Gases in Food Production and Monitoring: Recent Advances in Target Chemiresistive Gas Sensors

Shaalan, Nagih M.; Saber, Osama; Kumar, Shalendra

doi:10.3390/chemosensors10080338

Cited by 31 publications

(22 citation statements)

References 107 publications

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“…(2) LeNet This model consisted of a combined stack of convolution and max pooling layers. The structure of LeNet was as follows: {Input (1,75,14), convolution (1,64,3), max pool (2, 2), convolution (64,128,3), max pool (2, 2), linear (1024, 512), linear (512, Class)} [28].…”

Section: Multi-task Learning Vs Single-task Learningmentioning

confidence: 99%

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Huang,

Wang,

Chen

et al. 2023

Sensors

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With the development of gas sensor arrays and computational technology, machine olfactory systems have been widely used in environmental monitoring, medical diagnosis, and other fields. The reliable and stable operation of gas sensing systems depends heavily on the accuracy of the sensors outputs. Therefore, the realization of accurate gas sensor array fault diagnosis is essential to monitor the working status of sensor arrays and ensure the normal operation of the whole system. The existing methods extract features from a single dimension and require the separate training of models for multiple diagnosis tasks, which limits diagnostic accuracy and efficiency. To address these limitations, for this study, a novel fault diagnosis network based on multi-dimensional feature fusion, an attention mechanism, and multi-task learning, MAM-Net, was developed and applied to gas sensor arrays. First, feature fusion models were applied to extract deep and comprehensive features from the original data in multiple dimensions. A residual network equipped with convolutional block attention modules and a Bi-LSTM network were designed for two-dimensional and one-dimensional signals to capture spatial and temporal features simultaneously. Subsequently, a concatenation layer was constructed using feature stitching to integrate the fault details of different dimensions and avoid ignoring useful information. Finally, a multi-task learning module was designed for the parallel learning of the sensor fault diagnosis to effectively improve the diagnosis capability. The experimental results derived from using the proposed framework on gas sensor datasets across different amounts of data, balanced and unbalanced datasets, and different experimental settings show that the proposed framework outperforms the other available methods and demonstrates good recognition accuracy and robustness.

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Section: Multi-task Learning Vs Single-task Learningmentioning

confidence: 99%

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Huang,

Wang,

Chen

et al. 2023

Sensors

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“…In recent decades, the significance of gas sensing technologies has been strongly recognized as they have become essential in various fields such as air quality monitoring, hazard detection, medical diagnostics, food processing, and agriculture . The escalating demands from these diverse sectors have driven advancements in sensor technologies, highlighting the need for more sophisticated, reliable, and versatile gas detection methods.…”

Section: Introductionmentioning

confidence: 99%

Advancing Metal–Organic Framework Designs for Room-Temperature Chemiresistive Gas Sensors

Kim,

Pander,

Moon

2024

ACS Appl. Electron. Mater.

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Metal−organic frameworks (MOFs) represent a versatile class of porous reticular materials whose structures and properties can be precisely tailored for targeted applications. A prominent example of such an application is the chemiresistive sensing of gases, where MOFs exhibit robust responses even at room temperature, thereby demonstrating their potential for practical utilization. This feature article reviews the development of the design criteria that are crucial for the optimization of MOFs as room-temperature chemiresistors. To highlight key advancements in this field, we categorize these materials into three distinct groups to (1) 2D conductive MOFs, (2) composites of MOFs with other materials, and (3) MOF-on-MOF architectures. The representative achievements from each group are discussed to demonstrate research progress in the construction of these innovative materials and their performance in gas sensing. Special emphasis is placed on the MOF-on-MOF materials, underscoring their role as pioneering smart materials for targeted application.

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“…Monitoring of NH 3 emissions from food would allow early degradation of food to be detected and establish safe consumption. As a result, the development of sensors for NH 3 detection with high sensitivity, selectivity, speed of response, and low detection limit at room temperature (RT) is crucial [ 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Vanadium pentoxide (V 2 O 5 ) is an n-type semiconductor with a bandgap between 2.04 and 2.8 eV. It shows high stability, variable oxidation states, high specific capacitance, high energy density, resistivity, and excellent electrical properties [ 15 , 16 ]. Different morphologies of V 2 O 5 such as nanoparticles, nanowires, thin films, nanospheres, nanorods, nanofibers, and others, have been tested as sensors for many gases such as hydrogen (H 2 ), ethanol (C 2 H 5 OH), NO x , and ammonia.…”

Section: Introductionmentioning

confidence: 99%

Room Temperature Ammonia Gas Sensor Based on p-Type-like V2O5 Nanosheets towards Food Spoilage Monitoring

Duy

Nguyet

et al. 2022

Nanomaterials

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Gas sensors play an important role in many areas of human life, including the monitoring of production processes, occupational safety, food quality assessment, and air pollution monitoring. Therefore, the need for gas sensors to monitor hazardous gases, such as ammonia, at low operating temperatures has become increasingly important in many fields. Sensitivity, selectivity, low cost, and ease of production are crucial characteristics for creating a capillary network of sensors for the protection of the environment and human health. However, developing gas sensors that are not only efficient but also small and inexpensive and therefore integrable into everyday life is a difficult challenge. In this paper, we report on a resistive sensor for ammonia detection based on thin V2O5 nanosheets operating at room temperature. The small thickness and porosity of the V2O5 nanosheets give the sensors good performance for sensing ammonia at room temperature (RT), with a relative change of resistance of 9.4% to 5 ppm ammonia (NH3) and an estimated detection limit of 0.4 ppm. The sensor is selective with respect to the seven interferents tested; it is repeatable and stable over the long term (four months). Although V2O5 is generally an n-type semiconductor, in this case the nanosheets show a p-type semiconductor behavior, and thus a possible sensing mechanism is proposed. The device’s performance, along with its size, low cost, and low power consumption, makes it a good candidate for monitoring freshness and spoilage along the food supply chain.

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Gases in Food Production and Monitoring: Recent Advances in Target Chemiresistive Gas Sensors

Cited by 31 publications

References 107 publications

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Advancing Metal–Organic Framework Designs for Room-Temperature Chemiresistive Gas Sensors

Room Temperature Ammonia Gas Sensor Based on p-Type-like V2O5 Nanosheets towards Food Spoilage Monitoring

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