The accurate measurement of pH in highly alkaline environments is critical for various industrial applications but remains a complex task. This paper discusses the development of novel Fe-doped SrCoO-based FET sensors for the detection of extreme alkaline pH levels. Through a comprehensive investigation of the effects of Fe doping on the structure, electrical properties, and sensing performance of SrCoO, we have identified the optimal doping level that significantly enhances the sensor's performance in highly alkaline conditions. With a Fe doping level of 5 mol %, the sensitivity of the sensor improves to 0.86 lg(Ω)/pH while maintaining the response rate. Further increasing the Fe doping to 10 mol % results in a sensor that demonstrates favorable response time, a suitable pH range, and a linear correlation between lg() and pH. The combination of X-ray photoelectron spectroscopy and X-ray diffraction analysis provides insight into the regulation mechanisms of Fe doping on the crystal structure, electronic structure, and oxygen vacancy concentration of SrCoO. Our findings indicate that Fe doping leads to an increase in oxygen vacancy concentration and a decrease in the energy barrier for oxygen ion migration, which contributes to the improved sensing performance of the Fe-doped SrCoO sensors. Additionally, the study highlights the influence of oxygen vacancy concentration on the electrical properties of SrCoO. Precise control over the concentration of oxygen vacancies is crucial for optimizing the sensitivity and response speed of SrCoO FET sensors under extreme alkalinity conditions.
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Biosensors (Basel)
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Department of Chemistry and Materials, Faculty of Textile Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda 386-8567, Nagano, Japan.
Nitrogen-based fertilizers are crucial in agriculture for maintaining soil health and increasing crop yields. Soil microorganisms transform nitrogen from fertilizers into NO3--N, which is absorbed by crops. However, some nitrogen is converted to nitrous oxide (NO), a greenhouse gas with a warming potential about 300-times greater than carbon dioxide (CO).
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January 2025
School of Electronics Science and Engineering/National Laboratory of Solid-State Microstructures, Nanjing University, Nanjing, China.
Ultrathin silicon nanowires (diameter <30 nm) with strong electrostatic control are ideal quasi-1D channel materials for high-performance field effect transistors, while a short channel is desirable to enhance driving current. Typically, the patterning of such delicate channels relies on high-precision lithography, which is not applicable for large area electronics. In this work, we demonstrate that ultrathin and short silicon nanowires channels can be created through a local-curvature-modulated catalytic growth, where a planar silicon nanowires is directed to jump over a crossing step.
View Article and Find Full Text PDFNano Converg
January 2025
Bendable Electronics and Sustainable Technologies (BEST) Group, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, 02115, USA.
The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
View Article and Find Full Text PDFAnal Chim Acta
January 2025
Department of Medical Biotechnology, College of Life Science and Biotechnology, Dongguk University, Seoul, 04620, Republic of Korea. Electronic address:
Background: Sarcopenia, which is associated with many pathways and molecular mechanisms, not only deteriorates the quality of life in old age but is also linked to various diseases. The ratio between cortisol and dehydroepiandrosterone sulfate (DHEAS) was utilized as a candidate method to diagnose sarcopenia. The hormones can fluctuate in concentration throughout the day, so monitoring the ratio between the two hormones is necessary.
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