 at%, and Sm - 1.4 at%. The content of samarium (1.4 %) differs from the value determined by XRF (1.8%), for the reason that the
Fig. 1. a) Raman spectrum of the sol-gel-prepared TiO2:Sm3+ nanopowder. Indicated peaks correspond to the anatase phase of TiO2. b) Scanning electron micrograph of the powder. c) A diagram of the placement of TiO2:Sm3+ powder patch (red circle) on top of interdigitated electrodes. d) An optical image of the powder layer on the interdigitated electrodes. e) Setup of the electrical/optical gas sensing experiment. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 2
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
attributed to recombination of free electrons with trapped holes localized at specific defects, most probably oxygen vacancies and/or Ti3+ ions on the surface of nanocrystals [41,42]. To characterize the temporal responses of the optical and electrical signals, the PL spectra and EC were registered at fixed intervals of 1 s. The Sm3+ PL signal was calculated as an area of the main emission band between 610 and 630 nm after background subtraction (Fig. 3). In Fig. 4a, both EC and Sm3+ PL intensity signals are shown under steady-state conditions and at switching the UV light incident on the sample on and off. In the dark, during the UV light off, the sample had a relatively small EC which was increased by several orders of magnitude under UV irradiation. A transient behavior was observed upon switching the irradiation on or off. When the transient curves were fitted with an exponential function, the obtained time constants were in the order of tens of seconds. Similar responses were observed for the Sm3+ PL intensity. The temporal changes of the two signals were very well correlated: an increase of EC was always associated with a decrease of Sm3+ PL, and vice versa. This correspondence persisted when the composition of the ambient gas was changed: both signals responded systematically, but in an opposite fashion to the changes in oxygen content in the gas flow (Fig. 4b). When considered separately, such responses of the two signals to ambient oxygen are well established. Since the photoconductivity of nanocrystalline anatase is n-type, it is indeed expected to decrease with increasing oxygen pressure because of electron scavenging by surface adsorbed oxygen [43]. At the same time, the increase of PL intensity with increasing oxygen concentration has been explained by a special mechanism where the adsorbed oxygen deactivates the PL-quenching defects through an electron transfer [39].
Sm content may be different on the surface and inside the nano-powder grains. However, this difference is close to the uncertainty of the determination of concentrations (0.2%). The Raman spectrum was recorded on a Renishaw inVia Raman Spectrometer with the excitation wavelength 514 nm. X-ray Photoelectron Spectroscopy (XPS) analysis was performed by using the electron energy analyzer Scienta SES 100. The Mg K-α radiation was used for the excitation. The incident angle of the photon beam was 45°, and the photoelectrons were detected along the surface normal. CasaXPS software was used to estimate the relative content of the sample. XRF analysis was made with spectrometer Rigaku ZSX-400. The gas sensitivity experiments (as illustrated in Fig. 1e) were carried out with the sample placed on a temperature-controlled stage inside an air-tight cell (Linkam THMS350 V). The nitrogen and oxygen gases were both 99.999% pure. A continuous flow of the O2/N2 mixture through the cell at atmospheric pressure was maintained by calibrated mass flow controllers (Brooks SLA5820). The composition of the test gas was fixed by setting the flow rates of source gases individually and keeping the total flow rate at 200 ml/min. Before the gas sensing measurements, the sample was always preheated at 150 °C for at least 10 min in a dry gas flow to ensure a well-defined initial state of the sample and to minimize possible side effects due to condensed water. During the measurements, the samples were held in the dry flowing gas at a stabilized temperature (25 or 50 °C), slightly above the room temperature (20–22 °C). The sample in the cell was excited through a quartz window by a light-emitting diode (Thorlabs M365LP1, dominant wavelength 365 nm). The induced photoluminescence was dispersed with a monochromator (LOMO MDR-23) and detected by a CCD camera (Andor DU240-BU). Simultaneously, the electrical conductance was recorded with a Source Measure Unit instrument (Keithley K2450), by applying a constant voltage 1 V between the interdigitated electrodes.
