Mechanistic Insights into WO3 Sensing and Related Perspectives
Abstract
:1. Introduction
2. Sensing Capabilities of WO3
2.1. NO2
2.2. Acetone
2.3. Ammonia and Related Gases
3. WO3 Structures and Surfaces
3.1. Structural Complexity of WO3 and Relevant Hints for the Field of Gas-Sensing
3.2. Oxygen Vacancies in WO3
3.2.1. Experimental Findings: Surface Oxygen Vacancies
Electrical Properties
Spectroscopic Studies
3.2.2. First Principles Modeling of Oxygen Vacancies in WO3
- Oxygen vacancies (both in bulk and on the surface) in WO3 are ubiquitous and favorably formed during the heat-treatment. Re-healing is not a favorable process, with WO3 tending to remain oxygen-defective;
- There is theoretical evidence that oxygen vacancies are anisotropic, with the formation energy depending on the particular crystallographic axis. However, a distribution of all the kinds of vacancies can be expected. The vacancies confer electrical conductivity to WO3. The most energetically favorable surface of monoclinic WO3 is the (001).
3.2.3. Surface Oxygen Vacancies in WO3 and Adsorption Properties
Experimental Studies
First Principles Modeling of Adsorption onto WO3 Surfaces
- Experimentally, the surface of monoclinic WO3 contains strongly acidic W sites; the reduction of the surface strongly favors adsorption of methanol and water. Methanol dissociation takes place on reduced surfaces, which is not the case with water.
- Theoretically speaking, water adsorption is generally calculated to be more favorable than dissociation. However, water desorption resulting from hydrogen dissociation results in surface reduction. We can take this result as an indication that, reciprocally, water dissociation on reduced WO3 surfaces may result in the healing of oxygen vacancies. While apparently unfavorable in the DFT scenario, water dissociation cannot be neglected from experimental studies where an electrical bias is also applied, such as in the case of chemoresistive sensors.
4. Mechanistic Studies of WO3 Sensor Properties
5. Conclusions
- The complex crystallographic phase diagram of WO3 must be taken into account when determining the phase involved in the sensing activity. At room temperature, the as-prepared samples may be constituted by a mixture of γ-monoclinic and δ-triclinic phases, whose speciation can be facilitated by combining XRD and Raman spectroscopy. The investigation of the sensing behavior cannot disregard that at typical operating temperatures, such as 300 °C, the active phase may be the orthogonal one. Therefore, determining the phase composition of the as-prepared samples can be insufficient for studying sensing properties.
- The oxygen vacancies dominate the physical properties of WO3. From first principles modeling, it appears that both bulk and surface vacancies share some features: the formation energy and the electronic effect of the vacancies depend on the crystallographic direction along which they are formed. However, an increasingly accepted view is that at least some of the vacancy levels merge with the Fermi level, explaining the electrical conductivity of oxygen-deficient WO3, in agreement with experimental findings. From an experimental point of view, XPS analysis is extremely useful in distinguishing the various W oxidation states connected to the presence of oxygen vacancies. However, such a technique must be used carefully. For instance, the fitting of the W4f region cannot exclude a W5p3/2 component. Analysis of the valence band spectra may provide useful information about the presence of oxygen vacancies.
- Experimental studies into the sensing mechanisms of WO3 have radically modified the common view of chemoresistive sensing. In the case of CO sensing, the direct generation of oxygen vacancies onto the WO3 surface has been solidly supported. In the case of NO2, the healing of surface vacancies with NO by-product has been backed up by considerable evidence. These conclusions deeply question the usual hypothesis regarding the charge depletion layer by oxygen adsorption and its subsequent modulation by the gas analyte. This is a general indication to carefully check for the applicability of widespread models.
- This final conclusion is of paramount importance: if oxygen vacancies display such a dominant role and they are present in every oxide, even though to different extents and with different concentrations and structures, the achievability of selectivity by chemoresisistive oxide sensors is deeply questionable.
- 5.
- If direct extraction of oxygen from the WO3 surface by CO (Section 4) was a surprising result, even direct vacancy healing by NO2 remarkably differs from the widespread mechanism based on NO2 adsorption/desorption. It seems that such a mechanism still has to be reinforced by suitable measurements of the sensor exhaust during the NO2 tests.
- 6.
- In turn, such mechanistic conclusions indicate the need for a deep revision of the currently accepted models. This can only be done by intensifying the operando investigation of the sensor operation and the effective identification of the evolved species.
- 7.
- DFT modeling has been demonstrated as a powerful tool for verifying hypotheses about the surfaces and structures of WO3. The most commonly considered surface is the (001) plane in the γ-monoclinic phase, which is the most favorable and the most immediately formed upon cleaving single crystals. However, in a polycrystalline nanopowder, several other exposed facets are commonly present. In this sense, there are not many studies of the energy ordering [131] of the exposed crystal planes and of the oxygen vacancy formation energy. This topic needs to be investigated in detail to complete our knowledge of the surface properties of WO3. Other crystallographic phases, such as the orthorhombic phase, should also be considered.
- 8.
- Moreover, still there are very few DFT studies directly related to WO3 sensing. It would be helpful to obtain a catalogue of the most favorable adsorption sites for several gaseous analytes of interest. This is more and more desirable given the remarkably improved computing capabilities currently available.
- 9.
- If oxygen vacancies are vitally important in establishing the sensing behavior of WO3, then any improvement of WO3 sensors must be based on understanding and controlling such vacancies. “Understanding” means that a suitable catalogue of characterization techniques must emerge as a standard toolbox for establishing the vacancy concentration, topology and electronic structure. Therefore, XPS should ideally be complemented by other techniques, such as electron paramagnetic resonance, cathodoluminescence, etc. [152].
