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Global Electroencephalogram Equipment Market by Manufacturers, Regions, Type and Application, Forecast to 2021

The Global Electroencephalogram Equipment Market research study report is a respected source of information which offers a telescopic view of the current market status. Various key factors are discussed in the report, which will help the buyer in studying the Global Electroencephalogram Equipment market trends and opportunities. The Global Electroencephalogram Equipment market is a highly diligent study on competitive landscape analysis, prime manufacturers, marketing strategies analysis, Market Effect Factor Analysis and Consumer Needs by major regions, types, applications in Global market considering the past, current and future state of the Global Electroencephalogram Equipment industry. The report provides a thorough overview of the Global Electroencephalogram Equipment Market including definitions, classifications, applications and chain structure.

This Research study focus on these types: –

  • Routine EEG
  • Sleep EEG
  • Ambulatory EEG
  • Other

This Research study focus on these applications: –

  • Hospital
  • Clinic
  • Other

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This report studies Interferons in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with Production, price, revenue and market share for each manufacturer, covering

  • Natus Medical Incorporated
  • Nihon Kohden America
  • Cadwell Laboratories
  • Electrical Geodesics Incorporated
  • Covidien llc
  • Micromed
  • Neuroelectrics
  • NCC
  • Shanghai Medical Instruments

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Several important areas are covered in this Global Electroencephalogram Equipment market research report. Some key points among them: –

  1. What Overview Global Electroencephalogram Equipment Says? This Overview Includes Diligent Analysis of Scope, Types, Application, Sales by region, manufacturers, types and applications
  2. What Is Global Electroencephalogram Equipment Competition considering Manufacturers, Types and Application? Based on Thorough Research of Key Factors
  3. Who Are Global Electroencephalogram Equipment Global Key Manufacturers? Along with this survey you also get their Product Information (Type, Application and Specification)
  4. Global Electroencephalogram Equipment’s Manufacturing Cost Analysis –This Analysis is done by considering these prime elements like Key RAW Materials, Price Trends, Market Concentration Rate of Raw Materials, Proportion of Raw Materials and Labour Cost in Manufacturing Cost Structure
  5. Global Electroencephalogram Equipment Industrial Chain Analysis
  6. Global Electroencephalogram Equipment Marketing strategies analysis by
  7. Market Positioning
  8. Pricing and Branding Strategy
  9. Client Targeting
  10. Global Electroencephalogram Equipment Effect Factor Analysis
  11. Technology Process/Risk Considering Substitute Threat and Technology Progress In Global Electroencephalogram Equipment Industry
  12. Consumer Needs or What Change Is Observed in Preference of Customer
  13. Political/Economical Change
  14. What is Global Electroencephalogram Equipment forecast (2016-2021) Considering Sales, Revenue for Regions, Types and Applications?

Topics such as sales and sales revenue overview, production market share by product type, capacity and production overview, import, export, and consumption are covered under the development trend section of the Global Electroencephalogram Equipment market report.

Lastly, the feasibility analysis of new project investment is done in the report, which consist of a detailed SWOT analysis of the Global Electroencephalogram Equipment market. Both established and new players in the Global Electroencephalogram Equipment industry can use this report for complete understanding of the market.

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Some of key Tables and Figures included in this research study: –

  1. Figure Picture of Global Electroencephalogram Equipment
  2. Figure USA, Europe, China. Southeast Asia, India, Japan Global Electroencephalogram Equipment Revenue and Growth Rate (2011-2021)
  3. Table Production Base and Market Concentration Rate of Raw Material
  4. Figure Manufacturing Cost Structure of Global Electroencephalogram Equipment
  5. Figure Manufacturing Process Analysis of Global Electroencephalogram Equipment
  6. Figure Global Electroencephalogram Equipment Industrial Chain Analysis
  7. Figure Global Electroencephalogram Equipment Sales and Growth Rate Forecast (2016-2021)
  8. Figure Global Electroencephalogram Equipment Revenue and Growth Rate Forecast (2016-2021)
  9. Table Global Electroencephalogram Equipment Sales Forecast by Regions (2016-2021)
  10. Table Global Electroencephalogram Equipment Sales Forecast by Type (2016-2021)
  11. Table Global Electroencephalogram Equipment Sales Forecast by Application (2016-2021)

