Real-time Monitoring Platform Research Articles

Paper-based humidity sensor for wearable based on near-field electrohydrodynamic direct writing

Flexible humidity sensors have been widely used in wearable electronics, owing to their flexibility and compatibility with electronic device substrates. This study fabricated a flexible paper-based humidity sensor with a grid-like sensing film using the near-field electrohydrodynamic direct writing (NFEDW) method. A grid-like sensing layer with a dense network of carbon nanotubes/cellulose nanofiber (CNTs/CNF) was prepared, and induced by a voltage field applied during printing. The effects of the substrates and pattern width on the performance of the sensor were systematically investigated. The prepared sensor exhibited high sensitivity of 101 % at 95 % relative humidity (RH). An interlocking structure formed between the CNTs/CNF composite and the paper substrate, endowing the sensor with outstanding bending (minimum diameter of 5 mm) and folding (up to 100 times) durability. Furthermore, the prepared sensor exhibited rapid response performance (17 % within 10 s). This addresses the issue of long response time, which is typically associated with CNTs-based sensors, making these sensors more suitable to practical applications. The above-mentioned advantages enable the sensor to be used for respiration monitoring and even for vocal recognition. Finally, a real-time respiration monitoring platform was developed to demonstrate the sensor’s potential in wearable devices.

Plant Information Management System Using OSIsoft PI System: A Case Study of Cilacap Power Plant Units 1 and 2

In the dynamic industrial sector, the ability to combine and manage data from various sources is essential. This study focuses on the utilization of the PI System to integrate operational data from multiple data sources at power plants, facilitating data driven decision making and enhancing operational efficiency. The application of the PI System in collecting data from Distributed Control Systems (DCS) and asset management systems like Maximo is examined, as well as its potential for integrating data from other power plant units. The study utilizes systems analysis methods to understand the potential of data integration in improving operational visibility and control. Findings from testing reveal that effective data integration can extend monitoring and management capabilities, indicating an overall improvement in operational performance. This study contributes to the literature on industrial data management by demonstrating the effective use of the PI System as a comprehensive tool for integrating data from various systems within power plants. It showcases the system's adaptability in enhancing operational decision-making and providing a cohesive platform for real-time performance monitoring. Additionally, the research offers insights into the practical application of systems analysis methods in the context of power plant operations, contributing to the ongoing discourse on digital transformation in the energy sector.

All-in-one strategy for the nano-engineering of paper-based bifunctional fluorescent platform for robustly-integrated real-time monitoring of food and drinking-water safety

Cu2+ contamination and food spoilage raise food and drinking water safety issues, posing a serious threat to human health. Besides, Cu2+ and H2S levels indicate excess Cu2+-caused diseases and protein-containing food spoilage. Herein, a coumarin-containing bifunctional paper-based fluorescent platform integrated with a straightforward smartphone color recognition app is developed by an all-in-one strategy. The proposed fluorescent materials can simultaneously detect Cu2+ and H2S for on-demand food and drinking water safety monitoring at home. Specifically, a coumarin-derived fluorescence sensor (referred to as CMIA) with a low detection limit (0.430 μM) and high-selectivity/-sensitivity for Cu2+ is synthesized through a simple one-step route and then loaded onto commercially used cellulose fiber filter paper to engineer a biomass-based fluorescent material (CMIA-FP). The CMIA-FP offers user-friendly, high-precision, fast-responsive, and real-time visual monitoring of Cu2+. Moreover, CMIA forms a chemically stable complex with Cu2+, loaded onto filter paper to prepare another biomass-based fluorescent platform (CMIA-CU-FP) for visual real-time monitoring of H2S. Based on the exquisite composition design, the proposed dual-function paper-based fluorescent materials equipped with a smartphone color recognition program concurrently realize fast, accurate, and easy real-time monitoring of Cu2+ in drinking water and H2S in chicken breast-/shrimp-spoilage, demonstrating an effective detection strategy for the Cu2+ and H2S monitoring and presenting the new type of biomass-based platforms for concentrated reflection of drinking water and food safety.

A Highly Integrated Lab-on-a-CMOS Platform for Real-Time Monitoring of E. Coli Growth Kinetics.

