Several years ago, scientists hypothesized that a narrow spectrum of ultraviolet light called far-UVC could kill microbes without damaging healthy tissue. Far-UVC light at about 222 nanometers (nm) has a very limited range and cannot penetrate through the outer dead-cell layer of human skin or the tear layer in the eye, so it’s not a human health hazard. But because viruses and bacteria are much smaller than human cells, far-UVC light can reach their DNA and kill them. In the study, aerosolized H1N1 virus—a common strain of flu virus—was released into a test chamber and exposed to very low doses of 222nm far-UVC light. A control group of aerosolized virus was not exposed to the UVC light. The far-UVC light efficiently inactivated the flu viruses with about the same efficiency as conventional germicidal UV light. Continue reading
In NCCA Tool Kit # 26, “Factors Influencing the Long-Term Performance of Prepainted Metal Building,” an emphasis was justifiably placed on the selection of materials. That selection process starts with a substrate that must provide the corrosion-resistant properties for the environment in which the prepainted product will be used, probably for decades. Whether the specifier is considering steel or aluminum, the mechanical properties of the material are of paramount importance. After all, all prepainted metal is post-formed, so the substrate, as well as the paint system, must be able to withstand the rigors of the fabrication process. With a steel substrate, it’s the thickness of the metallic coating layer; with hot-dipped galvanized steel, it’s the zinc; and with Galvalume®, it’s the Zn-Al metallic blend that needs to have an adequate thickness to provide the necessary lifetime and level sacrificial of galvanic properties. There are many parameters to consider. Continue reading
Thanks for sticking with us—this is the sixth and final post about the NCCA Color Project experiment we conducted at METALCON. In the previous five posts, we presented our analyses of the 28 observers’ ratings to see how discerning and consistent they were. We concluded that human observers see color differences differently; some see a lot of difference, some just a little. This was not unexpected. Finally, it’s time to look at the observed color differences plotted against the machine readings for color difference.
Let’s quickly review the previous two posts on NCCA’s “The Color Project”:
- In Part Three, we showed that about 20% of our observers fell into an “extremes” category (i.e., they were either notably far less critical or far more critical); but the majority—80%—of the observers were more or less in agreement.
- In Part Four, we concluded that most of the people, most of the time, were not fooled by the identical-pair panels. Only 9% of the time were there notable color differences declared, and half of the observers saw no difference at all. If you expect to see a color difference, by golly, you will!
In Part Three of “The Color Project” blog post series, we began to discuss the vast amount of data collected from the NCCA color experiment at METALCON. We also looked at how each individual observer compared to the other observers. We found that about 20% fell into an “extreme” category (i.e., they were notably far less critical or far more critical), but the majority—80%—of the observers were more or less in agreement.
Parts One and Two of this series of posts on NCCA’s “The Color Project” discussed why we needed to run a visual assessment experiment and how we structured the study. You may recall that we created 54 panel pairs, and within this set there were 15 repeats (i.e., pairs that were shown to the observers—unbeknownst to them—a second time to see how closely they would rate the pairs), as well as 8 pairs of identical panels (i.e., take a panel, cut it in half, tape the halves together, and call it a color difference pair). I also mentioned the tedium of collecting data for 13 solid hours. And lastly, I teased you with promise of revealing data here in Part Three. So, without further ado, let’s dive in. But first, let’s discuss the visual observations. We’ll talk color data later. Continue reading
In the last post, Part One, we left off with two facts: We depend on a numerical description of color and color difference rather than judging a sample vs. a standard visually; and NCCA began to investigate ΔE2000 to determine how well it might work in the coil industry.
Let’s start Part Two with a short discussion on ΔE2000. It is way more than the usual ΔE with a little “2000” as a subscript. (If only it were that easy.) Our current ΔE is a straightforward root-mean-squared calculation, as shown here:
ΔEHunter = [(L2-L1)2 + (a2-a1)2 + (b2-b1)2]1/2