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AI-Powered Breath Detector Diagnoses 17 Different Diseases

AI-Powered Breath Detector Diagnoses 17 Different Diseases



Our breath contains a slew of information about our health in the form of molecules whose existence and concentration can serve as biomarkers for disease. Typically breath sensors focus on a single biomarker and therefore are limited in their scope and screening ability. A worldwide scientific collaboration headed by a team from Technion−Israel Institute of Technology has developed a breath sensor capable of detecting many different molecules and correlated these biomarkers to 17 different diseases.nanoarray

The device consists of an array of specially prepared gold nanoparticle sensors and ones based on a random network of single-walled carbon nanotubes. While it is impressive on its own, what gave it special powers was to use it to collect breath samples from thousands of patients with different diseases and to use artificial intelligence software to find correlations in the data. Thanks to the AI component of this research, the average diagnostic accuracy of the system was demonstrated at 86% for 17 different diseases, including such disparate conditions as a number of cancers, Crohn’s disease, two types of Parkinson’s, preeclampsia, and pulmonary hypertension.

Here’s video from Technion with more on the technology:

Study in ACS Nano: Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules…

Via: American Chemical Society…

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IRadimed’s MRI Compatible MRidium 3860+ IV Infusion Pump FDA Cleared

IRadimed’s MRI Compatible MRidium 3860+ IV Infusion Pump FDA Cleared



IRadimed, a company out of Winter Springs, Florida, won FDA clearance for its MRidium 3860+ MRI-compatible IV infusion pump. The device has non-magnetic motor and no ferrous (containing iron) components that would be affected by a magnetic field. It’s safe to use around MRI machines up to 3.0 Tesla, which means just about any scanner found inside a hospital.

mridiumThe device can pump doses from 0.1 mL to 1,400 mL per hour, and so can be used with pediatric and adult patients that may need very different infusion rates.

Thanks to a built-in lithium battery, the device can be self-powered for up to 12 hours at 125 mL per hour.

  • World’s ‘Only’ Non–Magnetic IV Infusion Pump
  • Placement up to the 10,000 Gauss Line
  • Intuitive Smart IV Pump technology reduces field errors
  • Expandable to a second infusion channel
  • Masimo™ SpO2 Patient Monitoring
  • Large 10-Numeric Keypad – Quick Programming
  • Infusion Range – 0.1 to 1400 mL/Hr
  • Over 12–Hour Battery at 125mL/Hr
  • Adjustable KVO Rate
  • Upstream / Downstream Occlusion Detection
  • Air-in-Line Detection
  • Delivery via Syringe, Bag or Bottle

Product page: MRidium 3860+…

Via: IRadimed…

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Miniature Sensor Measures Velocity of Blood Flow Below Skin

Miniature Sensor Measures Velocity of Blood Flow Below Skin





Kyocera Corporation out of Kyoto, Japan has announced the development of a tiny optical sensor for measuring blood flow within subcutaneous tissue. Readings from such a device may help assess how injured tissue is healing, produce evidence of dehydration, and detect altitude sickness. Many other applications may come to light as this kind of technology becomes widely available for use by the public.

The sensor measures 1.6 mm by 3.2 mm and is only 1 mm in height. Because of its size it can be integrated into various devices, including smartphones and wearable activity trackers. Within the sensor is a laser that shines light onto the skin, and a photodiode that converts light returning from the skin into an electrical signal. By detecting and measuring the Doppler shift of the returning light compared to what the laser emits, the device can extrapolate how fast red blood cells are moving. Moreover, the strength of the light signal bouncing from the skin is indicative of the concentration of red blood cells, another interesting indicator.

The sensor at the moment only works on certain parts of the body where there’s a lot of blood perfusion close to the surface of the skin. This includes the ear lobe, fingers, and the forehead, and may include other parts of the body. It’s probably not very effective for using on the bottom of feet for people at risk of diabetic neuropathy, for example.




