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Fast fingerprinting & detection of materials using portable / hand-held devices and high performance computing for use in manufacturing and supply chain applications.

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0017047
Agency Tracking Number: 235567
Amount: $1,500,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: 20d
Solicitation Number: DE-FOA-0001794
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-05-21
Award End Date (Contract End Date): 2020-05-20
Small Business Information
17 Kershaw Ct.
Bridgewater, NJ 08807-2595
United States
DUNS: 969041057
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Vijaykumar Hanagandi
 (812) 205-8551
Business Contact
 Vijaykumar Hanagandi
Phone: (812) 205-8551
Research Institution
 Rutgers University
 Rohit Ramachandran
98 Brett Road
Piscataway, NJ 08854-8058
United States

 (848) 445-6278
 Nonprofit College or University

Material analysis is critical to enable the production of safe and quality goods and today companies use cumbersome, time-consuming, and error-prone methods to analyze materials using expensive analytical laboratories. Companies have a pressing need to measure material properties (like chemical composition and physical properties) throughout their supply chains. This project addresses DOE’s interest which is to bring the benefits of high performance computing (HPC) in manufacturing supply chains via turn-key solutions.Overall Objective and Approach: Our overall Phase I and II objective is to bring to market a cutting-edge material analysis application which produces accurate, near real-time readings. Our innovative idea and approach is to use machine learning (ML) algorithms leveraging HPC to process data gathered from handheld infrared sensors for rapid material analysis. Rapid material analysis enables companies to produce quality products safely. The confluence of HPC, ML, and handheld sensors is at the center of our innovative approach and our project will be the first one to accomplish this.Phase I R&D resulted in the development of (1) ML-based calibration models for portable sensors and (2) the technology to parallelize the training of the ML models. We demonstrated the feasibility of using HPC and portable sensors to achieve 80-times calibration speedup, 10,000-times faster analysis cycle time, and 70% cost reduction (vs. legacy lab-based sensors) without a significant loss of accuracy. We also demonstrated a latency of less than 1 second per sample analysis which enables near real-time analysis and control. Our results were reviewed by (1) prospective customers who gave us validation and encouraging feedback (see support letters) and (2) industry peers who approved our results for presentation at Pittcon-2018.Phase II work will expand our Phase I models to address an extended list of requirements (like dissolution rates of tablets, counterfeit drug detection, etc.). We will also focus on overcoming challenges including adaptation of our product for in-process use, scalability, and data security. Our target is to be ready by the end of Phase II with a prototype turnkey application running on a hosted-HPC infrastructure. Commercial Applications: Our product will be used to produce better quality and safer products. Companies will potentially save millions of dollars from reductions in supply chain costs and product recalls. Prospective customers include the FDA-regulated wine & spirits and pharmaceutical companies. Per FDA, companies recall thousands of food and drug products annually. Timely material analysis is key to reducing recalls. This presents us with a huge market opportunity.

* Information listed above is at the time of submission. *

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