Patent Application
20250224386
The present invention relates to gas sensors and particularly to sensors capable of sensitive and selective detection of gases.
Gas sensing is a rapidly developing technology field due to its vast application potential in the monitoring of air polluting levels (indoors, outdoors, or in industrial applications), food freshness, healthcare diagnostics (via breath analysis), etc. At present, gas sensing technology is dominated by metal oxide semiconductor (MOS) sensors, which often lack selectivity and cannot efficiently distinguish between gaseous components, in particular between volatile organic compounds (VOCs). Hence, a novel miniaturized sensor technology capable of distinguishing a VOC of interest from a complex background is needed to reach the full potential in the above-mentioned applications.
An emerging class of gas sensors are adsorption-based sensors, which employ a microporous sensing layer made of metal-organic frameworks (MOFs) or zeolites. Herein, MOFs will be addressed in more detail, but the concepts can be equally applied to other microporous or nanoporous materials. MOFs are a class of crystalline solids that consist of inorganic nodes connected by organic linkers [Kitagawa et al. (2004) Angew. Chem. Int. Ed. 43, 2334-2375]. Uniform nanopores (typically 0.5-2 nm) and walls of only a single molecule thick form through self-assembly of the MOF crystal lattice and give rise to record internal surface areas (up to >6000 m2g−1) [Farha et al. (2012) J. Am. Chem. Soc. 134, 15016-15021]. Because of their chemical and structural features, MOFs can capture analytes, such as VOCs, even at trace concentrations, with partition coefficients orders of magnitude higher than established materials (Leidinger et al. (2016) Sensors and Actuators B: Chemical 236, 988-996). So far, MOF-based sensing has been exclusively based on the equilibrium sensor response and, therefore, relied only on thermodynamic adsorption parameters to achieve selectivity (i.e., differences between equilibrium uptake isotherms).
US20180195990 discloses a gas sensor comprising a gas-sensing material including a metal-organic framework with fcu topology (RE-fcu-MOF), wherein the ligand of the RE-fcu-MOF is one or more of fumaric acid and 1,4-napthalene dicarboxylic acid. The sensing signal transduction was based on measuring the capacitance of a sensor in interdigitated electrode configuration. Sensors showed higher sensitivity to H2S and NH3 vapours when compared to NO2, CH4, H2 and C7H8.
Campbell et al. (2015) J. Am. Chem. Soc. 137, 13780-13783, demonstrated a chemiresistive sensor array from conductive 2D metal-organic frameworks. The array consisted of three separate devices, each employing a different MOF as a sensing layer. The combined response of the sensor array was treated using principal component analysis, which yielded classification of the data by functional group class (alkanes, alcohols, ketones, amines, aromatics and aliphatics).
Alternative to sensor arrays, a multivariable approach was used to improve the selectivity of a single sensor element [Potyrailo (2016) Chem. Rev. 116, 11877-11923]. In contrast to traditional single-output sensors, a single multivariable sensor generates two or more (at least partially) independent outputs. The non-correlated information in these outputs improves the selectivity compared to single-output sensors and can enable quantification of the individual VOCs in a mixture by using standard methods used in chemometrics (e.g., principal component analysis). Moreover, since the multivariable sensor makes use of a single sensing material, the drift of the outputs (if any) is correlated and can be more easily compensated for. The multivariable sensors based on MOFs have been up to now only reported for optical transduction, for example, measuring the wavelength-dependent adsorption of MOF [Potyrailo, cited above].
Described in this invention are multivariable gas sensors that rely on the discrimination of analytes based on their diffusion kinetics. The diffusion constant in microporous materials can vary over orders of magnitude for different analytes since their diffusion is controlled by steric hindrance and specific host-analyte interactions. Therefore, even molecules with similar uptake properties can often be discriminated based on diffusion kinetics. For example, methanol and ethanol diffuse dramatically faster in ZIF-8 (a microporous MOF material) compared to 1-butanol, leading to an ideal kinetic selectivity of up to 106, while the thermodynamic selectivity is modest at comparable concentrations. Similarly, while the uptakes of butanol and benzene are similar, the diffusion of benzene is >104 times slower. In present invention “kinetic selectivity” is defined as the ratio of the diffusivities of the adsorbates, and “thermodynamic selectivity” as the ratio of their equilibrium uptake.