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Tiny Embeddable Sensor Unveiled for Measuring pH, Chloride

Tiny Embeddable Sensor Unveiled for Measuring pH, Chloride



Imec and Holst Centre, two sister research organizations based in Belgium and Holland, unveiled a tiny sensor for measuring a fluid’s pH and chloride levels. Chloride is an electrolyte involved in a variety of cellular processes, including regulating the body’s pH level. Being able to measure these parameters may make them popular metrics for assessing athletic performance and for personalized medicine.

The new sensor and supporting components are integrated into a unified chip. It’s small enough to be embedded into other devices, such as activity trackers, for example.

The major technological challenge was creating an electrode that remains stable and does not skew results over time. There are two electrodes within this type of sensor. One is an ion-sensitive electrode that has a membrane, and a reference electrode. Electric potential is produced when the electrodes are immersed in an ion-rich fluid, the intensity of which is proportional to the  ion concentration. The problem is that reference electrodes that are as tiny as required in such a small device are typically unstable and eventually begin attenuating the signal.

According to Marcel Zevenbergen, a senior researcher on the project, overcoming these limitations involved creating a reference electrode that has a microfluidic channel as junction and solid-state iridium oxide (IrOx) and silver chloride (AgCl) electrodes on a silicon substrate. The two types of electrodes are optimized for measuring the pH and chloride, respectively.

The team tested the new sensor and showed that it remains stable over an extended time, providing accurate readings quickly and at high sensitivity.

Via: Imec…

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Clarius App-Powered Wireless Ultrasounds Win FDA Clearance

Clarius App-Powered Wireless Ultrasounds Win FDA Clearance



Clarius Mobile Health, a company out of Burnaby, British Columbia, just won FDA clearance for its C3 and L7 Clarius Wireless Ultrasound Scanners. The devices use just about any iOS or Android phone or tablet as the display. A proprietary Clarius app is used to control the transducers and display the visualizations. The transducers feature automatic gain and frequency settings, helping to quickly locate and view the target anatomy. The app provides options to manage the images, as well as to share them securely via the firm’s secure “Clarius Cloud”.


The exterior of the transducers is made of magnesium metal, allowing it to be rugged enough to be used in and outside the clinic. Inside is a rechargeable battery and an extra one is provided with every set.

The Clarius C3 is intended for abdominal and lung exams, while the Clarius L7 is meant for needle guidance and for imaging shallow anatomy. Both are now available for purchase directly from Clarius.

Here’s a quick promo video for the Clarius ultrasound:

Product page: Clarius…

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An Interview with Gene Saragnese, Chairman & CEO of MedyMatch Technology

An Interview with Gene Saragnese, Chairman & CEO of MedyMatch Technology



MedyMatch Technology, a company based in Tel Aviv, Israel, leverages artificial intelligence, deep learning, and computer vision technologies to offer patient-specific clinical decision support. Their application helps radiologists and emergency room physicians to detect signs of intracranial hemorrhages, which are difficult to diagnose by standard analysis of imaging data alone. The Medgadget team recently had an opportunity to speak with Gene Saragnese, Chairman and Chief Executive Officer of MedyMatch, to discuss their technology and its significance in depth.

Prior to joining MedyMatch in January of 2016, Gene was the Chief Executive Officer of Philips Imaging and a member of Philips Healthcare’s Executive Team. A graduate of Rutgers College of Engineering in New Jersey, he has also previously served as GE Healthcare’s Chief Technology Officer and has held management roles with GE, RCA, Martin Marietta, and Lockheed Martin. Our interview with MedyMatch’s lead executive is given in full below.