Existing miniaturized and cost-effective solutions for bacterial growth monitoring usually require offline incubators with constant temperature to culture the bio-samples prior to measurement. Such a separated sample preparation and detection scheme requires extensive human intervention, risks contamination, and suffers from poor temporal resolution. To achieve integrated sample preparation and real-time bacterial growth monitoring, this article presents a lab-on-a-CMOS platform incorporates an optical sensor array, temperature sensor array, micro-heaters, and readout circuits. Escherichia coli's (E. coli) optimum growth temperature of 37 °C is achieved through a heat regulation system consisting of two micro-heaters and an on-chip temperature sensor array. A photodiode array with an in-pixel capacitive trans-impedance amplifier to reduce inter-pixel cross-coupling is designed to extract the optical information during bacterial growth. To balance the footprint, power consumption, and quantization speed, a 10 b column successive-approximation register (SAR)/single-slope (SS) dual-mode analog-to-digital converter (ADC) is designed to digitize the temperature and optical signals. Fabricated in a standard 0.18 um CMOS process, the proposed platform can regulate the sample temperature to 37 +/- 0.2/0.3 °C within 32 mins. Enabled by an on-chip heat regulation system and photodetectors, the prototype demonstrates a real-time monitoring of bacterial growth kinetics and antibiotic responses. Minute-level temporal resolution is achieved as this proposed platform is free of extensive and time-consuming human intervention. The proposed platform can be viably used in contamination sensitive applications such as antibiotic tests, stem cell cultures, and organ-on-chips.

Preparation of multifunctional hydrogels based on co-pigment-polysaccharide complexes and establishment of a machine learning monitoring platform

Economic loss due to fish spoilage exceeds 25 billion euros every year. Accurate and real-time monitoring of the freshness of fish can effectively cut down economic loss and food wastage. In this study, a dual-functional hydrogel based on sodium alginate-co-pigment complex with volatile antibacterial and intelligent indication was prepared and characterized. The characterization results indicated that the sodium alginate-co-pigment complex successfully improved the stability and color development ability of blueberry anthocyanins and bilberry anthocyanins at different temperatures and pH. The double cross-linking network inside the hydrogel conferred it with excellent mechanical properties. During rainbow trout storage, the hydrogel indicated a color difference of 73.55 on the last day and successfully extended the shelf-life of rainbow trout by 2 days (4 °C). Additionally, four dual-channel monitoring models were constructed using machine learning. The validation error of the genetic algorithm back propagation model (GA–BP) was only 5.6e-3, indicating that GA-BP can accurately monitor the freshness of rainbow trout. The rainbow trout real-time monitoring platform built based on GA-BP model can monitor the freshness of rainbow trout in real time through the images uploaded by users. The results of this study have broad applicability in the food industry, environmental conservation, and economic sustainability.

Nitrodopamine modified MnO2 NS-MoS2QDs hybrid nanocomposite for the extracellular and intracellular detection of glutathione.

We have developed a highly sensitive and reliable fluorescence resonance energy transfer (FRET) probe using nitro-dopamine (ND) and dopamine (DA) coated MnO2 nanosheet (ND@MnO2 NS and DA@MnO2 NS) as an energy acceptor and MoS2 quantum dots (QDs) as an energy donor. By employing surface-modified MnO2 NS, we can effectively reduce the fluorescence intensity of MoS2 QDs through FRET. It can reduce MnO2 NS to Mn2+ and facilitate the fluorescence recovery of the MoS2 QDs. This ND@MnO2 NS@MoS2 QD-based nanoprobe demonstrates excellent sensitivity to GSH, achieving an LOD of 22.7 nM in an aqueous medium while exhibiting minimal cytotoxicity and good biocompatibility. Moreover, our sensing platform shows high selectivity to GSH towards various common biomolecules and electrolytes. Confocal fluorescence imaging revealed that the nanoprobe can image GSH in A549 cells. Interestingly, the ND@MnO2 NS nanoprobe demonstrates no cytotoxicity in living cancer cells, even at concentrations up to 100 μg mL-1. Moreover, the easy fabrication and eco-friendliness of ND@MnO2 NS make it a rapid and simple method for detecting GSH. We envision the developed nanoprobe as an incredible platform for real-time monitoring of GSH levels in both extracellular and intracellular mediums, proving valuable for biomedical research and clinical diagnostics.