3.2. Long term multivariable measurements For long term measurements, a special test program was implemented, which generated randomly but continuously changing oxygen concentration between 21% and 0.21% throughout several days. The temporal profile of the concentration is given in Fig. 5. The TiO2:Sm3+ material was exposed to the random gas program, while simultaneously monitoring PL intensities and EC. In addition to the external PL (EPL) of Sm3+ ions, the defect-related broadband internal PL (IPL) was also recorded. The signal of IPL was evaluated as an area of the broad emission band between 530–545 nm, away from Sm3+ emission lines (Fig. 3). The oxygen response of IPL was relatively weak but, as will be shown later, plays a valuable role in the multivariable approach. Large data set (time series) was collected where each data point contained the actual oxygen concentration (determined by the gas program) along with the corresponding EPL and IPL intensities and EC values. The time series of logarithms of the three signals marked as SEPL, SIPL, and SEC, respectively, are depicted in Fig. 6. The logarithms were used because this allowed the linearization of EC and EPL signals, that both had approximately power-law dependencies on oxygen concentration. The dependence of EC on gas concentration obeys the power-law quite generally [44], and approximately powerlaw dependence is justified for the dependence of Sm3+ PL intensity on oxygen concentration [8,39]. It can be seen in Fig. 6 that the Sm3+ PL and EC are very closely correlated. As a result, if the logarithmic signals are plotted against each other, all data points will fall on an almost straight line (Fig. 7), which points to a common physical process responsible for the magnitudes of both the Sm3+ PL and EC. High levels of ambient O2 quench the intrinsic PL, in contrary to Sm3+ PL. It also appears that IPL responds more slowly to the changes in oxygen content. Over the 50 h period of time, all the signals have a drift that is most significantly expressed for IPL because of its weaker oxygen response. Fig. 7 depicts the pairwise correlations between the signals. As already noted, logarithmic EC and EPL signals are almost linearly related. Still, some shift of the straight line occurs in time, which is caused by
3. Results 3.1. Elemental gas responses The PL spectrum of TiO2:Sm3+ nanopowder obtained under UV excitation consisted of sharp spectral lines on top of a broad emission band (Fig. 3). The sharp lines are typical to Sm3+ ion in a regular crystalline surrounding of anatase phase of TiO2 and emerge as a result of crystal field splitting of various 4G5/2→6HJ transitions in the 4f shell [40]. The PL excitation spectra, studied by us before [8], clearly demonstrate the effective excitation of Sm3+ PL by means of electronic energy transfer from the electronic states of the host matrix. The broad intrinsic emission band centered around 500 nm (2.5 eV) is also frequently observed in TiO2 (anatase) nanomaterials. It is usually
Fig. 3. Typical PL emission spectrum of annealed TiO2:Sm3+ nanopowder under UV excitation. The shaded areas were used to evaluate the Sm3+ PL intensity and the defect-related PL intensity. 3
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
Fig. 4. a) The effect of switching on/off the UV excitation on the Sm3+ PL intensity and EC in N2 atmosphere. b) The response of PL intensity and EC to oxygen exposure at different concentrations. The signals were measured under constant UV excitation at T = 50 °C and are normalized relative to the initial signal.
one of the other two signals could, in principle, more reliably predict the ambient O2 concentration. 3.3. Multivariate data analysis We introduce a bivariate function f so that the logarithm of the predicted oxygen concentration is expressed as
ln(p [O2]) = f (S1, S2)
(1)
Where, S1 and S2 are two logarithmic signals from the studied dataset, e.g., S1 can be SIPL and S2 can be SEC or SEPL. Any pairwise combination of the recorded signals can be used. The function f must be sufficiently flexible so that it can be “trained” to fit the data. The bivariate dependences on Fig. 6 are quite close to a linear one; hence it is justified to expand the unknown function into a Taylor series:
Fig. 5. The random temporal profile of O2 concentration used in long-term measurements. The histogram on the right panel illustrates the overall distribution of the concentration values used.
ln(p [O2]) = a0 + a1 S1 + a2 S2 + a11 S12 + a12 S1 S2 + a22 S22 + …
(2)
(truncated after 2nd order terms for clarity). Arbitrarily high order terms could be introduced to fit complex bivariate data. However, a higher-order polynomial would become numerically unstable and unable to yield reasonable results beyond the training data set. The unknown coefficients a 0 , ai , ai, j , etc. can be determined (trained) by minimizing a cost function taken as the residual sum of squared errors (SSE) between the logarithms of the predicted p [O2] and the corresponding true concentrations t [O2], N
SSE =
N
p
2
∑ [ln(pi ) − ln(ti )]2 = ∑ ln ⎛ t i ⎞ ⎜
i=1
i=1
⎟
(3)
⎝ i⎠
where N is the number of data points. The advantage of the approach is that the optimization is an ordinary least squares (OLS) problem, due to the linear dependence of the fitting function (Eq. 2) on the unknown parameters, and the particular form of the cost function. Regardless of the number of variables, the OLS problem leads to a system of linear equations. Therefore, the computation is straightforward and yields a unique solution. To simulate a real-life scenario where the sensor is first calibrated and then put into use, and in order to avoid overfitting with arbitrarily high order polynomials, we split the data set into a training set (first 60% of the data points) and a test set (the last 40% of the data points). The training set was utilized for OLS optimization where the cost function defined by Eq. (3) was minimized. Eq. (3) involves the ratio p [O2]/ t [O2] and is presumably well correlated to the practical figure of merit (FOM), which we define as the root mean square of the relative error,
Fig. 6. The temporal behavior of the signals (logarithms of PL intensities and EC) under the random O2 gas concentration program shown in Fig. 5. The temperature of sample holder was stabilized at 25 °C. The color of data points encodes actual oxygen concentration varied between 0.21% and 21% in this experiment.
