scholarly journals Lab on optical fiber: surface nano-functionalization for real-time monitoring of VOC adsorption/desorption in metal-organic frameworks

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jieyun Wu ◽  
Chunlan Tang ◽  
Wanying Zhang ◽  
Xiaoxia Ma ◽  
Shiwei Qu ◽  
...  
Keyword(s):  
Real Time ◽  
Refractive Index Change ◽  
Fiber Surface ◽  
High Porosity ◽  
Real Time Monitoring ◽  
The Real ◽  
Voc Adsorption ◽  
Fiber Technology ◽  
Metal Organic ◽  
Adsorption Desorption

Abstract Metal-organic framework (MOF) nanomaterials are emerging porous coordinative polymers with large surface area and high porosity. Their application scenarios highly depend on adsorption/desorption dynamics of guest molecules in the framework. For representative ZIF-8 with framework flexibility, the study of molecule transportation in the pore channels of ZIF-8 will address the ambiguity of unclear application scenarios. In this study, the integration of lab-on-fiber technology and nanotechnology are demonstrated for real-time monitoring of adsorption/desorption dynamics of heterocyclic volatile compounds (VOCs) with kinetic diameters larger than the window aperture of ZIF-8. The in-line fiber interferometer with cascaded long-period gratings is used to monitor the real-time refractive index change of VOC adsorption/desorption. The structure-effect relationship between guest VOCs and framework flexibility is analyzed. It shows that the adsorption dynamics is highly related to the molecular geometry and kinetic diameter. The framework flexibility results in the trapping of guest VOCs toluene, pyridine, and tetrahydrofuran in the frameworks. The methanol adsorption/desorption is an effective strategy for the fast desorption of trapped residual VOCs in the framework. Finally, we conceptually demonstrated the real-time monitoring of trace toluene enrichment using ZIF-8 for indoor air purification. This study paves the way for the in-depth understanding of framework flexibility for MOF’s application.

Real-Time Monitoring of Dissolved Oxygen with Inherent Oxygen-Sensitive Centers in Metal–Organic Frameworks

2016 ◽  
Vol 28 (8) ◽  
pp. 2652-2658 ◽  
Author(s):  
Jian Yang ◽  
Zhe Wang ◽  
Yongsheng Li ◽  
Qixin Zhuang ◽  
Jinlou Gu
Keyword(s):  
Dissolved Oxygen ◽  
Real Time ◽  
Metal Organic Frameworks ◽  
Real Time Monitoring ◽  
Oxygen Sensitive ◽  
Metal Organic

Observing the real time formation of phosphine-ligated gold clusters by electrospray ionization mass spectrometry

2017 ◽  
Vol 19 (26) ◽  
pp. 17187-17198 ◽  
Author(s):  
Marshall R. Ligare ◽  
Grant E. Johnson ◽  
Julia Laskin
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Real Time ◽  
Electrospray Ionization Mass Spectrometry ◽  
Gold Cluster ◽  
Gold Clusters ◽  
Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Real Time Monitoring ◽  
The Real

Real-time monitoring of the gold cluster synthesis by electrospray ionization mass spectrometry reveals distinct formation pathways for Au8, Au9 and Au10 clusters.

A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture

2021 ◽  
pp. 2150294
Author(s):  
Neng Huang ◽  
Junxing Zhu ◽  
Chaonian Guo ◽  
Shuhan Cheng ◽  
Xiaoyong Li
Keyword(s):  
Information Systems ◽  
Real Time ◽  
Data Center ◽  
Disaster Recovery ◽  
Distributed Storage ◽  
Data Availability ◽  
Data Consistency ◽  
Real Time Monitoring ◽  
The Real ◽  
Hash Chain

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.

An automated apparatus for the real-time monitoring of bioluminescence in plants

2005 ◽  
Vol 340 (2) ◽  
pp. 187-192 ◽  
Author(s):  
Kazuhisa Okamoto ◽  
Kiyoshi Onai ◽  
Norihiko Ezaki ◽  
Toru Ofuchi ◽  
Masahiro Ishiura
Keyword(s):  
Real Time ◽  
Real Time Monitoring ◽  
The Real

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

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