Fully integrated wearable microneedle biosensing platform for wide-range and real-time continuous glucose monitoring

Wearable microneedle sensors for continuous glucose monitoring (CGM) have great potential for clinical impact by allowing access to large data sets to provide individualized treatment plans. To date, their development has been challenged by the accurate wide linear range tracking of interstitial fluid (ISF) glucose (Glu) levels. Here, we present a CGM platform consisting of a three-electrode microneedle electrochemical biosensor and a fully integrated radio-chemical analysis system. The long-term performance of the robust CGM on diabetic rats was achieved by electrodepositing Prussian blue (PB), and crosslinking glucose oxidase (GOx) and chitosan to form a 3D network using glutaraldehyde (GA). After redox by GOx, PB rapidly decomposes hydrogen peroxide and mediates charge transfer, while the 3D network and graphite powder provide enrichment and release sites for Glu and catalytic products, enabling a sensing range of 0.25–35 mM. Microneedle CGM has high sensitivity, good stability, and anti-interference ability. In diabetic rats, CGM can accurately monitor Glu levels in the ISF in real-time, which are highly consistent with levels measured by commercial Glu meters. These results indicate the feasibility and application prospects of the PB-based CGM for the clinical management of diabetes. Statement of SignificanceThis study addresses the challenge of continuous glucose monitoring system design where the narrow linear range of sensing due to the miniaturization of sensors fails to meet the monitoring needs of clinical diabetic patients. This was achieved by utilizing a three-dimensional network of glutaraldehyde cross-linked glucose oxidase and chitosan. The unique topology of the 3D network provides a large number of sites for glucose enrichment and anchors the enzyme to the sensing medium and the conductive substrate through covalent bonding, successfully blocking the escape of the enzyme and the sensing medium and shortening the electron transfer and transmission path.

Implementation of a scalable platform for real-time monitoring of machine tools

In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher data processing needs than the previous ones.

Design and implementation of a medical platform for real-time and remote ECG monitoring using DigiMesh wireless sensor network technology

Design and implementation of a medical platform for real-time and remote ECG monitoring using DigiMesh wireless sensor network technology

Fast responding and highly selective chemoresistive humidity sensor based on hydrated V2O5 nanobelts for real-time breath monitoring

Chemoresistive humidity sensors have gained massive attraction owing to the high potential for application in real-time breath monitoring. Herein, we introduce a chemoresistive humidity sensor based on ultrathin V2O5·nH2O nanobelts (NBs) prepared by liquid-phase exfoliation. Due to the layered structure, water molecules were embedded between layers, increasing the active surface area. Moreover, the multivalent vanadium states and oxygen vacancies are highly beneficial to increasing the adsorption sites in gas sensing reactions. V2O5·nH2O NBs exhibited promising humidity sensing properties examined by exposure to 50% relative humidity (RH). The response and recovery time to 50% RH was 21 s and 356 s, respectively, with stable and reliable sensing behaviors to repetitive exposure to humid air. The response to 50% RH was 27 times higher than the response to interference gas, indicating the outstanding selectivity to humidity. The theoretical detection limit was 0.55% RH, showing linear response increase with humidity concentration. The response degradation was marginal even after 3 weeks of relaxation, showing high stability. The breath monitoring experiment reveals extremely rapid response and recovery to exhaled breath, enlarging the suitability for a real-time breath monitoring platform.

Demonstration of real-time monitoring in smart graded-water supply grid: an institutional case study

Abstract Real-time information on water supply and quality is a crucial asset for planning and managing water resources, infrastructure, and scientific research for sustainable development.  In this direction, the innovative concept of smart water infrastructure is progressing. The present paper reports a case study on the demonstration of a `smart graded-water supply grid' on the campus of the Indian Institute of Technology Jodhpur, India. The paper describes the transformation of ∼13 km long water distribution network that supplies drinking water to ∼5,000 inhabitants into smart supply grid by deploying sensors and establishing an IoT-enabled real-time monitoring platform. The data sets of water flow and pressure collected from sensor nodes are analyzed to understand the characteristic diurnal water usage profiles unique to student hostels on the campus. The data show a distinctive consumption profile of student hostels over the weekdays with a maximum peak consumption of 16.38 m3/h. Monitoring of vital quality parameters such as chlorine, pH, and temperature demonstrate acceptable levels thereby ensuring compliance with safety standards. The purpose of the paper is to provide insights from a real-world case and close the knowledge gap between general awareness and the potential of smart water grid in sustainable management of graded-water services.