- 10.
- “Controlling” the vacancies means that the synthesis should be tailored toward the achievement of a given distribution of oxygen vacancies. Currently, treatments in reduced atmospheres are employed for preparing oxygen-deficient WO3. However, such structures are naturally amenable to being reoxidized during the sensor operation. Another strategy could be the designed introduction of dopants into the structure of WO3. For instance, let A2O3 be the oxide of a trivalent cation. The incorporation equation for substitutional defects for such an oxide into WO3 is, in Kröger–Vink notation:
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sample Morphology | Phase Composition | Gas Concentration | Response | Operating Temperature (°C) | Reference |
---|---|---|---|---|---|
Sintered powder | n/d | 80 ppm | 97 | 300 | [32] |
Lamellae | Monoclinic | 500 ppb | >150 | 200 | [34] |
Nanoplates | Monoclinic | 5 ppm | 960 | 200 | [35] |
2D nanosheets | Monoclinic | 50 ppb | ~6 | 140 | [36] |
Porous nanosheets | Monoclinic | 10 ppm | >450 | 100 | [37] |
Yolk–shell spheres | Monoclinic | 50 ppb | ~100 | 100 | [38] |
Nanosheets | Monoclinic | 100 ppb | ~50 | 75 | [39] |
Nanoplatelets | Monoclinic | 1 ppm | ~80 | 200 | [40] |
Nanotubes | Monoclinic | 5 ppm | 7 | 300 | [41] |
Nanoflowers | Monoclinic | 50 ppb | >30 | 100 | [43] |
Nanocolumns | Monoclinic | 10 ppm | 22 | 110 | [42] |
Nanosheets | Monoclinic | 40 ppb | 30 | 150 | [44] |
Nanobricks | Monoclinic | 100 ppm | 12 | 300 | [45] |
Nanosheets | Monoclinic | 4 ppm | >32 | 300 | [46] |
Nanoflowers | Monoclinic | 100 ppm | >50 | 100 | [47] |
Nanosheets | Triclinic | 300 ppb | 18.8 | 100 | [48] |
Nanofibers | Monoclinic | 50 ppm | >104 | 150 | [49] |
Sample Morphology | Phase Composition | Gas Concentration (ppm) | Response | Operating Temperature (°C) | Reference |
---|---|---|---|---|---|
Hollow spheres | Monoclinic | 50 | 3.5 | 400 | [53] |
Thick films | Triclinic | 50 | 4.56 | 300 | [54] |
Nanoplates | Triclinic | 2 | 5 | 300 | [55] |
Nanoparticles | Monoclinic | 10 | 10 | 350 | [56] |
Nanoplates | Monoclinic | 300 | 50 | 307 | [57] |
Nanoplates | Monoclinic | 500 | 40 | 200 | [58] |
Flower-like | Monoclinic | 100 | 7 | 300 | [59] |
Nanotubes | Monoclinic | 100 | 42.5 | 250 | [60] |
Urchin-like | Monoclinic | 25 | 15 | 300 | [61] |
Nanoplates | Monoclinic | 100 | 8 | 300 | [62] |
Nanosheets | Monoclinic | 100 | 50 | 340 | [63] |
Nanosheets | Triclinic | 1 | 2.04 | 230 | [64] |
Mesoporous nanofibers | Monoclinic | 50 | 22 | 300 | [65] |
Nanosheets | Monoclinic | 50 | 15 | 300 | [66] |
Urchin-like | Monoclinic | 100 | 30 | 200 | [67] |
Sample Morphology | Phase Composition | Gas Concentration (ppm) | Response | Operating Temperature (°C) | Reference |
---|---|---|---|---|---|
AMMONIA | |||||
Sintered powders | Probably monoclinic | 50 | <5 | 200–600 | [68] |
Nanopowders | Monoclinic + triclinic | 500 | <5 | 350 | [69] |
Nanopowders | Monoclinic + triclinic | 50 | <6 | 250 | [71] |
Nanowires | Monoclinic | 1500 | 9.7 | 250 | [72] |
Nanofibers | Orthorhombic | 100 | <5 | 300 | [73] |
Nanosheets | Hexagonal | 100 | 35 | 350 | [75] |
Nanoplates | Monoclinic | 100 | <20 | 300 | [76] |
ALKYLAMINES (TEA/TMA) | |||||
Hollow spheres | Orthorhombic | 5 | 56.9 | 450 | [77] |
Nanosheets assembled in microspheres | Monoclinic | 50 | 16 | 220 | [78] |
Hierarchical spheres | Monoclinic | 10 | 35.3 | 150 | [79] |
Nanorods | Monoclinic | 50 | 50 | 250 | [80] |
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Epifani, M. Mechanistic Insights into WO3 Sensing and Related Perspectives. Sensors 2022, 22, 2247. https://doi.org/10.3390/s22062247
Epifani M. Mechanistic Insights into WO3 Sensing and Related Perspectives. Sensors. 2022; 22(6):2247. https://doi.org/10.3390/s22062247
Chicago/Turabian StyleEpifani, Mauro. 2022. "Mechanistic Insights into WO3 Sensing and Related Perspectives" Sensors 22, no. 6: 2247. https://doi.org/10.3390/s22062247
APA StyleEpifani, M. (2022). Mechanistic Insights into WO3 Sensing and Related Perspectives. Sensors, 22(6), 2247. https://doi.org/10.3390/s22062247