MOF refers to a class of crystalline solids that consist of inorganic nodes connected by organic linkers.
The methods of the present invention can be used for example in the monitoring of air polluting levels (indoors, outdoors, or in industrial applications), food freshness, healthcare diagnostics (via breath analysis).
Throughout the specification nanopores or nanoporous are used as synonyms for micropores or microporous.
The invention is further summarised in the following statements:
1. A gas sensor for detecting one or a plurality of volatile compounds in a gas, the sensor comprising:
“Volatile compound” refers to a compound which, at room temperature, occur partially or completely, in gas phase. Examples thereof include water, alkanes, ketones, alcohols, aldehydes, and aromatic compounds.
Herein water is typically a background component that hampers measurements of other volatile components.
2. The gas sensor according to statement 1, wherein the layer comprising nanopores has a thickness of less than 50 μm, or of less than 10 μm, or less than 5 μm, for example between 50 to 300 nm.
3. The gas sensor according to statement 1 or 2, wherein the nanopores in the layer comprising nanopores have an average diameter of below 10 nm, of below 10 nm, or of below 2 nm.
4. The gas sensor according to any one of statements 1 to 3, wherein the layer comprising nanopores is a zeolite or a porous carbon.
5. The gas sensor according to any one of statements 1 to 3, wherein the layer comprising nanopores is a MOF (Metal-Organic Framework).
6. The gas sensor according to any one of statements 1 to 5, wherein the heating element and layer comprising nanopores are separated by a heat conductive material.
7. The gas sensor according to statement 6, wherein the heat conductive material has a heat conductivity of at least 0.3 W/mK.
8. The gas sensor according to statement 6 or 7, wherein the heat conductive material is a non-electrically conductive material such as silicon nitride, silicon oxide, silicon carbide or a ceramic.
9. The gas sensor according to any one of statements 1 to 8, wherein the heating element is ohmic heater, such as a micro hotplate.
10. The gas sensor according to any one of statements 1 to 9, wherein the signal transducer is an electronic, capacitive, optical or gravimetric signal transducer.
11. The gas sensor according to statement 10, wherein the capacitive signal transducer comprises a bottom heat conductive layer, and a top gas permeable conductive layer, and the layer comprising nanopores is positioned between said bottom layer and said top layer.
12. The gas sensor according to statement 10, wherein the optical signal transducer a bottom reflective or semi-reflective layer, a top reflective or semi-reflective gas permeable layer and the layer comprising nanopores is positioned between said bottom and said top layer.
13. The gas sensor according to statement 10, comprising a gravimetric signal transducer wherein the layer comprising nanopores is positioned, and in contact with, on one or more mechanical resonators of which the resonant frequency or amplitude can be monitored.
14. The gas sensor according to statement 13, wherein the gravimetric transducer operates in a static or resonant mode.
15. The gas sensor according to statement 13 or 14, wherein the gravimetric transducer is a cantilever or a coupled resonator.
16. The gas sensor according to any one of statements 1 or 15, wherein the layer comprising the nanopores is in direct contact with the transducer.
17. The gas sensor according to any one of statements 1 or 15, wherein the layer comprising the nanopores and the transducer are spatially separated.
18. The gas sensor according to statement 17, wherein the transducer is a metal oxide semiconductor sensor.
19. The sensor according to any one of statements 1 to 18, wherein the gas sensor comprises a plurality of layers comprising nanopores, wherein the material of the layers have different affinities for a volatile compound and/or different diffusion properties for a volatile compounds, and wherein each of layers is part of an individual signal transducer.
20. The sensor according to statement 19, wherein each of the plurality of layers comprising nanopores can be subjected to a separate temperature regime.
21. The sensor according to statement 19 or 20, wherein each of the plurality of layers comprising nanopores differs in thickness.
22. A method for determining the presence and/or quantity of a plurality of volatile compounds in a gas comprising the steps of:
23. The method according to statement 22, wherein is step b) the temperature of the layer comprising nanopores is increased, thereby releasing adsorbed compounds from the nanopores.
24. The method according to statement 22 or 23, determining in step d) the presence and concentration of water in the gas.
25. The method according to any one of Statements 22 to 24, wherein the temperature of the layer comprising nanopores is perturbated in a periodic manner.