Ratiometric upconversion luminescence sensing platform for visual monitoring of antiarrhythmic drug level in serum

Arrhythmia seriously threatens life and health, it requires long-term use of antiarrhythmic drugs such as mexiletine and needs to monitor the dosage of drugs in the body to ensure efficacy and avoid serious side effects. Here, we designed a sensing platform for real-time visual monitoring of drug efficacy using a ratiometric upconversion probe, which overcomes the fluorescence background interference in the serum, and improves the resolution and accuracy of visual detection. By combining lanthanide-doped upconversion nanoparticles (UCNPs) with readily available organic dyes, an efficient upconversion nanoprobe was constructed under the reaction mechanism of internal filtration effect (IFE) to realize the quantitative detection of mexiletine in serum. Due to the addition of mexiletine, the characteristic emission peak of the probe at 540 nm changes, while the fluorescence intensity at 655 nm is unchanged. Therefore, the visual detection of mexiletine in serum is realized according to the fluorescence intensity changes of I540/I655. In addition, the designed sensing platform is combined with a smartphone to realize real-time visualization and quantitative monitoring of mexiletine, and the detection limits in fluorescence and colorimetric modes are as low as 55.6 nM and 469.75 nM respectively, consequently resulting in sensitive monitoring the effective dosage of drugs in the serum. The proposed detection platform provides a new sensing strategy for the visual determination of antiarrhythmic drugs and an instant device to warn the treatment process of the disease immediately.

Multifunctional DNA nanosphere platform for real-time monitoring and fluorescence imaging-guided photodynamic therapy

The DNA nanosphere (DNS) is constructed through the assembly of multifunctional Y-scaffold DNA monomers, which encompass aptamers, RNA interferences, and photodynamic therapy agents. The DNS nanoplatform facilitates efficient cellular uptake via aptamer-mediated delivery. The RNA interferences inhibit the microRNA-21 and microRNA-155, thereby enabling precise imaging guidance for photodynamic therapy, ultimately leading to apoptosis in MCF-7 cells. The platform allows for real-time monitoring of the therapeutic process, presenting a more comprehensive approach to cancer treatment. The results of in vitro and in vivo studies suggest the potential of this multifunctional DNS platform as a promising approach for cancer therapy that can be tailored to individual patients based on their specific cancer type.

Internet of Things experimental platform for real-time water monitoring: a case study of the Araranguá River estuary

The Internet of Things (IoT) is a technology that can be employed to monitor several sectors, mostly precision agriculture, where environmental data can be used to better manage resources, reduce costs, and improve the use of natural resources. This work proposes the application of cloud-based IoT for monitoring irrigation water in rice fields. Southern Brazil was chosen as a case study because it suffers from water pollution problems due to urban activities, coal mining, and the inflow of saline water into the Araranguá River estuary. All these factors can compromise the water quality for irrigation. The presence of salt in irrigation water can compromise rice growth, making its monitoring essential for successful cultivation. Currently, the rice farmer needs to travel to the riverbank to assess the water quality using a manual sensor. This procedure demands time and resources and is not always efficient. The technology helps indicate whether the water is of quality to irrigate crops, helping the farmers decide whether to use that water to irrigate their fields. The solution is based on low-cost wireless sensor network devices, with subsequent transmission of data from the gateway using the network mobile phone cloud. Data collected were also accessed by the smartphone through a mobile application. This system was implemented for testing in the Araranguá River estuary, southern Santa Catarina, Brazil. Also, it appears as an option for farmers who need to monitor the quality of the water channel of the crop, thus avoiding the loss and reduction in crop productivity because the system can be continuously monitored and show notifications to the user. The highlights of this work were prototyping a complete solution to help the farmer using a smartphone application.

