Room temperature ZnO/NiO heterostructure sensing response: A breath biomarker sensor

Selvaraj, Bhuvaneswari; Rayappan, John Bosco Balaguru; Babu, K. Jayanth

doi:10.1016/j.jallcom.2022.165224

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…Overall, the developed readymade sensor prototype responds to the multicomponents and secured an accuracy of 98.36%. The fabricated homemade sensors are selective toward specific biomarkers as discussed in our previous work. , Hence, the accuracy of the prototype is significantly enhanced toward specific biomarkers after integrating homemade sensors.…”

Section: Results and Discussionmentioning

confidence: 90%

“…The fabricated homemade sensors are selective toward specific biomarkers as discussed in our previous work. 22,23 Hence, the accuracy of the prototype is significantly enhanced toward specific biomarkers after integrating homemade sensors.…”

Section: Discussionmentioning

confidence: 99%

“…Three different room temperature-operated sensors such as ZnO, Cr–ZnO, and ZnO/NiO have been fabricated for the specific detection of ammonia, ethanol, and acetone. These sensors were individually integrated with the aforementioned sensor array.…”

Section: Classification Analysis Of the Homemade Sensor Array Data Setmentioning

confidence: 99%

“…Moreover, integration and combination of homemade and commercial sensors along with data analysis have seldom been reported. Henceforth, the homemade onedimensional metal oxide sensing elements such as ZnO, Crdoped ZnO, and ZnO/NiO composite nanostructures, which were reported in our previous work, 22,23 have been integrated with the aforementioned sensor array, and the multiclass disease biomarker prediction and classification accuracy of the implemented model have been observed and reported.…”

Section: Introductionmentioning

confidence: 96%

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Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers

Selvaraj,

Rajasekar,

Balaguru Rayappan

2023

ACS Omega

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In recent days, the development of sensor-based medical devices has been found to be very effective for the prediction and analysis of the onset of diseases. The instigation of an electronic nose (eNose) device is profound and very useful in diverse applications. The analysis of exhaled breath biomarkers using eNose sensors has attained wider attention among researchers, and the prediction of multiple disease variants using the same is still an open research problem. In this work, an enhanced XGBooster classifier-based prediction mechanism was introduced to identify the disease variants based on the responses of commercially available metal oxide-based Figaro (Japan) sensors including TGS826, TGS822, TGS2600, and TGS2602. The implemented model secured 98.36% prediction accuracy in multiclass disease prediction and classification. The homemade one-dimensional metal oxide sensing elements such as ZnO, Cr-doped ZnO, and ZnO/NiO were integrated with the aforementioned sensor array for the specific detection of the three biomarkers of interest. This model has attained a classification accuracy of 99.77, 94.91, and 96.56% toward ammonia, ethanol, and acetone, respectively. And the multiclass disease biomarker classification accuracy of the readymade and homemade eNose prototype models was compared, and the results are summarized.

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Section: Results and Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Classification Analysis Of the Homemade Sensor Array Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers

Selvaraj,

Rajasekar,

Balaguru Rayappan

2023

ACS Omega

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“…Multi-junction interconnected structure [194] Acetone ( The enhancement of gas sensitivity of metal oxides by noble metals is attributed to their catalytic properties. Noble metals such as gold, platinum, silver, and palladium have high catalytic activity due to their ability to adsorb and dissociate gas molecules on their surface.…”

Section: = Cuomentioning

confidence: 99%

Growth Processing and Strategies: A Way to Improve the Gas Sensing Performance of Nickel Oxide-Based Devices

Ben Arbia,

Comini

2024

Chemosensors

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The review paper provides a comprehensive analysis of nickel oxide (NiO) as an emerging material in environmental monitoring by surveying recent developments primarily within the last three years and reports the growth processing and strategies employed to enhance NiO sensing performance. It covers synthesis methods for pristine NiO, including vapor-phase, liquid-phase, and solution-processing techniques, highlighting advantages and limitations. The growth mechanisms of NiO nanostructures are explored, with a focus on the most recent research studies. Additionally, different strategies to improve the gas sensing performance of NiO are discussed (i.e., surface functionalization by metallic nanoparticles, heterostructure formation, carbon-based nanomaterials, and conducting polymers). The influence of these strategies on selectivity, sensitivity, response time, and stability of NiO-based sensors is thoroughly examined. Finally, the challenges and future directions that may lead to the successful development of highly efficient NiO-based gas sensors for environmental monitoring are introduced in this review.

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A Strategy to Enhance Humidity Robustness of p‐Type CuO Sensors for Breath Acetone Quantification

Oosthuizen

Weber

2023

Small Science

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Low‐cost metal oxide sensors are highly attractive for emerging applications such as breath analysis. Particularly promising are p‐type sensors that can operate at low temperatures, a key requirement for compact and low‐power devices. To date, however, these sensors lack sufficient sensitivity, selectivity, and humidity robustness to fulfil stringent requirements faced in real applications. Herein, a flame‐made and low‐power sensor (operated at 150 °C) that consists of CeO2‐decorated CuO nanoparticles is introduced, as determined by X‐ray diffraction and X‐ray photoelectron spectroscopy analysis. Most remarkably, this sensor features excellent robustness to 10–90% relative humidity. This is attributed to the presence of CeO2 nanoclusters, which may act by scavenging OH− and allow the readsorption of oxygen onto the CuO surface. To demonstrate its immediate impact, this sensor is investigated for the detection of acetone, a biomarker for fat burning. It detects acetone with high sensitivity (i.e., 50 ppb) and features excellent acetone selectivity (>9.8) toward key inorganic interferants (i.e., NH3, H2, and CO). Most importantly, the CeO2–CuO sensor accurately quantifies acetone concentrations in the exhaled breath of 16 volunteers (bias and precision of 90 and 457 ppb). As a result, it is attractive for low‐power and humidity robust detection of volatiles in breath analysis.

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Room temperature ZnO/NiO heterostructure sensing response: A breath biomarker sensor

Cited by 16 publications

References 40 publications

Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers

Machine Learning Approaches: Detecting the Disease Variants in Human-Exhaled Breath Biomarkers

Growth Processing and Strategies: A Way to Improve the Gas Sensing Performance of Nickel Oxide-Based Devices

A Strategy to Enhance Humidity Robustness of p‐Type CuO Sensors for Breath Acetone Quantification

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