the drifts in both signals. The defect-related PL shows a different type of correlation being relatively independent on Sm3+ PL or EC. At any given O2 concentration there is a tendency that an increase of Sm3+ PL signal or decrease of EC would correspond to a decrease of IPL. More importantly, there are almost no occurrences where different O2 concentrations map to the same coordinates in these two-dimensional spaces. Therefore, the defect-related PL intensity in combination with
FOM =
N 2 1 ⎛ pi − ti ⎞ = ∑ N i = 1 ⎝ ti ⎠ ⎜
⎟
N 2 1 ⎛ pi − 1⎞ ∑ N i = 1 ⎝ ti ⎠ ⎜
⎟
(4)
Therefore, the test data set was used to calculate the figure of merit 4
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
Fig. 7. Mutual correlations of three signals - logarithms of intensities of Sm3+ and defect-related PL and EC - plotted pairwise against each other.
Table 1 The figure of merit (by Eq. 4) of the optimized models with different signal combinations and different polynomial orders. Order/Signals
EC
EPL
IPL
EC + EPL
EC + IPL
EPL + IPL
EC + EPL + IPL
1 2 3
0.893 0.908 0.920
0.694 0.710 0.721
2.162 3.511 10.121
0.356 0.292 0.413
0.175 0.213 0.280
0.207 0.223 0.319
0.187 0.223 0.395
fitted but predicted.
of the optimized model, according to Eq. (4). We started from polynomial order n = 1 and increased n until no further reduction of the error was observed. Different combinations of the three signals were tested. Table 1 lists the FOMs produced by various models. It has to be noted that two variables were used in the formulas (1) and (2) for clarity and it is straightforward to generalize the approach for an arbitrary number of signals. In the first three columns of the table, the results for the univariate models are shown and, in the last column of the table, the results are given for the case with all three signals involved. It follows from Table 1 that the multivariable approach resulted in significantly reduced errors. In most cases, except EC + EPL, using Eq. (2) with only first-order terms resulted in the smallest error. The signal combinations with the involvement of IPL gave the best results. Note that this is true in spite of the relatively large error of the IPL signal alone (4th column) due to its relatively small response on the background of large drift (see Fig. 5). Fig. 8 demonstrates the prediction of the model as a continuous colored background superimposed on the experimental data shown in Fig. 7. This presentation allows to visualize the errors in a more detailed way as for an ideal fitting one should not see the experimental trajectories at all in such a plot. One can see that there are fewer discrepancies in the SEC vs. SEPL plot, because of the good correlation of these signals. We will further demonstrate the reduction of measurement error, including the reduction of long-term drift, with the results of another experiment, where the oxygen concentration was varied within a smaller span, between 0.21% and 2.1%. In this example, the signal changes due to variations in oxygen concentrations are significantly smaller as compared to the drifts of the background (Fig. 9). Similar training procedure with OLS minimization was conducted on this dataset. Fig. 10 compares the relative errors of optimized models for a univariate case of electrical conductance, and a bivariate case with internal PL added to conductance signal. As one can see, a significant improvement of the precision was achieved not only in the training set (0–32 h) but also at the later stage when the oxygen concentration is not
4. Discussion The previous studies of oxygen responsive PL of a similar TiO2:Sm3+ powder [8,39] implied that adsorption of oxygen on the nanocrystals has an indirect influence on fluorescence quantum yield of embedded Sm3+ ions while their excitation pathway remains almost unaffected. It was proposed that some defects in TiO2 lattice (such as oxygen vacancies) act as fluorescence quenchers in a certain charge state. These defects can exchange electrons with the adsorbed oxygen species, probably over the conduction band of TiO2. Hence, for a given morphology of the material and at a fixed excitation rate, the fluorescence yield of the Sm3+ ions is determined by the amount of charge trapped on the crystallite surface. The temporal and ambient-sensitive behavior of photoconductivity (Fig. 4b) is in qualitative agreement with some previous studies establishing that photoconductivity of TiO2 (anatase) films relatively quickly stabilized in the air, whereas in vacuum the photoconductivity slowly increased by several orders of magnitude [34,42]. The latter was associated with suppression of electron scavenging, owing to the removal of adsorbed molecular oxygen. We observe highly correlated responses from Sm3+ emission and photoconductivity (Figs. 4, 6 and 7). There is a direct relationship between the two signals at every moment of time, which points to a single controlling parameter. Let’s suppose that this parameter is the density of conduction electrons. While this would naturally lead to increased photoconductivity, the excitation rate of Sm3+ ions via band-to-band excitations should also increase, leading to a stronger PL which is not observed. Also, the Sm3+ PL and photoconductivity cannot be considered to have somehow concurrent relaxation paths, as it would contradict the established gas sensing mechanism of Sm3+ PL, controlled by PL quenching, rather than by excitation rate. Another possibility is that the density of conduction electrons remains approximately constant (independently of gas adsorption), determined by the other, more intense steady-state processes, such as the 5
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
Fig. 8. Visualization of the bivariate approximation. The same signal trajectories as in Fig. 6 are shown in the background of the model functions.
