Nano Graphene Inc. dba GrapheneCA (“GrapheneCA”), a company that develops and produces next generation 2D materials for the 21st century, announces that it is using a new method for the planning stage that employs neural networks to extrapolate on data from just one sample.
Nano Graphene Inc. dba GrapheneCA (“GrapheneCA”), a company that develops and produces next generation 2D materials for the 21st century, announces that it is using a new method for the planning stage that employs neural networks to extrapolate on data from just one sample. Developed with investigators at the NYU Tandon School of Engineering, the system quickly formulates and provides analytics on theoretical graphene-enhanced advanced composites, thereby solving the costly and time consuming process of optimizing advanced composites for specific end uses, requiring manufacturers to test many samples to arrive at the best formulation.
“Working with the researchers at NYU Tandon’s department of mechanical and aerospace engineering, we have developed a new method for predicting the behavior of thermosetting nanocomposites over a wide range of temperature and strain rates,” said Dr. Voskresensky, Head of Research & Development at GrapheneCA’s New York-based production facility. “Furthermore, the same approach potentially can be applied to predict a behavior of thermoplastic materials,” he added. “This is a critical step towards advanced 3D composite production.”
With Nikhil Gupta, professor of mechanical and aerospace at NYU Tandon, who led the research with Ph.D. student Xianbo Xu, GrapheneCA developed data that could help manufacturers optimize the characteristics of composites for specific uses without having to perform countless costly, time-intensive testing with numerous samples. The work is detailed in “Artificial Neural Network Approach to Predict the Elastic Modulus from Dynamic Mechanical Analysis Results,” which will be featured on the inside cover of Advanced Theory and Simulations, an interdisciplinary, international journal that publishes high-quality scientific results.
“Applying an artificial neural network approach to predict the properties of nanocomposites can help in developing an approach where modeling can guide the material and application development and reduce the cost over time,” said Dr. Gupta.
“This is an invitation for manufacturers to work with us to make new graphene composites,” concluded Dr. Voskresensky, “But it is but one example of what we envision doing in cooperation with NYU Tandon.”
Grapheneca is a privately owned, commercial scale graphene and graphene-based materials producer and supply company headquartered in New York. It is dedicated to tackling the challenge of integrating graphene into real-world applications through the use of its own highly effective, scalable and environmentally friendly production process. Grapheneca has developed a production facility in New York and currently produces high quality graphene on a large scale in the form of pristine 1–3 and 5-8-layer stacked graphite flakes with less than 0.03% oxygen contamination. GrapheneCA’s proprietary manufacturing processes are efficient and remarkably eco-friendly.
ABOUT THE NEW YORK UNIVERSITY TANDON SCHOOL OF ENGINEERING
The NYU Tandon School of Engineering dates to 1854, the founding date for both the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute (widely known as Brooklyn Poly). A January 2014 merger created a comprehensive school of education and research in engineering and applied sciences, rooted in a tradition of invention and entrepreneurship and dedicated to furthering technology in service to society. In addition to its main location in Brooklyn, NYU Tandon collaborates with other schools within NYU, one of the country’s foremost private research universities, and is closely connected to engineering programs at NYU Abu Dhabi and NYU Shanghai. It operates Future Labs focused on start-up businesses in downtown Manhattan and Brooklyn and an award-winning online graduate program. For more information, visit http://engineering.nyu.edu.
Certain statements contained in this press release constitute “forward-looking information” as such term is defined in applicable Canadian securities legislation. The words “may”, “would”, “could”, “should”, “potential”, “will”, “seek”, “intend”, “plan”, “anticipate”, “believe”, “estimate”, “expect” and similar expressions as they relate to GrapheneCA, including: the applications, results and uses of GrapheneCA’s new method for predicting the behavior of thermosetting nanocomposites over a wide range of temperature and strain rates; information relating to the business plans of GrapheneCA; and the future uses of graphene, are intended to identify forward-looking information. All statements other than statements of historical fact may be forward-looking information. Such statements reflect GrapheneCA’s current views and intentions with respect to future events, and current information available to GrapheneCA, and are subject to certain risks, uncertainties and assumptions. Material factors or assumptions were applied in providing forward-looking information, including GrapheneCA successfully mass-producing graphene and graphene becoming adopted by the markets. Many factors could cause the actual results, performance or achievements that may be expressed or implied by such forward-looking information to vary from those described herein should one or more of these risks or uncertainties materialize. These factors include, without limitation: changes in law; the ability to implement business strategies and pursue business opportunities; state of the capital markets; the availability of funds and resources to pursue operations; a novel business model; dependence on key suppliers and local partners; competition; the outcome and cost of any litigation; as well as general economic, market and business conditions. Should any factor affect GrapheneCA in an unexpected manner, or should assumptions underlying the forward-looking information prove incorrect, the actual results or events may differ materially from the results or events predicted. Any such forward-looking information is expressly qualified in its entirety by this cautionary statement. Moreover, GrapheneCA does not assume responsibility for the accuracy or completeness of such forward-looking information. The forward-looking information included in this press release is made as of the date of this press release and GrapheneCA undertakes no obligation to publicly update or revise any forward-looking information, other than as required by applicable law. GrapheneCA’s results and forward-looking information and calculations may be affected by fluctuations in exchange rates.
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