Zach Kaufman, MedgadgetGene, thank you so much for taking the time to speak with us on behalf of MedyMatch! To start us off, I thought I would offer you an opportunity to highlight any features or applications of the technology that Medgadget readers might find of particular interest or importance.

gene-saragneseGene Saragnese, MedyMatch: MedyMatch will make available several applications to be utilized in the acute care setting. The first application, for clinical research, is for the detection of intracranial hemorrhage. Upon regulatory approval, it will be utilized as a ‘second read’ by physicians, to identify areas of suspected bleeds. MedyMatch integrates seamlessly into the hospital workflow by extracting medical images from the PACS, processing in the cloud, and returning annotated images to the PACS for viewing by the physicians. In initial preclinical trial results, MedyMatch achieved a ~97% sensitivity with 90% specificity, and a ~96% Negative Predictive Value (NPV) for intracranial hemorrhage detection on hundreds of thousands of non-contrast CT slices.


Medgadget: MedyMatch relies on “Deep Vision” technology, as you term it, to aid radiologists and emergency room physicians in identifying intracranial hemorrhages related to brain trauma and strokes, which are difficult to detect by looking at medical imaging scans directly. What types of abnormalities and indications are medical professionals most susceptible to missing in analyzing imaging data with the human eye?

Saragnese: Extensive studies have been performed on this topic. Research summarized by the National Institutes of Health in January 2012 indicates there are a wide range of causes.


Medgadget: With what frequency do errors in medical imaging diagnosis occur using status quo techniques?

Saragnese: In some cases, the detection of a brain bleed is difficult. Studies have shown that the error rate in the detection of brain bleed by emergency room physicians is estimated to be between 20-30%. Additionally, studies have shown 80% of medical imaging diagnosis errors are evident, but remain unnoticed by the physician.


Medgadget: You mention that MedyMatch will be made available as a prioritization algorithm operating within radiological PACS systems, and I understand that you are aiming to enable implementations with computer assisted detection (CAD) tools as well. Can you tell us a bit about how the different forms the technology might take will vary from one another in terms of their applications, direct users, and benefits?

Saragnese: MedyMatch is a near zero footprint solution. We do not sell workstations, viewers, or imaging systems. Rather, we simply process patient studies that are either sent to a server on the premises or directed to a secure, HIPPA-compliant cloud. Patient studies are annotated and regions of interest are marked before being reinserted into the customers current PACS system for consideration by the physician. It is foreseen that the algorithm can also advise the PACS system in the background of a potential finding. This signal will be considered as part of the prioritization process in PACS.


Medgadget: Are the deep vision algorithms involved in identifying hemorrhages suited only for acute, patient-specific investigation, or can the technology be utilized to offer proactive, population-level information as well?

Saragnese: MedyMatch has developed a generalized deep learning platform. This means that we will be able to apply the company’s technology, techniques, and methods to a wide range of clinical areas. The application of the technology is virtually limitless. However, MedyMatch is not a clinical research company, so applications are to be built where variability in reader performance can be reduced, thereby improving patient outcomes while reducing overall healthcare costs.


Medgadget: Regarding the algorithms themselves, what types of non-imaging data are integrated into the deep learning and computer vision analysis? How does that supplemental data help to inform diagnoses beyond traditional CAD tools?

Saragnese: Traditional CAD considers imaging data, while Deep Vision (deep learning and computer vision) techniques consider the whole patient. Data such as EMR, genomic, lab results, etc., can all be integrated into the data set that the algorithms consider. The more data, the higher the probability for a more personalized assessment.


Medgadget: Once the MedyMatch system produces its insights, what is the role of the radiologist or physician in interpreting the outputs?

Saragnese: MedyMatch will never replace the radiologist or physician. MedyMatch is a visual clinical decision support tool to provide a second set of eyes. MedyMatch can only assess a patient; the final diagnosis will always be the responsibility of the doctor.


Medgadget: Is there an opportunity for users of the system to integrate their feedback in an effort to continue to fine-tune the software once in use? From my basic knowledge of deep learning, I know that the algorithms involved are rarely static and, in fact, generally continuously improve as they encounter new and wide-ranging data sets. Is it reasonable to expect that MedyMatch could be getting ‘better’ as it sees more broad use?

