In this work, we proposed a rapid and easy check of the drinking water pollution level due to bacteria growth by semiconductor gas sensor. Highly sensitive vertical channel organic ammonia gas sensor was used to detect the gases emitted from the polluted water, and then determined effective ammonia concentration according to its response. Residues from meat of fish, shrimp, and fruits were mashed and added to the clean water. The water samples were stored at 35 °C for natural decay. Initially the bacteria concentration was below 100 colony forming unit per ml (cfu/ml), then it increased to10 cfu/ml in 2 h and 10 cfu/ml in 4 h, which was beyond the drinking safety standard, 500 cfu/ml. At this gas level no bad odor can be sensed by human yet, however, the effective ammonia concentration of those samples rises to 300-500 ppb in 2 h. The amine gas sensor can therefore be used as a rapid check if the bacteria level inside the water is far over the safety standard.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.aca.2022.339729 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hierarchical Self-Assembly of SnO Nanoparticles into Porous Microspheres: Exceptionally Selective Ammonia Sensing at Ambient.
ACS Appl Mater Interfaces
January 2025
Nanomaterials Laboratory, Department of Polymers and Functional Materials, CSIR-Indian Institute of Chemical Technology, Uppal Road, Tarnaka, Hyderabad 500 007, India.
Herein, porous SnO microspheres in a three-dimensional (3D) hierarchical architecture were successfully synthesized via a facile hydrothermal route utilizing d-(+)-glucose and cetyltrimethylammonium bromide (CTAB), which act as reducing and structure-directing agents, respectively. Controlled adjustment of the CTAB to glucose mole ratio, reaction temperature, reaction time, and the calcination parameters all provided important clues toward optimizing the final morphologies of SnO with exceptional structural stability and reasonable monodispersity. Electron microscopy analysis revealed that microspheres formed were hierarchical self-assemblies of numerous primary SnO nanoparticles of ∼3-8 nm that coalesce together to form nearly monodispersed and ordered spherical structures of sizes in the range of 230-250 nm and are appreciably porous.
View Article and Find Full Text PDFAccelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.
ACS Sens
January 2025
College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low cost targeting four typical GHGs (CO, CH, NO, SF). This strategy introduces machine learning (ML) methods based on density functional theory (DFT) to achieve accurate and rapid screening from a large number of candidate gas sensor materials.
View Article and Find Full Text PDFOnline monitoring of propofol concentrations in exhaled breath.
Heliyon
December 2024
Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Propofol, a widely used intravenous anesthetic agent, requires accurate monitoring to ensure therapeutic efficacy and prevent oversedation. Recent developments in modern analytical instrumentation have led to significant breakthroughs in on-line analysis of exhaled breath. This review discusses several sophisticated analytical methods that have been explored for noninvasive, real-time monitoring of propofol concentrations, including proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, and gas chromatography coupled to surface acoustic wave sensors.
View Article and Find Full Text PDFKGPM: A Knowledge-Guided Predictive Model for Virtual Metering in Gas Wells.
ACS Omega
December 2024
China University of Petroleum-Beijing, Changping, Beijing 102249, China.
One of the key points in the construction of smart oil and gas fields is the effective utilization of data. Virtual Flow Metering (VFM), as one of the representative research directions for digital transformation, can obtain real-time production from oil and gas wells without the need for additional field instrumentation, utilizing pressure and temperature data obtained from sensors and employing multiphase flow mechanism models. The data-driven VFM demonstrates a commendable capacity in capturing the nonlinear relationship between sensor data and flow rates, while circumventing the necessity for rigorous analysis of the underlying mechanistic processes.
View Article and Find Full Text PDFHigh-Aspect-Ratio InGaO Integrated with Amorphous AlO Nanofibers: All-Inorganic Self-Supporting Wearable Membranes for Ultralow-Concentration NO Sensing in Simulated Exhalation.
Nano Lett
January 2025
School of Optical and Electronic Information, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Achieving high flexibility, breathability, and sensitivity in inorganic semiconductor gas sensors remains a substantial challenge, especially for wearable applications in high-humidity environments. This study develops a hyper-flexible, thermally stable, and highly breathable full-inorganic, self-supporting InGaO-AlO/AlO nanofiber membrane sensor, fabricated using a dual-spinneret electrospinning method with an interlocking design. This innovative sensor has a bilayer structure with an amorphous AlO nanofiber substrate layer supporting an active layer of high-aspect-ratio interwoven InGaO and AlO nanofibers, providing outstanding flexibility, elevated breathability, and strong thermal stability.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!