photogeneration of charge carriers which is approximately balanced by recombination through various pathways (incl. excitation of PL centers). In that case, the EC of the powder layer is controlled by the intercrystallite conductivity, which is affected by the space-charge layers (double-Schottky barriers) – induced by gas adsorption on the crystallite surface - a common situation for metal oxide gas sensors [1,2]. Under these assumptions, EC and Sm3+ PL are both uniquely determined by the amount of adsorbed species (and, of course, the intensity of the optical excitation), so that a unique relationship between these two quantities should also exist. Consequently, the same surface oxygen species are active in both cases. It has been shown that at room temperature the negatively charged oxygen molecular ion is the dominant adsorbate on metal oxides, in particular, TiO2 [45,46]. The conductivity of such a semiconductor layer is known to follow a power-law dependence on oxygen pressure [44]. An approximately power law dependence was also established for the TiO2:Sm3+ PL response [8,39]. Therefore, a power law relation should exist between EC and Sm3+ PL (EPL) as well, leading to a straight line in a log-log plot, in agreement to the experimental result (Fig. 7, left graph). Whereas the previous results predict such a linear relation for steady-state signals, the results of the current work show that almost linear relation holds in the dynamic situation as well. The oxygen sensitivity of IPL in TiO2 has been associated with the near-surface oxygen vacancies and Ti3+ centers [6]. It is generally proposed that nonradiative relaxation pathways are created by oxygen absorption, but the response times are generally different for IPL and EC [3,5]. Whatever the exact details of the IPL mechanism in anatase are, the IPL signal is mainly controlled by another parameter(s) as compared to EC and EPL. Because of this different underlying physics, there is a complementary dependency between IPL and the other two signals so that a multivariable function involving IPL can discriminate the drift and thereby predict O2 concentration more accurately (Fig. 10). In contrast, the space spanned by EC and EPL is nearly one-dimensional and cannot so easily distinguish the effect of O2 concentration change from other influences affecting the sensor signals. As already mentioned in the Introduction, different physical phenomena and material properties have been used for transducing the gas pressure into the output signal. There are two materials, porous Si and ZnO, which have shown gas responses for both electrical and optical signals (see Table 2). In both cases, oxygen response has been recorded [3,14], but only in different experiments and not simultaneously. The same is valid for all the data on electro-optical sensors given in Table 2; all previous works show the potential for multivariate sensing with EC and PL signals but it is realized only in the present work. A different
Fig. 9. The temporal behavior of the signals (logarithms of PL intensities and EC), when subjected to the random gas program similar to the one shown in Fig. 5 but with a span ten times smaller. The temperature of the sample holder was stabilized at 25 °C. The color of data points encodes actual oxygen concentration varied between 0.21% and 2.1% in this experiment.