26. The method according to any one of Statements 22 to 25, wherein the adsorption or release of compounds is monitored on multiple layers comprising 30 nanopores.
27. The method according to any one of statements 22 to 26, wherein the gas introduced in step a) contains up to 50% (v/v) up to 75% (v/v) or up to 100% (v/v) water vapor.
28. The method according to any one of statements 22 to 27, wherein the gas introduced in step a) is outside ambient air or air within a building.
29. The method according to any one of statements 22 to 28, wherein the gas introduced in step a) is an exhaled animal or human breath.
30. The method according to any one of statements 22 to 29, wherein the method determines the presence and/or concentration of one or more 1-propanol, 1-butanol, acetone pentane and hexane in a gas.
31. The method according to any one of statements 22 to 30, wherein the method determines the presence and/or concentration of one or more 1-propanol, 1-butanol, acetone pentane and hexane in a gas comprising water vapor.
A. Open-top implementation of the separated element concept. Dark elements (1): heater with nanoporous material coated on top. Lighter elements (2): transduction element. Top: top view. Bottom: cross-sectional side view.
B-C. Semi-enclosed implementations of the separated element concept. Dark elements (1): heater with nanoporous material coated on top. Lighter elements (2): transduction element. Top: top view. Bottom: cross-sectional side view.
D Sampled measurement method. The adsorbent temperature jumps from T1 to T0 (T0<T1), and molecules will start to diffuse into the adsorbent (since the adsorbed quantity n0>n1). Before equilibrium at T0 is established, the temperature jumps back to T1 after a well-defined dwell time t1, and the molecules that could diffuse in are desorbed. The desorbed fraction is quantified downstream using a mass spectrometer. By systematically varying the time t, the amount adsorbed vs. time curve can be constructed and fitted with a suitable diffusion model to extract the diffusivity at T0.
The invention presented herein devises a method to selectively detect analytes and measure their concentration by gas sensors or sensor arrays that display kinetic selectivity.
The features disclosed in the description, or in the claims, or in the accompanying drawings, expressed in their specific forms or in terms of a means for performing the disclosed function, or a method or process for obtaining the disclosed results, as appropriate, may, separately, or in any combination of such features, be utilised for realising the invention in diverse forms thereof.
While the invention has been described in conjunction with the exemplary embodiments described above, many equivalent modifications and variations will be apparent to those skilled in the art when given this disclosure. Accordingly, the exemplary embodiments of the invention set forth above are considered to be illustrative and not limiting. Various changes to the described embodiments may be made without departing from the spirit and scope of the invention.
For the avoidance of any doubt, any theoretical explanations provided herein are provided for the purposes of improving the understanding of a reader. The inventors do not wish to be bound by any of these theoretical explanations.
Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.
Throughout this specification, including the claims which follow, unless the context requires otherwise, the word “comprise” and “include”, and variations such as “comprises”, “comprising”, and “including” will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.
It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by the use of the antecedent “about,” it will be understood that the particular value forms another embodiment. The term “about” in relation to a numerical value is optional and means for example +/−10%.
The measurement of diffusion requires out-of-equilibrium conditions for adsorption. The adsorption equilibrium can be modulated by changing the analyte concentration, gas pressure or sensor temperature. The analyte concentration and gas pressure are difficult to modulate in real-world applications, but the temperature modulation is easily achieved by introducing a heating element to the sensor. Therefore, the diffusion discriminating sensors comprise a heating element that provides a rapid temperature perturbation to the sensing element that measures the amount of adsorbed gas (
Ideally, the microporous layer has a geometrical form that ensures fast diffusion time constants and a single-exponential diffusion event for a single analyte. Possible geometrical forms are thin film and particles with narrow particle size distribution. A particularly suitable form is a thin film. For a simple signal interpretation, the sensor signal is linearly proportional to the number of adsorbed molecules. All examples here assume geometrical form of a thin film and linear relation between signal and amount of adsorbed molecules. In such a case, the diffusion of a single analyte produces an exponential decay function (Equation 1).
where S is the sensor signal, D is the diffusion constant, l is the film thickness and t is time.
