Malvern Nano ZS Particle Size Comparison with the Integrated Virus Detection System (IVDS)

2010 ◽  
Author(s):  
Charles H. Wick ◽  
Patrick E. McCubbin
Keyword(s):  
Particle Size ◽  
Detection System ◽  
Virus Detection

Mass Spectrometry and Integrated Virus Detection System Characterization of MS2 Bacteriophage

2005 ◽  
Author(s):  
Rabih E. Jabbour ◽  
Deborah Kuzmanovic ◽  
Patrick E. McCubbin ◽  
Ilya Elashvili ◽  
Charles H. Wick
Keyword(s):  
Mass Spectrometry ◽  
Detection System ◽  
Virus Detection ◽  
Ms2 Bacteriophage ◽  
System Characterization

Integrated Virus Detection System Characterization of MS2 and TBSV After Pulsed Lamp Exposure

2010 ◽  
Author(s):  
Charles H. Wick ◽  
Stephen Wengraitis ◽  
Patrick E. McCubbin
Keyword(s):  
Detection System ◽  
Virus Detection ◽  
System Characterization

scholarly journals Development of a New Sensor Module for an Enhanced Fuel Flexible Operation of Biomass Boilers

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 661
Author(s):  
Martin Meiller ◽  
Jürgen Oischinger ◽  
Robert Daschner ◽  
Andreas Hornung
Keyword(s):  
Particle Size ◽  
Bulk Material ◽  
Detection System ◽  
Wood Chips ◽  
Convective Drying ◽  
Characteristic Functions ◽  
Biomass Combustion ◽  
Fuel Flexibility ◽  
Sensor Module ◽  
Over Time

The heterogeneity of biogenic fuels, and especially biogenic residues with regard to water and ash content, particle size and particle size distribution is challenging for biomass combustion, and limits fuel flexibility. Online fuel characterization as a part of process control could help to optimize combustion processes, increase fuel flexibility and reduce emissions. In this research article, a concept for a new sensor module is presented and first tests are displayed to show its feasibility. The concept is based on the principle of hot air convective drying. The idea is to pass warm air with 90 °C through a bulk of fuel like wood chips and measure different characteristics such as moisture, temperatures and pressure drop over the bulk material as a function over time. These functions are the basis to draw conclusions and estimate relevant fuel properties. To achieve this goal, a test rig with a volume of 0.038 m3 was set up in the laboratory and a series of tests was performed with different fuels (wood chips, saw dust, wood pellets, residues from forestry, corn cobs and biochar). Further tests were carried out with conditioned fuels with defined water and fines contents. The experiments show that characteristic functions arise over time. The central task for the future will be to assign these functions to specific fuel characteristics. Based on the data, the concept for a software for an automated, data-based fuel detection system was designed.

scholarly journals Expression and Purification of Recombinant Mayaro Virus Envelope Glycoproteins E1 and E2 to Develop a Mayaro Virus Detection System

2020 ◽  
Vol 50 (1) ◽  
pp. 25
Author(s):  
So Yeon Yi ◽  
Kyungah Yoon ◽  
Jungsun Kwon ◽  
Kyoon Eon Kim ◽  
Kyoungsook Park ◽  
...  
Keyword(s):  
Detection System ◽  
Virus Detection ◽  
Envelope Glycoproteins ◽  
Expression And Purification ◽  
Virus Envelope ◽  
Mayaro Virus

scholarly journals MultiScan: A Private Online Virus Detection System

2019 ◽  
Vol 8 (6) ◽  
pp. 53-55
Author(s):  
Ming Liu ◽  
Yuxuan He ◽  
Zhi Xue ◽  
Xiangjian He ◽  
Jinjun Chen
Keyword(s):  
Detection System ◽  
Virus Detection

scholarly journals Non Invasive Detection of Dengue Viruses from Saliva: In vitro Study

2018 ◽  
Vol 22 (2) ◽  
pp. 75
Author(s):  
Budi Saksono
Keyword(s):  
Dengue Virus ◽  
Detection System ◽  
Point Of Care ◽  
Virus Detection ◽  
In Vitro Study ◽  
Vitro Study ◽  
Dengue Viruses ◽  
Non Invasive ◽  
Preliminary Study

      In the previous paper, we had succeeded in developing an early detection system of dengue viruses using Sugar liganded Gold Nano Particle (SGNP) only from 6 μL serum. It has been reported that dengue virus is also detected in the saliva and urine of the patient. The evidences lead to the possibility of developing non-invasive methods of dengue virus detection. In this in vitro study, we evaluated the utility of SGNP to capture and concentrate dengue virion in 10% saliva solution. The results showed that dengue virion was successfully detected in 10% of saliva solution. Analysis of virion stability during storage showed that virions in salivary samples were stable up to 3 days at temperature wherease the RNA has significantly degraded. Although still a preliminary study, the data obtained show the prospect of SGNP as a non-invasive dengue virus detection method, as well as the development of POC (Point of Care) method. Clinical trials using saliva from dengue viruses infected patients need to be done to prove the effectiveness of the SGNP method.

High-Performance Virus Detection System by using Deep Learning

2020 ◽  
Author(s):  
Ying-Feng Hsu ◽  
Makiko Ito ◽  
Takumi Maruyama ◽  
Morito Matsuoka ◽  
Nicolas Jung ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Detection System ◽  
Virus Detection
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