Fig. 10. The relative errors of different calibration functions. Only the points to the left of the red line were used for training the calibration function by minimizing the residual SSE (Eq. 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
6
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
References
Table 2 Examples of multivariable sensor materials with electrical and optical output signals. Material
Sensor signals
Gases used
Reference
Porous Si
EC, PLI
CH4, CO, O2, RH NO2, EtOH, RH EtOH EtOH, DMF, RH CO2 NO2 O2 H2, CO, NO2 O2
[3]
ZnO nanowires ZnO nanocrystals ZnO thin film with Au or Pt Sm:TiO2 nanopowder
EC, PLI, Δλ EC, PLI, Δλ C, Δλ EC, PLI EC, PLI EC, PLI EC, OA EC, PLI(EPL), PLI (IPL)
[1] R. Jaaniso, O.K. Tan (Eds.), Semiconductor Gas Sensors, 2nd ed., Elsevier, 2019. [2] N. Barsan, K. Schierbaum (Eds.), Gas Sensors Based on Conducting Metal Oxides: Basic Understanding, Technology and Applications, Elsevier, 2018. [3] G.D. Francia, V.L. Ferrara, L. Quercia, G. Faglia, Sensitivity of porous silicon photoluminescence to low concentrations of CH4 and CO, J. Porous Mater. 7 (2000) 287–290, https://doi.org/10.1023/A:1009640316318. [4] G. Faglia, C. Baratto, S. Sberveglieri, M. Zha, A. Zappettini, Adsorption effects of NO2 at ppm level on visible photoluminescence response of SnO2 nanobelts, Appl. Phys. Lett. 86 (2005) 011923, https://doi.org/10.1063/1.1849832. [5] E. Comini, C. Baratto, G. Faglia, M. Ferroni, G. Sberveglieri, Single crystal ZnO nanowires as optical and conductometric chemical sensor, J. Phys. D Appl. Phys. 40 (2007) 7255, https://doi.org/10.1088/0022-3727/40/23/S08. [6] D.K. Pallotti, L. Passoni, F. Gesuele, P. Maddalena, F. Di Fonzo, S. Lettieri, Giant O2Induced photoluminescence modulation in hierarchical titanium dioxide nanostructures, ACS Sens. 2 (2017) 61–68, https://doi.org/10.1021/acssensors.6b00432. [7] W. Di, X. Wang, X. Ren, Nanocrystalline CePO4:Tb as a novel oxygen sensing material on the basis of its redox responsive reversible luminescence, Nanotechnology 21 (2010) 075709, https://doi.org/10.1088/0957-4484/21/7/075709. [8] M. Eltermann, K. Utt, S. Lange, R. Jaaniso, Sm3+ doped TiO2 as optical oxygen sensor material, Opt. Mater. 51 (2016) 24–30, https://doi.org/10.1016/j.optmat. 2015.11.020. [9] W. Tang, Y. Sun, S. Wang, B. Du, Y. Yin, X. Liu, B. Yang, W. Cao, M. Yu, Pr3+-doped (K0.5Na0.5)NbO3 as a high response optical oxygen sensing agent, J. Mater. Chem. C 4 (2016) 11508–11513, https://doi.org/10.1039/C6TC04216C. [10] V. Kiisk, L. Puust, H. Mändar, P. Ritslaid, M. Rähn, I. Bite, D. Jankovica, I. Sildos, R. Jaaniso, Phase stability and oxygen-sensitive photoluminescence of ZrO2:Eu,Nb nanopowders, Mater. Chem. Phys. 214 (2018) 135–142, https://doi.org/10.1016/j. matchemphys.2018.04.090. [11] S.-W. Chiu, K.-T. Tang, Towards a chemiresistive sensor-integrated electronic nose: a review, Sensors 13 (2013) 14214–14247, https://doi.org/10.3390/s131014214. [12] R.A. Potyrailo, Multivariable sensors for ubiquitous monitoring of gases in the era of internet of things and industrial internet, Chem. Rev. 116 (2016) 11877–11923, https://doi.org/10.1021/acs.chemrev.6b00187. [13] M.K. Nomani, D. Kersey, J. James, D. Diwan, T. Vogt, R.A. Webb, G. Koley, Highly sensitive and multidimensional detection of NO2 using In2O3 thin films, Sens. Actuators B 160 (2011) 251–259, https://doi.org/10.1016/j.snb.2011.07.044. [14] V.S. Nguyen, Y. Champouret, M.L. Kahn, V. Jubera, H. Debéda, Simultaneously measured photoluminescent and conductive properties of printed ZnO nanocrystals under Ar/O2 mixtures, Procedia Eng. 120 (2015) 993–997, https://doi.org/10. 