Design of an Artificial Opal/Photonic Crystal Interface for Alcohol Intoxication Assessment: Capillary Condensation in Pores and Photonic Materials Work Together

Zaytsev, Valeriy; Ermatov, Timur; Fedorov, Fedor S.; Balabin, Nikita; Kapralov, P. O.; Bondareva, Julia; Ignatyeva, Daria O.; Khlebtsov, Boris N.; Kosolobov, S. S.; Belotelov, V. I.; Nasibulin, Albert G.; Gorin, Dmitry A.

doi:10.1021/acs.analchem.2c00573

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…The chemiresistive gas sensors based on nanostructured metal oxide semiconductors (MOSs) have the advantages of low manufacturing cost, small size, and long life, making them easy to integrate into portable breath detection systems. Recently, there have been some work on the detection of ethanol in breath by gas sensors based on MOSs. , However, high humidity in the breath affects the performance and detection accuracy of the sensors.…”

Section: Introductionmentioning

confidence: 99%

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Hollow-Out Fe₂O₃-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

Zhou

et al. 2023

ACS Appl. Mater. Interfaces

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The analysis of exhaled breath has opened up new exciting avenues in medical diagnostics, sleep monitoring, and drunk driving detection. Nevertheless, the detection accuracy is greatly affected due to high humidity in the exhaled breath. Here, we propose a regulation method to solve the problem of humidity adaptability in the ethanol-monitoring process by building a heterojunction and hollow-out nanostructure. Therefore, large specific surface area hollow-out Fe2O3-loaded NiO heterojunction nanorods assembled by porous ultrathin nanosheets were prepared by a well-tailored interface reaction. The excellent response (51.2 toward 10 ppm ethanol at 80% relative humidity) and selectivity to ethanol under high relative humidity with a lower operating temperature (150 °C) were obtained, and the detection limit was as low as 0.5 ppb with excellent long-term stability. The superior gas-sensing performance was attributed to the high surface activity of the heterojunction and hollow-out nanostructure. More importantly, GC–MS, diffuse reflectance Fourier transform infrared spectroscopy, and DFT were utilized to analyze the mechanisms of heterojunction sensitization, ethanol-sensing reaction, and high-humidity adaptability. Our integrated low-power MEMS Internet of Things (IoT) system based on Fe2O3@NiO successfully demonstrates the functional verification of ethanol detection in human exhalation, and the integrated voice alarm and IoT positioning functions are expected to solve the problem of real-time monitoring and rapid initial screening of drunk driving. Overall, this novel method plays a vital role in areas such as control of material morphology and composition, breath analysis, gas-sensing mechanism research, and artificial olfaction.

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Section: Introductionmentioning

confidence: 99%

“…Recently, there have been some work on the detection of ethanol in breath by gas sensors based on MOSs. 10,11 However, high humidity in the breath affects the performance and detection accuracy of the sensors.…”

Section: ■ Introductionmentioning

confidence: 99%

Hollow-Out Fe₂O₃-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

Zhou

et al. 2023

ACS Appl. Mater. Interfaces

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“…With the development of sensor technology, gas sensor arrays are playing an increasingly important role in machine olfaction applications such as environmental monitoring [ 1 ], gas quality detection [ 2 ], food quality control [ 3 ], and medical diagnosis [ 4 ]. A machine olfactory system (e.g., an electronic nose) identifies gases by analyzing the data returned from a gas sensor array using pattern recognition methods.…”

Section: Introductionmentioning

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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“…Only on the basis of fully studying students' learning motivation and needs can the course be adjusted and optimized more pertinently. Therefore, we try to make a new discussion on the curriculum design of Russian audio-visual speaking in the third year of undergraduate course based on the relevant theories of motivation psychology [3][4].…”

Section: Introductionmentioning

confidence: 99%

Design and Research on Virtual Simulation Experiment Teaching of Russian Audio-visual and Speaking Course

Feng,

2023

Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8–10, 20

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Due to students' insufficient emphasis on Russian audiovisual and oral courses, coupled with factors such as lack of attractiveness in course content, insufficient class hours, and a lack of diversity in teaching methods, students face certain difficulties in their Russian listening and speaking abilities. To address this issue, this article conducts in-depth research on the course based on theories related to motivational psychology and needs theory, clarifying the relationship between students' learning motivation and Russian audiovisual and oral courses. At the same time, based on personal learning and teaching experience, as well as questionnaire survey results, the author elucidates the guiding significance of motivational psychology related theories in Russian audiovisual and oral teaching. The research results can effectively guide practical teaching practices, and motivational psychology provides relevant theoretical basis and training methods for Russian audiovisual and oral teaching. This also helps to improve the listening, speaking, and communication abilities of Russian learners, enabling them to smoothly pass the Professional Level 8 exam on the basis of fluent and accurate expression of their thoughts.

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Design of an Artificial Opal/Photonic Crystal Interface for Alcohol Intoxication Assessment: Capillary Condensation in Pores and Photonic Materials Work Together

Cited by 5 publications

References 53 publications

Hollow-Out Fe₂O₃-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

Hollow-Out Fe₂O₃-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

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

Design and Research on Virtual Simulation Experiment Teaching of Russian Audio-visual and Speaking Course

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Design of an Artificial Opal/Photonic Crystal Interface for Alcohol Intoxication Assessment: Capillary Condensation in Pores and Photonic Materials Work Together

Cited by 5 publications

References 53 publications

Hollow-Out Fe2O3-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

Hollow-Out Fe2O3-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

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

Design and Research on Virtual Simulation Experiment Teaching of Russian Audio-visual and Speaking Course

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Hollow-Out Fe₂O₃-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity

Hollow-Out Fe₂O₃-Loaded NiO Heterojunction Nanorods Enable Real-Time Exhaled Ethanol Monitoring under High Humidity