In the first example, the heating element provides a step-like temperature perturbation from temperature T1 to T2 (
In the second example, the heating element provides a periodic temperature perturbation, for example, in the form of a sine wave (
A sensor transduction mechanism translates the amount of analyte adsorbed to measurable signal. Possible transduction mechanisms include the ones with electrical, optical or gravimetrical readout. Especially useful are electrical and optical readouts, since they can be easily integrated with a heater element. A possible transduction mechanism for adsorption sensors with electrical readout is the measurement of capacitance (or complex impedance), since most of the microporous materials are dielectrics. Upon the adsorption of analyte molecules in the microporous material, its effective dielectric constant increases proportionally to the amount adsorbed and the dielectric constant of the adsorbed phase. Similarly, the refractive index increases upon adsorption of analytes, which is exploited in sensors with optical readout. Here, the capacitive sensors are described in more detail, although same principles apply also to sensors with other transduction mechanisms.
Suitable capacitor architectures are the metal-insulator-metal (MIM) configuration (
The MIM configuration consists of a microporous sensing layer sandwiched between two electrodes. The capacitance of the MIM senor is determined by the area of electrode overlap, the thickness of the sensing layer and dielectric constant of the sensing layer. The bottom electrode is deposited on the substrate and is covered by a microporous sensing layer. The top electrode is deposited on the top of a sensing layer and is gas permeable. In some embodiments, the top electrode is in the form of a thin layer made of noble metal (Au, Pt, or Ag) with thickness between 7-100 nm. In the MIM configuration, the top electrode can, in some cases, introduce an additional resistance for the diffusing gas molecules, which increases the apparent diffusion time constants. The diffusion resistance of the electrode scales with the denser morphology of the electrode (or absence of pinholes in the electrode) and, consequently, with its thickness. In some embodiments, the electrode thickness is used to shift the diffusion time constants of fast-diffusing analytes into the operating range of a sensor by increasing the diffusion path length through the microporous material.
The IDE configuration consists of in-plane electrodes in the form of interdigitated fingers separated by a certain distance. The sensing layer is deposited on top of the electrodes, or the electrodes are deposited on top of the sensing layer. Due to the in-plane configuration of the electrodes, IDE sensors can accommodate sensing layers with a variety of morphologies, including rough layers with pinholes and particle coatings. The diffusion in the sensing layer is not obstructed by the top electrode, which can benefit the sensing of analytes with slow diffusion. However, IDE sensors have inferior sensitivity compared to MIM sensors due to the large stray contribution from the underlying substrate and surrounding atmosphere to the measured capacitance.
The heater provides the thermal perturbation to the sensing element, which results in out-of-equilibrium conditions required for detection of diffusion. The heater needs to provide a thermal input (either a pulse, step change, or a periodic temperature variation) that is fast relative to the time constant of diffusion of the fastest gas molecule to be detected. When the frequency of a periodic temperature variation is swept over a certain range, at least the fastest frequency in the range should fulfil this criterion. The thermal time constant of a whole sensor determines the lower detection limit for diffusivity and depends on the heater configuration, thermal properties, and thicknesses of layers composing the sensor.
In some embodiments, the heater is deposited on the backside of a thin substrate (
Ultra-low thermal time constants can be realized by using a suspended thin membrane (
The limit of detection at long times (low frequency) is determined by the maximum measurement time acceptable in sensing applications. Ideally, the measurement time should not exceed a few tens of seconds, for example, 10 seconds. The sensor bandwidth is determined by the range between the low and high time limits of the sensor. The typical bandwidth of diffusion discriminating sensors is between 103 to 107, with larger values preferred.
According to Equation 1, the intrinsic temporal response of the sensor depends on the diffusion constant of the VOC and the thickness of the nanoporous layer. The thickness of the nanoporous layer can be adjusted accordingly to the sensing application and may be, for example, less than 5 μm, suitably about 50 nm to 300 nm thick.
Implementation Variant with Separated Elements & Sampled Measurement
The heater with nanoporous material on top and the transduction element do not have to be stacked on top of each other. Instead, both elements can be spatially separated inside of an open-top or semi-enclosed sensor package (
The diffusive transport of analyte molecules in the microporous material is largely controlled by steric interactions between the host and the analyte. Hence, diffusion kinetics are strongly dependent on the diameter of the diffusing molecule relative to the micropore size. Depending on the analyte molecule size, their diffusion constant can span over several orders of magnitude.