Seasonal glacier change revealed from the real-time monitoring platform on Baishui River Glacier No.1 in Yulong Snow Mountain, Southeastern Qinghai–Tibet plateau

Abstract The mass balance of glaciers requires more detailed and continuous observations to understand their seasonal change in relation to climate. Here, we designed and installed an automated real-time monitoring platform at 4645 m a.s.l. on the Baishui River Glacier No.1 to collect continuous high-resolution observational data, and analyzed the seasonal dynamic from glacier movement and surface mass balance from glacier melting and snow accumulation. The results showed that the platform moved northeastward ~12.9 m at a rate of 0.06 ± 0.02 m d−1 between September 2021 and April 2022. The surface mass balance showed a varied temporal period. July and August were the main ablation periods, while ablation decreased and ceased in September. The glacier neither melted nor accumulated much between October and December, but began to have rapid snow accumulation in January. The glacier surface temperature varied with the air temperature and showed significant inter-seasonal differences among monsoon, post-monsoon and winter seasons. The surface mass balance also exhibited a strong response to the air temperature changes, with an average decrease of 1°C the point mass balance increased by 0.11 m w.e. from monsoon to post-monsoon and 0.22 m w.e. from post-monsoon to winter. Moreover, we found snowfall caused a decrease in the glacier surface temperature by increasing the surface albedo.

PVF composite conductive nanofibers-based organic electrochemical transistors for lactate detection in human sweat

The detection of lactate and other bioactive molecules is of great significance in biological research, medical testing, and environmental monitoring. Organic electrochemical transistors are a kind of biosensor devices, which can convert biological signals into electrical signals. In recent years, fiber-based organic electrochemical transistors (FECTs) provide a new approach for the construction of flexible wearable biosensors, promoting greatly the development of flexible wearable electronic products. In this study, a multilayer composite electrode was first prepared by incorporating MXene and PEDOT:PSS onto electrospun oriented polyvinyl formal (PVF) nanofiber bundle, and then assembled into FECT with high sensitivity and selectivity. Meanwhile, a wearable real-time monitoring platform was built to detect the lactate concentrations in human sweat. The results showed that MXene could act as a bridge between PEDOT:PSS and PVF, and the sensing performance of PVF/MXene/PEDOT:PSS-based FECT was enhanced obviously. It had a wide linear response range of 1 nM-100 mM, a sensitivity of 0.442 NCR/decade, a quick response time of 0.5 s, a low detection concentration, an excellent reproducibility, and a superior anti-interference characteristic. Therefore, this work provided more possibilities for the development of wearable biosensor.

Real-Time Macrophage Phenotype Identification Using Single Walled Carbon Nanotubes and Machine Learning

Macrophages play a critical role in the immune system, as both pro and anti-inflammatory immune cells these are the first line of defense from foreign bodies. Although methods for identifying Macrophage Phenotype (M1, M2) currently exist, these methods are limiting to factors like time, cost, operational training, and sample size. Cost and time effective identification of macrophage phenotypes can not only lead to better therapeutic research but also help in creating a platform for real time monitoring of the wound healing process as well as other (macrophage related) disease progression.Single Walled Carbon Nanotubes are ideal optical biosensors due to their intrinsic near-Infrared (nIR) emission, which is highly sensitive, easily tunable and indefinitely photostable. Due to such desirable properties, non-covalent functionalization of these fluorescent nanotubes has resulted in them being used as in vitro and in vivo sensors for numerous applications. Even so, the full extent of nanotube applicability is yet to be explored due to their underdeveloped potential as next generation bioimaging and bio-sensing probes. Here we demonstrate a platform using DNA functionalized Single Walled Carbon Nanotubes and machine learning to identify macrophage phenotype with 99% accuracy and effectively estimate the number of each polarization state in a sample.

CF/PEKK advanced pultrusion thermoforming process temperature field simulation

Advanced pultrusion molding technology is one of the composite molding processes. It has unique advantages in the automated manufacturing of long trusses and long beam components. Thermoplastic composites are becoming popular for research in many fields such as aerospace because of their room-temperature storage and secondary use. Since the temperature history is critical to the molding quality of thermoplastic composites, the temperature field variation of CF/PEKK composites in the advanced pultrusion thermoforming process has been explored in the context of studying the advanced pultrusion molding process of thermoplastic composites in this paper. By constructing a 3D model of the temperature field and setting up a complete solution scheme in ANSYS simulation software, the simulated temperature field data were obtained, and a real-time temperature monitoring platform was set up to obtain the measured temperature data, and the comparison showed that the two temperature profiles fit well, which proved the applicability of the model and solution scheme to this study.

Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing

In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.

A real-time monitoring platform of colorimetric LAMP for developing rapid visual detection kits of SARS-CoV-2

A real-time monitoring platform of colorimetric LAMP for developing rapid visual detection kits of SARS-CoV-2