Saragnese: Continuous learning is at the very heart of the intellectual property of the company. In fact, the concept of feedback in A.I. was pioneered by MedyMatch and discussed in our patent filings. Real world feedback is critical for improvement. MedyMatch has been compared to Waze in healthcare; just as user feedback optimizes which driving routes are recommended to users, so too will the MedyMatch feedback mechanism assist in the development of the next generation of algorithms required for patient assessment.


Medgadget: Is MedyMatch designed to be local or cloud-based software, or some combination depending on the specific implementation? Are there limitations due to computational processing power or other factors as to how and where the technology can be used?

Saragnese: MedyMatch can be deployed in either a cloud or on-premises solution. Limitations of performance are self-designed and are usually limited by budget availability and local infrastructure. Customer expectations are reasonable, and patient studies can be processed in just a few minutes.


Medgadget: I imagine that identifying hemorrhages is only the first of many potential applications of the 3D deep vision platform that you have developed. Do you see an opportunity to apply the technology as a patient assessment tool for other diseases? If so, what might those solutions look like and how far away might they be?

Saragnese: MedyMatch has developed a generalized deep vision platform capable of considering the full richness of medical imaging along with any other patient data. This platform and A.I. approach will facilitate rapid discovery and decision support development. In addition, MedyMatch’s has core IP in continuous learning. This IP will allow MedyMatch to harvest only the ‘right data’ to very cost-effectively accelerate continuous learning and accuracy. Platform and continuous learning will rapidly propel MedyMatch into adjacent decision support opportunities. Yes; there are significant opportunities to apply our technology to other acute diseases. Product development is currently underway, and we expect to see our next generation of applications to be made available over the course of 2017.


Medgadget: MedyMatch states one of its main objectives to be reducing healthcare costs. Can you describe how leveraging artificial intelligence based image classification software for clinical decision support might ultimately reduce the cost of care?

Saragnese: Our goal is to deliver A.I.-based, patient-specific, clinical decision support applications to improve quality and outcomes while reducing healthcare costs. To accomplish this, we consider all of the multidimensional patient data (i.e., raw imaging concurrently with other relevant patient data at the leading edge of machine learning technologies). We want every physician to be a life-saving expert, every time. This is what drives us forward every day. MedyMatch is committed to improving real-time decisions in the acute care setting with a laser focused on those decisions in the emergency room which have the largest impact on outcomes and healthcare cost. Stroke decision support is our initial focus area due to the need to make rapid, accurate, and timely decisions. We must create capacity to care for more patients with less resources per patient, as well as access to care everywhere in the world. We at MedyMatch are committed to improving quality and reducing errors through A.I.-based decision support. This in turn will improve outcomes and reduce costs.


Medgadget: As the holiday season approaches and this year comes to a close, what can we look forward to seeing from MedyMatch in 2017?

Saragnese: Our intent is to continue to reapply our A.I. platform capability to new and diverse clinical problems with interest in continuing to build out capability in the acute care ER setting with a natural extension into trauma. Structured problem solving and collaboration are key to realizing the full potential of Deep Vision. With the right partners and data, there is a strong desire and potential to address more chronic diseases, such as neurodegenerative disease, cerebrovascular disease, and PTSD. The pipeline of potential treatments will require definitive complementary diagnosis and prognosis. This is an ideal challenge for deep machine vision and learning.


Medgadget: Thank you again, Gene, for sharing your thoughts with us! Our best wishes to the MedyMatch team, and we look forward to following along with your progress!

Saragnese: You’re welcome!



Link: MedyMatch homepage…

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Silly Putty with Serious Sensing Capabilities

Silly Putty with Serious Sensing Capabilities



Researches from Trinity College Dublin, Ireland and the University of Sao Paolo, Brazil have converted a silicone polymer, better known by Crayola’s trademarked name Silly Putty, into an incredibly sensitive strain sensor. It’s so sensitive that a piece of putty pressed against the carotid artery can detect not only the heart rate, but the blood pressure of a person. It can be made hundreds of times more sensitive than a traditional strain sensor, something the researchers demonstrated by detecting the footsteps of spiders walking over it.