1016/j.proeng.2015.08.641. [15] L. Giancaterini, C. Cantalini, M. Cittadini, M. Sturaro, M. Guglielmi, A. Martucci, A. Resmini, U. Anselmi-Tamburini, Au and Pt nanoparticles effects on the optical and electrical gas sensing properties of sol-gel-Based ZnO thin-film sensors, IEEE Sens. J. 15 (2015) 1068–1076, https://doi.org/10.1109/JSEN.2014.2356252. [16] M. Bektas, T. Stöcker, A. Mergner, G. Hagen, R. Moos, Combined resistive and thermoelectric oxygen sensor with almost temperature-independent characteristics, J. Sens. Sens. Syst. 7 (2018) 289–297, https://doi.org/10.5194/jsss-7-289-2018. [17] C. Baratto, G. Faglia, G. Sberveglieri, Z. Gaburro, L. Pancheri, C. Oton, L. Pavesi, Multiparametric porous silicon sensors, Sensors 2 (2002) 121–126, https://doi.org/ 10.3390/s20300121. [18] H.-J. Kim, Y.-Y. Kim, K.-W. Lee, Multiparametric sensor based on DBR porous silicon for detection of ethanol gas, Curr. Appl. Phys. 10 (2010) 181–183, https://doi. org/10.1016/j.cap.2009.04.020. [19] T. Jalkanen, J. Tuura, E. Mäkilä, J. Salonen, Electro-optical porous silicon gas sensor with enhanced selectivity, Sens. Actuators B 147 (2010) 100–104, https:// doi.org/10.1016/j.snb.2010.03.005. [20] E. Kayahan, Porous silicon based CO2 sensors with high sensitivity, Optik 164 (2018) 271–276, https://doi.org/10.1016/j.ijleo.2018.03.024. [21] Y. Chang, Z. Hui, X. Wang, H. Qu, W. Pang, X. Duan, Dual-mode gas sensor composed of a silicon nanoribbon field effect transistor and a bulk acoustic wave resonator: a case study in Freons, Sensors 18 (343) (2018) 1–12, https://doi.org/10. 3390/s18020343. [22] R.A. Potyrailo, Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors, Chem. Soc. Rev. 46 (2017) 5311–5346, https://doi.org/10.1039/c7cs00007c. [23] E.S. Snow, F.K. Perkins, Capacitance and conductance of single-walled carbon nanotubes in the presence of chemical vapors, Nano Lett. 5 (2005) 2414–2417, https://doi.org/10.1021/nl051669c. [24] Y. Chen, H. Zhang, Z. Feng, H. Zhang, R. Zhang, Y. Yu, J. Tao, H. Zhao, W. Guo, W. Pang, X. Duan, J. Liu, D. Zhang, Chemiresistive and gravimetric dual-mode gas sensor toward target recognition and differentiation, ACS Appl. Mater. Interfaces 8 (2016) 21742–21749, https://doi.org/10.1021/acsami.6b02682. [25] Y. Yu, E. Wu, Y. Chen, Z. Feng, S. Zheng, H. Zhang, W. Pang, J. Liu, D. Zhang. Volatile organic compounds discrimination based on dual mode detection, Nanotechnology 29 (1–8) (2018) 245502, https://doi.org/10.1088/1361-6528/ aab29b. [26] T. Artursson, T. Eklöv, I. Lundström, P. Mårtensson, M. Sjöström, M. Holmberg, Drift correction for gas sensors using multivariate methods, J. Chemom. 14 (2000) 711–723, https://doi.org/10.1002/1099-128X(200009/12)14:5/6<711::AIDCEM607>3.0.CO;2-4. [27] J.E. Haugen, O. Tomic, K. Kvaal, A calibration method for handling the temporal drift of solid state gas sensors, Anal. Chim. Acta 407 (2000) 23–39, https://doi.org/ 10.1016/S0003-2670(99)00784-9.
[17] [18] [19] [20] [5] [14] [15] This work
EC – electrical conductivity; PLI – photoluminescence intensity; Δλ – wavelength shift of PL or reflectance band maximum; OA – optical absorbance; RH – relative humidity; DMF - dimethylformamide.
type of dual but only electrical oxygen sensor has been demonstrated with a combined resistive and thermoelectric response [16]. In our previous study, the Sm-doped TiO2 oxygen sensor had a very wide span ranging from less than 100 ppm to 100% of oxygen at normal pressure [39]. This is in contrast to the other type of room temperature oxygen sensors based on collisional luminescence quenching by dioxygen [47], where the range is more limited and has to be adjusted to trace or atmospheric levels by material selection. The price for a large concentration range is decreased sensitivity, leading to quite large errors in the case of a single output signal (as exemplified by EC signal in Fig. 10). Evidently, the multivariable sensing is very important for the TiO2 based gas sensors, allowing to achieve both the wide concentration span and reasonable accuracy.
