An Intelligent Optical Dissolved Oxygen Measurement Method Based on a Fluorescent Quenching Mechanism

Oxygen Measurement in Microdevices

Oxygen plays a fundamental role in respiration and metabolism, and quantifying oxygen levels is essential in many environmental, industrial, and research settings. Microdevices facilitate the study of dynamic, oxygen-dependent effects in real time. This review is organized around the key needs for oxygen measurement in microdevices, including integrability into microfabricated systems; sensor dynamic range and sensitivity; spatially resolved measurements to map oxygen over two- or three-dimensional regions of interest; and compatibility with multimodal and multianalyte measurements. After a brief overview of biological readouts of oxygen, followed by oxygen sensor types that have been implemented in microscale devices and sensing mechanisms, this review presents select recent applications in organs-on-chip in vitro models and new sensor capabilities enabling oxygen microscopy, bioprocess manufacturing, and pharmaceutical industries. With the advancement of multiplexed, interconnected sensors and instruments and integration with industry workflows, intelligent microdevice-sensor systems including oxygen sensors will have further impact in environmental science, manufacturing, and medicine.

Dissolved oxygen sensor based on the fluorescence quenching method with optimal modulation frequency

Measurement of dissolved oxygen (DO) in liquid samples is of vital importance in both industrial and biomedical fields. In this paper, a DO sensor based on the fluorescence quenching method has been built. The measurement principle is based on fluorescence lifetime detection, which is indicated by the phase difference between an excitation light signal and a fluorescence signal. The nonlinear effect of the fluorescent material has been taken into consideration to obtain a more accurate fitting model. The performance of the system varying with the modulation frequency of excitation light signals is also reported. Modulation frequency mainly affects the sensitivity and phase resolution ratio of the system. The system at the optimized modulation frequency has a good degree of fitting with R² value of 0.9981 and a small relative error of 0.79%. The study shows that this kind of sensor with optimal modulation frequency has good performance, which can be used in many important fields.

Development of a Photometric Method to Measure Molecular Oxygen in Water

A photometric method to determine molecular oxygen in water was developed. When manganese(II) is oxidized by oxygen under alkaline conditions, the presence of polyphosphate can prevent precipitation due to a coacervate reaction. The oxidized manganese later dissolves in acid to form a pink Mn(III) species, which has a stable UV/vis spectrum. Monitoring of the oxygen concentration based on the absorbance of the pink Mn(III) species at 517 nm showed a strong correlation with both the Winkler method and an optical sensor. As a result, the present method can measure not only dissolved oxygen, but also fine bubbles oxygen in in the water sample with high reliability (0 - 26 mg dm^-3, r² = 0.9995). During this process, no significant interference from nitrite or metal ions was observed. The accuracy of the measurement was steady at high temperatures of the water samples (≤ 363 K).

Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference

A well-known approach to the optical measure of oxygen is based on the quenching of luminescence by molecular oxygen. The main challenge for this measuring method is the determination of an accurate mathematical model for the sensor response. The reason is the dependence of the sensor signal from multiple parameters (like oxygen concentration and temperature), which are cross interfering in a sensor-specific way. The common solution is to measure the different parameters separately, for example, with different sensors. Then, an approximate model is developed where these effects are parametrized ad hoc. In this work, we describe a new approach for the development of a learning sensor with parallel inference that overcomes all these difficulties. With this approach we show how to generate automatically and autonomously a very large dataset of measurements and how to use it for the training of the proposed neural-network-based signal processing. Furthermore, we demonstrate how the sensor exploits the cross-sensitivity of multiple parameters to extract them from a single set of optical measurements without any a priori mathematical model with unprecedented accuracy. Finally, we propose a completely new metric to characterize the performance of neural-network-based sensors, the Error Limited Accuracy. In general, the methods described here are not limited to oxygen and temperature sensing. They can be similarly applied for the sensing with multiple luminophores, whenever the underlying mathematical model is not known or too complex.

Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection

Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.

Nitrogen and Sulfur Co-doped Fluorescent Carbon Dots for the Detection of Morin and Cell Imaging

Background:

Morin has many pharmacological functions including antioxidant, anticancer, anti-inflammatory, and antibacterial effects. It is commonly used in the treatment of antiviral infection, gastropathy, coronary heart disease and hepatitis B in clinic. However, researches have shown that morin is likely to show prooxidative effects on the cells when the amount of treatment is at high dose, leading to the decrease of intracellular ATP levels and the increase of necrosis process. Therefore, it is necessary to determine the concentration of morin in biologic samples.

Method:

Novel water-soluble and green nitrogen and sulfur co-doped carbon dots (NSCDs) were prepared by a microwave heating process with citric acid and L-cysteine. The fluorescence spectra were collected at an excitation wavelength of 350 nm when solutions of NSCDs were mixed with various concentrations of morin.

Results:

The as-prepared NSCDs were characterized by transmission electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy. The fluorescence intensity of NSCDs decreased significantly with the increase of morin concentration. The fluorescence intensity of NSCDs displayed a linear response to morin in the concentration 0.10-30 μM with a low detection limit of 56 nM. The proposed fluorescent probe was applied to analysis of morin in human body fluids with recoveries of 98.0-102%.

Conclusion:

NSCDs were prepared by a microwave heating process. The present analytical method is sensitive to morin. The quenching process between NSCDs and morin is attributed to the static quenching. In addition, the cellular toxicity on HeLa cells indicated that the as-prepared NSCDs fluorescent probe does not show obvious cytotoxicity in cell imaging. Our proposed method possibly opens up a rapid and nontoxic way for preparing heteroatom doped carbon dots with a broad application prospect.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture

In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.