Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures
Abstract
:1. Introduction
Gas Sensor Characteristics
- Response (sensitivity) refers to the modification in resistance or current when exposed to specific gas molecules. Various techniques can be employed to measure the resulting change in sensor resistance. The response is characterized by the ratio of the sensor’s resistance when in the presence of air to its resistance when exposed to target gas molecules (i.e., ). This response definition is predominantly applied when the target gas has reducing properties. Conversely, when the gas exhibits oxidative traits, the response is defined as . In addition to the simple ratio, the relative response can be quantified better, to reflect changes in sensor behavior under varying conditions. For reducing gases, the relative response is calculated as , which measures the percentage decrease in resistance due to gas exposure. For oxidizing gases, it is determined by , representing the percentage increase in resistance [8].
- Recovery time is the duration a gas sensor takes to return to its baseline resistance or current after the removal of the target gas. This parameter indicates how quickly a sensor can be ready for the next detection cycle [19].
- Response time is the amount of time taken by a gas sensor when the concentration of a gas reaches a certain value, with respect to the time that it takes to compel the sensor to generate an alarm signal [20].
- Stability is the characteristic that tells whether the given sensing material can reconcile to its initial state once the detection is over. This includes retaining the response time, recovery time, sensitivity, and selectivity [20]. This can be quantified by measuring the sensor’s response over time and ensuring it remains consistent. This can involve calculating the standard deviation () of the response over time and ensuring it stays within acceptable limits. A common expression for stability is [22]
- The limit of detection (LOD) refers to the smallest concentration of a gas that can be reliably detected by the sensor. It represents the lowest amount of analyte that produces a signal distinguishable from the noise or baseline of the sensor system. The LOD is a crucial parameter for evaluating the performance of a gas sensor, as it determines the sensor’s sensitivity and its ability to detect trace amounts of gases. The LOD is typically expressed in units, such as parts per million (ppm), parts per billion (ppb), or even parts per trillion (ppt), depending on the sensitivity of the sensor and the application requirements. A lower LOD indicates a more sensitive sensor, capable of detecting smaller concentrations of the target gas. The LOD can be mathematically represented as [23]
2. Optically Assisted Gas Sensing
2.1. Optically Activated Gas Sensors Based on Graphene
2.2. Optically Activated Gas Sensors Based on Organic Materials
2.3. Optically Activated Gas Sensors Based on Metal Oxides
2.4. Optically Activated Gas Sensors Based on TMDCs
3. Conclusions and Outlook
3.1. Conclusions
3.2. Future Outlook
- Enhanced sensing performance: There is a need to further improve the sensitivity, selectivity, and response times of transition metal dichalcogenide (TMDC)-based optically activated gas sensors. Achieving this may involve innovative material synthesis methods and optimizing device structures. For instance, the optical properties of TMDCs can be harnessed for gas sensing, as interactions between gas molecules and the TMDC surface induce measurable changes in the material’s optical characteristics, such as photoluminescence (PL) intensity, Raman shifts, or absorption spectra. Although research on cavity-enhanced light–matter interactions in TMDC monolayers, particularly with strongly bound excitons for optically assisted gas sensing, is limited, integrating TMDCs with plasmonic cavities offers significant advantages [136,137]. This integration enhances the electromagnetic field near the TMDC surface, resulting in a stronger PL response when gas molecules interact with the TMDC layer. Consequently, plasmonic cavities can amplify even subtle changes in PL intensity, enabling the detection of low concentrations of gas molecules with greater precision. Hybrid TMDC–plasmonic cavity sensors are highly effective for environmental monitoring, particularly for detecting and quantifying trace gases like nitrogen dioxide () and sulfur dioxide () in the atmosphere [7]. The enhanced sensitivity and selectivity of these sensors result from leveraging the strong light–matter interactions provided by the plasmonic cavities, which amplify the TMDCs’ response to specific gas molecules. Furthermore, dielectric metasurfaces with a high Q-factor are highly desirable for enhancing the performance of sensing [138]. Hybridizing these high Q-factor dielectric metasurfaces with ultrathin layers of TMDCs can further increase sensitivity to minute environmental changes, making for real-time sensing. Despite notable progress having been made in sensitivity enhancement and detection limit refinement, achieving superior selectivity remains a significant challenge. Subsequently, future endeavours should concentrate on crafting selective sensing materials and investigating advanced signal-processing techniques, to distinguish target gases from interfering elements effectively.
- Integration with IoT and smart systems: Integrating TMDC-based optically activated gas sensors into Internet of Things (IoT) platforms and smart systems facilitates real-time gas concentration monitoring and data analysis. This integration enables remote surveillance of environmental pollutants and dangerous gases, empowering timely responses to potential hazards. Future research should emphasize scalable fabrication methods and energy-efficient designs, to enable the widespread deployment of smart sensing networks, bolstering environmental and public-health-monitoring abilities.