5. Conclusions We have shown that three different signals from a single material, all responding to changes in the ambient oxygen concentration, can be simultaneously recorded and fused for significantly improved determination of oxygen concentration. These electrical and optical signals originated from UV-induced photoconductivity, and two photoluminescence bands of the nanopowders of Sm-doped anatase TiO2. An algorithm was introduced for signal fusion, which related the logarithm of oxygen concentration to the logarithms of signals. This relation was trained by ordinary least squares method using the sequence of oxygen concentrations changed pseudorandomly between 0.21% and 21%. It was shown that even when the simplest, linear relation between the logarithms was used, the multivariate approach with photoconductivity and internal photoluminescence signals can yield five times higher accuracy in predicting oxygen concentrations, as compared to univariate case. When photoconductivity was supplemented by the Sm3+ luminescence, the positive effect was somewhat smaller (accuracy was improved 2–3 times), which is probably caused by the mutual dependence of these two signals, due to a common governing factor in the underlying physical mechanism. The TiO2 nanomaterials with different crystal structures and morphologies are known to possess several additional intrinsic or extrinsic luminescence emissions over visible and NIR spectral ranges, providing further possibilities to develop promising multivariable sensor materials [6,48–50].
Acknowledgments This work was supported by institutional research funding (IUT3427) of the Estonian Ministry of Education and Research. The authors are grateful to Peeter Ritslaid for XRF measurements and Indrek Renge for constructive remarks. 7
Sensors & Actuators: B. Chemical 303 (2020) 127236
M. Eltermann, et al.
[28] S. Marco, A. Gutierrez-Galvez, Signal and data processing for machine olfaction and chemical sensing: a review, IEEE Sens. J. 12 (2012) 3189–3214. [29] X. Chen, S.S. Mao, Titanium dioxide nanomaterials: synthesis, properties, modifications, and applications, Chem. Rev. 107 (2007) 2891–2959, https://doi.org/10. 1021/cr0500535. [30] N. Rahimi, R.A. Pax, E.M. Gray, Review of functional titanium oxides. I: TiO2 and its modifications, Prog. Sol. State Chem. 44 (2016) 86–105, https://doi.org/10.1016/j. progsolidstchem.2016.07.002. [31] C. Garzella, E. Comini, E. Tempesti, C. Frigeri, G. Sberveglieri, TiO2 thin films by a novel sol–gel processing for gas sensor applications, Sens. Actuators B 68 (2000) 189–196, https://doi.org/10.1016/S0925-4005(00)00428-7. [32] R. Rella, J. Spadavecchia, M.G. Manera, S. Capone, A. Taurino, M. Martino, A.P. Caricato, T. Tunno, Acetone and ethanol solid-state gas sensors based on TiO2 nanoparticles thin film deposited by matrix assisted pulsed laser evaporation, Sens. Actuators B 127 (2007) 426–431, https://doi.org/10.1016/j.snb.2007.04.048. [33] M. Enachi, O. Lupan, T. Braniste, A. Sarua, L. Chow, Y.K. Mishra, D. Gedamu, R. Adelung, I. Tiginyanu, Integration of individual TiO2 nanotube on the chip: nanodevice for hydrogen sensing, Phys. Status Solidi RRL 9 (2015) 171–174, https://doi.org/10.1002/pssr.201409562. [34] N. Ababiia, M. Hoppe, S. Shree, A. Vahl, M. Ulfa, T. Pauporté, B. Viana, V. Cretu, N. Magariu, V. Postica, V. Sontea, M.-I. Terasa, O. Polonskyi, F. Faupel, R. Adelung, O. Lupan, Effect of noble metal functionalization and film thickness on sensingproperties of sprayed TiO2ultra-thin films, Sens. Actuators A 293 (2019) 242–258, https://doi.org/10.1016/j.sna.2019.04.017. [35] H. Tang, H. Berger, P.E. Schmid, F. Lévy, G. Burri, Photoluminescence in TiO2 anatase single crystals, Solid State Commun. 87 (1993) 847–850, https://doi.org/ 10.1016/0038-1098(93)90427-O. [36] Y. Lei, L.D. Zhang, G.W. Meng, G.H. Li, X.Y. Zhang, C.H. Liang, W. Chen, S.X. Wang, Preparation and photoluminescence of highly ordered TiO2 nanowire arrays, Appl. Phys. Lett. 78 (2001) 1125–1127, https://doi.org/10.1063/1.1350959. [37] T. Sekiya, M. Tasaki, K. Wakabayashi, S. Kurita, Relaxation process in anatase TiO2 single crystals with different colors, J. Lumin. 