Warren Jasper

The Scholle lab will be responsible for testing a plasma textile filter developed by Warren Jasper at NCSU and Stitchpartners for its ability to eliminate aerosolized coronaviruses. The Scholle lab will aerosolize the virus in a glove box with the filter, collect samples and analyze the presence of virus using RT-PCR methodology.

Several recent studies at NC State University focusing on resourcing textile and apparel manufacturing in the US indicated two major barriers: cost and quality. By combining several operations in the supply chain, companies are discovering new economies of scale. One of the most promising areas of process improvement is in dyeing and finishing Current dyeing and finishing operations are water and energy intensive. Studies have shown that most current dyeing operations could reduce total water consumption by 20%, energy by 25% and time (labor, machine utilization) by as much as 30% with no major capital outlays through strategic changes in processes, procedures and methodologies. Additionally, dyeing operations produce effluent containing salt, dyes, surfactants, and other chemicals that must be treated to meet environmental standards and regulations adding additional costs to U.S. manufacturing companies. The greatest impediment for the dyer in achieving a sustainable manufacturing process is the false impression that any changes to existing methods are costly, capital intensive or ineffective. Leading companies throughout the world are using Lean Six Sigma (LSS) to reduce manufacturing costs, improve quality and reduce cycle times. But these Lean Six Sigma methodologies have not been widely used in yarn and fabric dyeing due to the lack of instrumentation to measure and monitor two key components in dyeing: color and water. For any major quality improvement initiative, key inputs and outputs must be measured, either directly or indirectly to affect the desired change. This is a paradigm shift from current practices, where minimal data on dyes and the dyeing process are acquired and analyzed during commercial operations. There exits few instruments capable of measuring color during the dyeing process. Although many companies want to reduce water and energy consumption, they are reluctant to invest the human and monetary capital to affect changes in their current dyeing processes without proof of the results that can be achieved. There are also very few studies documenting the potential savings of using a LSS approach, ROI, and total cost savings. Thus the perceived risk in adopting a LSS approach to dyeing fabric coupled with measurements and monitoring of the dyeing process outweighs the perceived benefits and so this approach has not been embraced by the industry. This project proposes to demonstrate the actual cost savings that could be realized by a manufacturing plant by adopting a LSS approach to dyeing and implementing real-time measurements to measure dye strength, water utilization, dye-uptake or exhaustion, and fixation. Baseline measurements of water, energy, dye-strength, and exhaustion will be acquired by use of instruments capable of measuring dye absorbance in real-time. After baseline data of the dyeing process is acquired, we propose to implement an onsite LSS process with key engineering personnel focused on optimizing the dyeing process using the DMAIC (Design, Measure, Analyze, Improve and Control) methodology to reduce water, energy, and dye in production. Total cost savings will be analyzed. The outcomes will be hardened instrumentation capable of measuring dye utilization as well as water and energy utilization in the dyeing process, and a LSS methodology in place to use this data to improve the dyeing process by reducing water consumption, energy, and effluent. During these experiments we will also explore how improving the dyeing processes can lead to further improvements in the supply chain leading to new opportunities for U.S.-based manufacturing.

Grading or determining the amount of OPD is somewhat subjective, and requires a skilled technician to make this determination. The proposal will investigate ways to automate this process, and algorithmically determine to degree of OPD in a film.

Proof of principle study of using a textile embedded corona discharge in combination with optical emission spectrscopy to detect chemical and radiological hazards in air.

We propose to investigate the effects of Brownian motion and electrostatic charge (both in the particle and on the surface of the filter) on submicron particles (< 300 nm) in airflows containing laminar flow monolith filters with open channels of 0.5-5um in diameter. A monolith filter, where an aerosol flow is filtered in circular channels, insures a high surface to volume ratio, which improves overall filtration efficiencies as particle capture occurs on the surface of the membrane. Improved filtration efficiencies at lower pressure drops can be obtained by charging either the particles, the filter, or both. Charge on the filter surface can induce a dipole on uncharged particles, and electrostatically attract the particle to the surface of the monolith, thus improving capture efficiency.

The primary goal of the proposed research is to develop an accurate and precise integrated color control system that can be easily implemented throughout the US textile industrial manufacturing complex, from product designer through to merchandiser, dyer, retailer and consumer. Fundamental research in the following areas must be addressed to achieve this goal: 1) Develop illuminant data that correlate with the color rendering of lighting used in standard light booths (especially daylight simulators) and lighting used in retail stores, 2) Develop an accurate color inconstancy model and integrate it into color formulation software, 3) Establish the minimum possible error in color difference assessment via a well-controlled, statistically valid color difference experiment that is replicated by at least 3 independent observe panels from different regions of the world (U.S., Asia and Europe).

In order to move forward with this idea to the best of our ability, we need to accomplish the following tasks that we shall refer to as Phase I: 1. Complete a thorough review of the patent literature and technology. This would include utilizing University resources in terms of patent attorneys and professionals along with our own expertise and experience in searching the literature for prior art. 2. Spend significant time establishing a clear set of criteria and constraints including all expected long-term and short-term system deliverables. 3. Build a small-scale apparatus that would allow field testing to analyze: where is the field strongest; what is the range of field strengths for various transformer sizes, shapes and power usage; and what is the potential opportunity in terms of watts.

The primary goal of the proposed research is to develop an accurate and precise integrated color control system that can be easily implemented throughout the US textile industrial manufacturing complex, from product designer through to merchandiser, dyer, retailer and consumer. Fundamental research in the following areas must be addressed to achieve this goal: 1) Develop illuminant data that correlate with the color rendering of lighting used in standard light booths (especially daylight simulators) and lighting used in retail stores, 2) Develop an accurate color inconstancy model and integrate it into color formulation software, 3) Develop a perceptually uniform mathematical color space and accurate small color difference equation with visual experiments replicated by at least three observer panels. There is approximately 30% error, depending on the color difference equation and the visual dataset used, in correlation between calculated color difference and visual assessment of pass/fail of textile-based materials. There are many variables that lead to this error, and many have not been quantified, such as the degree of repeatability of visual judgments within a given human observer set, and between two or more observers sets. Hence, the minimum error that can be achieved has not been quantified. This data is essential if the US textile industry is to use an efficient and precise color management system for the textile supply chain. The lighting used in retail stores must also be quantified if the applicability of color difference formulae, which use illuminant data to predict color difference in a store, is to be determined. Furthermore, an accurate method to predict the degree to which color products will change in perceived hue, lightness and chroma under different lighting conditions is also essential. This project addresses all of these critical factors. Once the fundamental data is obtained, the project will move into the next phase, which will be to design a complete color management system that can predict, for instance, color difference, pass/fail tolerance, and color inconstancy (flare) specific to a given US retailer?s lighting conditions. The system will reduce error and improve efficiency, which are key to reducing cost and enhancing supply chain delivery times.