- Multi-gas detection: There is a need to develop novel optically activated gas sensors capable of simultaneously detecting multiple gases. This multi-gas detection capability enhances sensor versatility and utility across various applications. In addition to detecting different gases, it is important to separate their individual concentrations. In this respect, artificial intelligence could be used to train sensors to distinguish different gases and measure their concentrations.
- Miniaturization and wearable devices: Shrinking the size of TMDC-based optically activated gas sensors allows integration into wearable devices for personal exposure monitoring and health tracking. These compact, portable sensors furnish individuals with real-time insights into their immediate gas surroundings.
- Environmental and industrial applications: TMDC-based optically activated gas sensors offer substantial promise for environmental monitoring and industrial safety. Optically activated gas sensors based on graphene and metal oxides have shown significant promise in environmental monitoring [12], particularly in detection of disease-related volatile organic compounds (VOCs) by means of nanomaterial-based sensors. A notable example is the use of these sensors in smart city projects, where continuous monitoring of air quality is very important [13]. Future research should involve field deployment, to assess sensor performance in diverse conditions, validating reliability and durability. Additionally, efforts towards reducing the operating temperatures of metal oxides-based gas sensors, enhancing response/recovery times, and integrating energy-efficient sensing mechanisms (such as photoactivation) could further expand their applicability in diverse environmental and industrial settings.
- Multifunctional hybrid materials: Exploring the integration of multiple 2D nanomaterials and hybrid structures could yield multifunctional gas sensors with improved performance and adaptability. Incorporating novel materials like metal–organic frameworks (MOFs), Mxene, and quantum dots may facilitate tailored sensing platforms for a wide array of gas analytes. The integration of organic materials with inorganic components, particularly in the form of hybrid sensors, is likely to play a crucial role in overcoming current challenges, such as stability, surface area limitations, and sensitivity at room temperature.
- Flexible and wearable sensors: Responding to the burgeoning demand for wearable and flexible electronics, the development of lightweight, bendable gas sensors based on 2D nanomaterials presents promising opportunities in environmental monitoring, healthcare, and personal safety. Future research should prioritize optimizing sensor design and fabrication processes, to bolster durability, sensitivity, and user comfort.
- Improved detection capability: Organic materials, such as polymers and metal–organic frameworks (MOFs), offer advantages, such as tunable chemical functionalities, high surface areas, and biocompatibility. In the future, these materials are expected to continue evolving towards enhanced stability, selectivity for specific gases, and integration into flexible and wearable sensor platforms. Applications could expand into areas requiring sensitive detection of volatile organic compounds (VOCs), bioanalytes, and environmental pollutants with improved sensing capabilities and reliability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
2D | two-dimensional |
AACVD | aerosol-assisted chemical vapor deposition |
Ag NPs | silver nanoparticles |
APTES | (3-aminopropyl)triethoxysilane |
BP | black phosphorus |
CdO | cadmium oxide |
O | formaldehyde |
methane | |
CO | carbon monoxide |
carbon dioxide | |
CoPc | Cobalt Phthalocyanines |
Co(tpfpp)ClO₄ | Cobalt(II) 5,10,15,20-tetrakis(pentafluorophenyl)porphyrin perchlorate |
CP | conducting polymers |
cesium lead bromide | |
CuPc | copper phthalocyanines |
copper tungstate | |
CVD | chemical vapor deposition |
EDA | ethylenediamine |
F-P | Fabry–Perot |
GNF | graphite nanofiber |
GO | graphene oxide |
GQDs | graphene quantum dots |
hydrogen | |
S | hydrogen sulfide |
HCHO | formaldehyde |
HNs | hollow nanospheres |
indium oxide | |
InAG | with Aminonaphthalene-1-sulfonic acid (ANS) and reduced graphene oxide |