108 (2004) 69–73, https://doi.org/ 10.1016/j.jlumin.2004.01.013. [38] D. Palotti, E. Orabona, S. Amoruso, P. Maddalena, S. Lettieri, Modulation of mixedphase titania photoluminescence by oxygen adsorption, Appl. Phys. Lett. 105 (2014) 031903, https://doi.org/10.1063/1.4891038. [39] M. Eltermann, V. Kiisk, A. Berholts, L. Dolgov, S. Lange, K. Utt, R. Jaaniso, Modeling of luminescence-based oxygen sensing by redox-switched energy transfer in nanocrystalline TiO2:Sm3+, Sens. Actuators B Chem. 265 (2018) 556–564, https://doi. org/10.1016/j.snb.2018.03.034. [40] G.H. Dieke, Spectra and Energy Levels of Rare Earth Ions in Crystals, John Wiley, New York, 1968. [41] J. Shi, J. Chen, Z. Feng, T. Chen, Y. Lian, X. Wang, C. Li, Photoluminescence characteristics of TiO2 and their relationship to the photoassisted reaction of Water/ Methanol mixture, J. Phys. Chem. C 111 (2007) 693–699, https://doi.org/10.1021/ jp065744z. [42] D.K. Pallotti, L. Passoni, P. Maddalena, F. Di Fonzo, S. Lettieri, Photoluminescence
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
Mechanisms in Anatase and Rutile TiO2, J. Phys. Chem. C 121 (2017) 9011–9021, https://doi.org/10.1021/acs.jpcc.7b00321. J. Nelson, A.M. Eppler, I.M. Ballard, Photoconductivity and charge trapping in porous nanocrystalline titanium dioxide, J. Photochem. Photobiol. A 148 (2002) 25–31, https://doi.org/10.1016/S1010-6030(02)00035-7. N. Yamazoe, K. Shimanoe, Theory of power laws for semiconductor gas sensors, Sens. Actuators B 128 (2008) 566–573, https://doi.org/10.1016/j.snb.2007.07. 036. S. Lenaerts, J. Roggen, G. Maes, FT-IR characterization of tin dioxide gas sensor materials under working conditions, Spectrochim. Acta A 51 (1995) 883–894, https://doi.org/10.1016/0584-8539(94)01216-4. E. Konstantinova, J. Weidmann, Th. Dittrich, Influence of adsorbed water and oxygen on the photoluminescence and EPR of por-TiO2 (Anatase), J. Porous Mater. 7 (2000) 389–392, https://doi.org/10.1023/A:1009699324013. X. Wang, O.S. Wolfbeis, Optical methods for sensing and imaging oxygen: materials, spectroscopies and applications, Chem. Soc. Rev. 43 (2014) 3666–3761, https:// doi.org/10.1039/C4CS00039K. D.K. Pallotti, S. Amoruso, E. Orabona, P. Maddalena, S. Lettieri, Biparametric optical sensing of oxygen by titanium dioxide, Sens. Actuators B 221 (2015) 515–520, https://doi.org/10.1016/j.snb.2015.06.116. A. Chakraborty, G.H. Debnath, N.R. Saha, D. Chattopadhyay, D.H. Waldeck, P. Mukherjee, Identifying the correct host−Guest combination to sensitize trivalent lanthanide (Guest) luminescence: titanium dioxide nanoparticles as a model host system, J. Phys. Chem. C 120 (2016) 23870–23882, https://doi.org/10.1021/acs. jpcc.6b08421. V. Kiisk, K. Akulitš, M. Kodu, T. Avarmaa, H. Mändar, J. Kozlova, M. Eltermann, L. Puust, R. Jaaniso, Oxygen-sensitive photoluminescence of rare earth ions in TiO2 thin films, J. Phys. Chem. C 123 (2019) 17908–17914, https://doi.org/10.1021/ acs.jpcc.9b04588.
Marko Eltermann received his PhD in physics from the University of Tartu in 2019. His research is focused on the gas sensing properties of doped TiO2 nanomaterials. Valter Kiisk received his PhD in solid-state physics from the University of Tartu in 2006. His research activities mainly involve spectroscopic studies and applications of intrinsic and rare earth luminescence in various metal-oxide nanomaterials. Currently he is a senior research fellow at the University of Tartu, exploring rare earth activated oxide materials for luminescent gas sensing. Raivo Jaaniso received his PhD in solid-state physics from the Institute of Physics at the Estonian Academy of Sciences in 1988. His research interests have covered a wide area from laser spectroscopy and spectral hole burning to pulsed laser deposition of thin films and, most recently, development of luminescent, semiconductor, and graphene-based gas sensor materials. He is the head of the laboratory of sensor technologies at the University of Tartu.
8