Y and Al co-doped ZnO-nanopowder based ultrasensitive trace ethanol sensor: A potential breath analyzer for fatty liver disease and drunken driving detection

Mojumder, Subhajit; Das, Tanushri; Das, Suresh; Chakarborty, Nirman; Saha, Dibyajyoti; Pal, Mrinal

doi:10.1016/j.snb.2022.132611

Cited by 24 publications

(6 citation statements)

References 63 publications

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“…The peaks observed at ca. 2970, 2921, and 1375 cm −1 are due to C-H asymmetric stretching and bending vibrations of alkane groups [ 53 , 54 ]. These are ascribed to impurities present in the KBr powder.…”

Section: Resultsmentioning

confidence: 99%

Photocatalytic Performance of Undoped and Al-Doped ZnO Nanoparticles in the Degradation of Rhodamine B under UV-Visible Light:The Role of Defects and Morphology

Piras

Olla

Reekmans

et al. 2022

IJMS

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Quasi-spherical undoped ZnO and Al-doped ZnO nanoparticles with different aluminum content, ranging from 0.5 to 5 at% of Al with respect to Zn, were synthesized. These nanoparticles were evaluated as photocatalysts in the photodegradation of the Rhodamine B (RhB) dye aqueous solution under UV-visible light irradiation. The undoped ZnO nanopowder annealed at 400 °C resulted in the highest degradation efficiency of ca. 81% after 4 h under green light irradiation (525 nm), in the presence of 5 mg of catalyst. The samples were characterized using ICP-OES, PXRD, TEM, FT-IR, 27Al-MAS NMR, UV-Vis and steady-state PL. The effect of Al-doping on the phase structure, shape and particle size was also investigated. Additional information arose from the annealed nanomaterials under dynamic N2 at different temperatures (400 and 550 °C). The position of aluminum in the ZnO lattice was identified by means of 27Al-MAS NMR. FT-IR gave further information about the type of tetrahedral sites occupied by aluminum. Photoluminescence showed that the insertion of dopant increases the oxygen vacancies reducing the peroxide-like species responsible for photocatalysis. The annealing temperature helps increase the number of red-emitting centers up to 400 °C, while at 550 °C, the photocatalytic performance drops due to the aggregation tendency.

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Section: Resultsmentioning

confidence: 99%

Photocatalytic Performance of Undoped and Al-Doped ZnO Nanoparticles in the Degradation of Rhodamine B under UV-Visible Light:The Role of Defects and Morphology

Piras

Olla

Reekmans

et al. 2022

IJMS

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“…In the context of liver diseases, biosensors can be designed to detect liver function or damage biomarkers, such as liver enzymes, bilirubin, or specific proteins associated with certain liver conditions. Nanotechnology offers innovative ways to detect liver diseases at an early stage (Mojumder et al, 2022). Nanosensors can be engineered to target particular liver cells or biomarkers, providing sensitive and precise detection capabilities.…”

Section: Sensors Used To Detect Liver Diseasesmentioning

confidence: 99%

Efficient Hybrid CNN Method to Classify the Liver Diseases

Modhugu

2023

JOWUA

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This study focuses on classifying liver diseases using dynamic CT scan images and deep learning techniques. The primary objective is to develop accurate and efficient models for distinguishing between different liver disease categories. Three deep learning models, ResNet50, ResNet18, and AlexNet, are employed for three-class classification, including Hepatitis/cirrhosis, Hepatitis/Fatty liver, and Hepatitis/Wilson's Disease. The dataset comprises dynamic CT scan images of the liver, each manually segmented to identify lesions. To enhance model performance, the data is pre-processed by resizing, normalization, and data augmentation. The dataset is split into training, validation, and test sets for model evaluation. The performance of each model is assessed using confusion matrices, accuracy, sensitivity, and specificity. Results show varying accuracies for different liver disease classes, indicating the strengths and limitations of the models. To overcome the limits of the three-class classifiers, a framework for the Efficient Hybrid CNN method to classify Liver diseases (EHCNNLD) is proposed, combining the predictions from the three models with weighted probabilities. The Proposed EHCNNLD method demonstrates improved accuracy and classification power, enhancing the overall performance for liver disease classification. The study highlights the potential of deep learning techniques in medical image analysis and clinical diagnosis. The findings provide valuable insights into developing robust and accurate models for liver disease classification, paving the way for medical research and patient care advancements.

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“…Ethanol consumption by drivers is a major contributor to automotive accidents globally, with impairment and crash risk rising significantly at blood alcohol concentrations over 425 ppm [ 8 ]. Thus, detecting ethanol in a driver’s exhaled breath can help identify intoxication and reduce or prevent alcohol-related accidents [ 9 ]. Ethanol is also considered a biomarker and measurement of skin ethanol gas can be used to detect human volatile organic chemicals in blood [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode

Kim,

Choi,

Jung

et al. 2024

Sensors

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In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.2 to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which corresponds to a temperature of approximately 200 °C, produced a maximal response of 19.2 to 100 ppm ethanol. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor’s accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility, maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

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Y and Al co-doped ZnO-nanopowder based ultrasensitive trace ethanol sensor: A potential breath analyzer for fatty liver disease and drunken driving detection

Cited by 24 publications

References 63 publications

Photocatalytic Performance of Undoped and Al-Doped ZnO Nanoparticles in the Degradation of Rhodamine B under UV-Visible Light:The Role of Defects and Morphology

Photocatalytic Performance of Undoped and Al-Doped ZnO Nanoparticles in the Degradation of Rhodamine B under UV-Visible Light:The Role of Defects and Morphology

Efficient Hybrid CNN Method to Classify the Liver Diseases

CoFe2O4 on Mica Substrate as Flexible Ethanol Gas Sensor in Self-Heating Mode

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