The discrimination power of diffusion-based sensors is tested by examining the desorption profiles of binary mixtures of analytes with a factor of 10 (
The proof of the concept is demonstrated using a MIM capacitance sensor that employs the prototypical microporous material, ZIF-8, as a sensing material. The diffusion constant of various analytes in ZIF-8 is given in Table 1. Typical interfering compounds in the ambient atmosphere, i.e., nitrogen, oxygen, carbon dioxide, and water, have diffusion constants smaller than 2×10−11 m2s−1. The analytes that can be considered as biomarkers in a human body, e.g., ethanol and acetone, have diffusion constants about 100 and 4000 times smaller than the background compounds, respectively. 1-hexane and trichloromethane, which are analytes of interest when monitoring the indoor air quality, have diffusion constants more than 100 000 times smaller than the background. These large differences in diffusion kinetics between background compounds and analytes of interest enable selective detection of analytes even at low concentrations of the latter.
In addition, several MOFs show a large difference in the equilibrium uptake of certain analytes, which can benefit the discrimination power of a sensor. The sensitivity of the sensor with the ZIF-8 layer in the MIM configuration toward different analytes is given in Table 2. ZIF-8 capacitive sensors show higher sensitivity toward 1-butanol and 1-hexane, which benefits the detection limit of these analytes. On the other hand, the sensor sensitivity toward water is small, which is an advantage since water is frequently an interfering compound.
The data to support the above were collected using a capacitance sensor in the MIM configuration and a commercial hotplate setup that can reach heating rates of up to 150 K min-1. The sensor consists of a 260 nm thick ZIF-8 layer with a 20 nm thick silver top electrode. Capacitance measurements were performed at the frequency of 100 kHz. In pure nitrogen, the sensor shows the weak temperature dependence of capacitance with ΔC/C0=2×10−4 per degree Celsius, which is due to the intrinsic temperature dependence of dielectric properties of ZIF-8. The change in capacitance upon applying a temperature step (as described in
In the first example, the behaviour of diffusion signal at different concentrations of 1-butanol was tested (
In the second example, the temperature step amplitude (T2−T1) was varied in the range between 6° C.-36° C. (
In the third example, the temperature step with the amplitude of 16° C. was applied in the presence of a single analyte (either methanol, water, ethanol, 1-propanol, 1-butanol, or hexane), and the sensor signal (capacitance) was monitored over time (
In the fourth example, a temperature step with an amplitude of 16° C. was applied in the presence of a binary mixture of methanol (p/p0=8% at 24° C.) and 1-butanol (p/p0=0.5% at 24° C.). The temporal response of a sensor signal shows two desorption events occurring at the characteristic times (
In the fifth example, a desorption response of a sensor with an extended measurement bandwidth from 10−5 to 102 s is calculated for a ternary mixture of analytes. This scenario mimics a sensor fabricated on a suspended thin membrane with an ultra-low thermal time constant. The assumed mixture consisted of 40% of relative humidity, 200 ppm of acetone, and 10 ppm of 1-hexane. The sensor response is calculated using the sensor response values for single components given in Table 1 and Table 2. No mutual interactions between adsorbed analytes were assumed, which is reasonable assumption due to high dilution of analytes. Due to large differences in the diffusion constant of each analyte, three diffusion events are clearly observed in the calculated temporal response of a sensor. Each event can be fitted separately using a single exponential decay function to obtain the diffusion time constant and diffusion signal amplitude. Using this approach, the mixture composition can be perfectly reconstructed, i.e., the concentration and diffusion constant of individual analyte is measured within 5% error.
Another important feature of diffusion discriminating sensors is that the diffusion constants of analytes are intrinsic to a microporous material. This should simplify the transfer of calibration models between sensors, given that the microporous layer morphology and thickness are constant.
Additional measurements on single components and their mixtures were performed to evaluate the selectivity of the diffusion discriminating sensor in parts-per-million VOC concentration range. A classification of VOCs is possible by plotting the diffusion constant (giving the selectivity) vs the amplitude of the desorption curve (giving the concentration).
| Number | Date | Country | Kind |
|---|---|---|---|
| 22020147.9 | Apr 2022 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2023/058622 | 4/3/2023 | WO |