The material is really just Silly Putty mixed with graphene, which is a bunch of tiny sheets of carbon one atom thick. The material is conductive and its electrical resistance varies significantly in response to physical strain put on it. A simple multimeter can be used to detect this change by placing electrodes at opposite sides of a piece of putty. Because it is so easy to use, made of cheap materials, and has high sensitivity it should find a lot of application in medicine.

Here’s a short video from Science magazine about the new material:


Study in ScienceSensitive electromechanical sensors using viscoelastic graphene-polymer nanocomposites…

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Nanopolymer PolyGPA Can Detect Elusive Bladder Cancer Biomarker

Nanopolymer PolyGPA Can Detect Elusive Bladder Cancer Biomarker



Researchers have created a new technique to detect glycoproteins in biological fluids. The Purdue University team engineered an array they called polyGPA (polymer-based reverse phase glycoprotein array) and have shown proof-of-concept experiments in using it to detect the presence of glycoproteins associated with bladder cancer in patient urine samples.

“It is possible to use our platform to identify these sugar-modified proteins as a biomarker for bladder cancer,” said Dr. Andy Tao, principal investigator of the project.

The work by Li Pan and colleagues from the Tao Lab at Purdue University was published in the Journal of the American Chemical Society. It was funded by the National Institutes of Health, the National Science Foundation, and the Purdue Center for Cancer Research.

The authors deposited nanopolymers onto a surface to capture and separate the glycoproteins from human serum and urine samples by binding to their sugar groups. They could then detect and quantify the purified samples using typical detection antibodies. Furthermore, they could detect the degree of glycosylation by normalizing this detected amount to the amount of total protein.

The impact of this work lies in detecting these elusive glycoproteins specifically and with high sensitivity. The polyGPA increased the chances of identifying proteins by 17 to 25-fold compared with conventional detection techniques, according to Tao. By separating the detection of glycoproteins into two steps, isolation and detection, polyGPA could avoid the issues of the complex mixture of competing serum proteins and the lack of glycosylation-specific detection antibodies.

Translation of the polyGPA technology is already in the works through Tao’s company, Tymora Analytical Operations. Tymora offers products to detect phosphoproteins (pIMAGO) and phosphopeptides (polyMAC).

Study in Journal of American Chemical SocietyThree-Dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation…

Via: Purdue…

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Highly Flexible and Adhesive Electronic Body Monitoring Patch

Highly Flexible and Adhesive Electronic Body Monitoring Patch

A skin-like biomedical technology that uses a mesh of conducting nanowires and a thin layer of elastic polymer might bring new “electronic bandages.” (Purdue University image/Min Ku Kim)


Researchers from Purdue University, University of Illinois at Urbana-Champaign, and Oklahoma State University have developed an electronic patch that is extremely pliable and can be applied to the skin at just about any location and remain there without causing discomfort. Unlike thin film materials, the researchers instead relied on a wire mesh to provide electrical conduction while keeping the device flexible and stretchable. They demonstrated that their approach produces a less fragile material that can withstand a great deal of stress in different ways and directions.

The conducting nanowires are around 50 nanometers in diameter and more than 150 microns long, and are embedded inside a thin layer of elastomer, or elastic polymer, about 1.5 microns thick. (Purdue University image/Min Ku Kim)The material also sticks better to the skin due to its greater surface area, helping it to naturally maintain exceptional adhesion compared to existing technologies. Because it provides direct conductivity with the skin, the researchers were able to measure electric signals from the heart (ECG) and muscles (EMG).

From the study abstract in journal Advanced Materials:

Mechanically reinforced skin-electronics are presented by exploiting networked nanocomposite elastomers where high quality metal nanowires serve as conducting paths. Theoretical and experimental studies show that the established skin-electronics exhibit superior mechanical enhancements against crack and delamination phenomena. Device applications include a class of biomedical devices that offers the ability of thermotherapeutic stimulation and electrophysiological monitoring, all via the skin.

Here’s a Purdue video showing off the new patch:

Study in Advanced MaterialsMechanically Reinforced Skin-Electronics with Networked Nanocomposite Elastomer…

Via: Purdue…

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