LED | light-emitting diode |
LOD | limit of detection |
MBs | microballs |
molybdenum disulfide | |
Molybdenum diselenide | |
molybdenum ditelluride | |
MPs | metalloporphyrins |
MPcs | metallophthalocyanines |
MOSs | metal oxide semiconductors |
MOs | metal oxides |
MWIR | mid-wave infrared |
nitrogen | |
O | nitrous oxide |
ammonia | |
NiO | nickel oxide |
nitrogen dioxide | |
oxygen | |
ozone | |
PANI | polyaniline |
PA | polyacetylene |
Pd | palladium |
PdO | palladium oxide |
PDMS | polydimethylsiloxanes |
POM | polyoxometalate |
PPy | polypyrrole |
ppm | parts per million |
ppb | parts per billion |
PTh | polythiophene |
QDs | quantum dots |
RF | radio frequency |
rGO | reduced graphene oxide |
RT | room temperature |
S- | sulfur-doped tin oxide |
SMF | single-mode fiber |
tin(IV) oxide | |
sulfur dioxide | |
SPR | surface plasmon resonance |
TEA | trimethylamine |
titanium dioxide | |
TMDC | transition metal dichalcogenide |
UV | ultraviolet |
tungsten trioxide | |
tungsten disulfide | |
XPS | X-ray photoelectron spectroscopy |
ZnO | zinc oxide |
ZIF-8 | zeolitic imidazole framework material |
g- | graphitic carbon nitride |
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Active Material | Active Layer | Gas | Conc. (ppm) | Response | LOD (ppm) | Res./Recovery Time(s) | Ref |
---|---|---|---|---|---|---|---|
Graphene | Graphene/ | 10 | 0.2 | 0.7/0.9 | [38] | ||
Graphene | - | - | - | [33] | |||
Graphene | - | - | - | [33] | |||
Graphene | - | - | - | [33] | |||
Pd-doped rGO + ZnO- | 100 | 0.047 | 4/8 | [39] | |||
Multilayer graphene | 1 | 0.03 | 330/- | [23] | |||
rGO/cpoPcCo | 100 | 3.7 | 120 | [43] | |||
TMDC | / heterostructures | 0.5 | 0.05 | 9/8 | [124] | ||
P-type | 1 | 300/160 | [126] | ||||
Single-layer | 5 | 0.068 | 76/109 | [121] | |||
Nanosheets | 10 | 0.008 | 175/241 | [123] | |||
/ZnO nanohybrid | 100 | 130/110 | [125] | ||||
+ MXene | 100 | 0.2 | 3/2.4 | [129] | |||
Nanoflakes | 3 | 0.19 | 9/3 | [130] | |||
Organic | ZIF-8 | - | - | 9 | [50] | ||
ZIF-8 | Acetone | 62 | - | 6.67 | 1/5 | [51] | |
PR-MOF-1 | EDA | 0.001 | - | 2 | [53] | ||
PANI/GNF | 0.125 | - | 0.04 | 381/406 | [58] | ||
Metal oxide | 5 | 12,200% c | 428 | [73] | |||
ZnO | OH | 1000 | 10 | 6/94 | [79] | ||
0.8 | b | 0.1 | 14/67 | [85] | |||
S | 1 | 26,268.5 b | 0.001 | 48 | [86] | ||
ZnO/ | HCHO | 10 | b | 5 | 14/16 | [101] | |
Au-/ZnO | 5 | a | 0.05 | 119/377 | [10] | ||
/ | Triethylamine | 100 | b | 200/500 | [102] | ||
/ | HCHO | 1 | b | 0.06 | 28/50 | [104] | |
/ZnO | Toluene | 100 | b | 0.1 | 213/1709 | [106] | |
PANI/NiO-loaded | acetone | 50 | b | 0.176 | 150/290 | [105] | |
CuPc/ZnO | 80 | b | 0.8 | 20/10 | [107] | ||
ZnO/ | 5 | a | 0.041 | 63/40 | [108] | ||
/S- | 5 | a | 170/64 | [111] | |||
/ANS/rGO nanohybrids | HCHO | 0.5 | b | 0.005 | 19/179 | [112] | |
/g--Au | 1 | a | 0.020 | - | [113] | ||
PdO/ | 500 | c | 2.03 | 77/470 | [114] | ||
N719-sensitized POM/ films | 1 | b | 0.05 | 48/66 | [115] | ||
hierarchical porous / | 1 | a | 0.05 | 93/28 | [116] |
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Odebowale, A.A.; Abdulghani, A.; Berhe, A.M.; Somaweera, D.; Akter, S.; Abdo, S.; As’ham, K.; Saadabad, R.M.; Tran, T.T.; Bishop, D.P.; et al. Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures. Nanomaterials 2024, 14, 1521. https://doi.org/10.3390/nano14181521
Odebowale AA, Abdulghani A, Berhe AM, Somaweera D, Akter S, Abdo S, As’ham K, Saadabad RM, Tran TT, Bishop DP, et al. Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures. Nanomaterials. 2024; 14(18):1521. https://doi.org/10.3390/nano14181521
Chicago/Turabian StyleOdebowale, Ambali Alade, Amer Abdulghani, Andergachew Mekonnen Berhe, Dinelka Somaweera, Sanjida Akter, Salah Abdo, Khalil As’ham, Reza Masoudian Saadabad, Toan T. Tran, David P. Bishop, and et al. 2024. "Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures" Nanomaterials 14, no. 18: 1521. https://doi.org/10.3390/nano14181521
APA StyleOdebowale, A. A., Abdulghani, A., Berhe, A. M., Somaweera, D., Akter, S., Abdo, S., As’ham, K., Saadabad, R. M., Tran, T. T., Bishop, D. P., Solntsev, A. S., Miroshnichenko, A. E., & Hattori, H. T. (2024). Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures. Nanomaterials, 14(18), 1521. https://doi.org/10.3390/nano14181521