Using Six Sigma tools to evaluate pigment dispersant demand performance.A useful and relatively new methodology for reducing cost and improving quality is Six Sigma Not to be confused with Sigma 6. Six Sigma is a set of practices originally developed by Motorola to systematically improve processes by eliminating defects.[1] A defect is defined as nonconformity of a product or service to its specifications. . Applying Six Sigma to product design and/or and/or conj. Used to indicate that either or both of the items connected by it are involved. Usage Note: And/or is widely used in legal and business writing. process control has been done on a routine basis. We would like to take this approach into the lab and offer the coatings formulator the same opportunity to explore the benefits of Six Sigma. Therefore, in this work, we focus on evaluating dispersant dis·per·sant n. Chemistry A liquid or gas added to a mixture to promote dispersion or to maintain dispersed particles in suspension. demand of four pigments using a Six Sigma approach. Specifically, we show how to define which parameters are important to measure, how to perform the measurements, analyze the data, and make conclusions regarding the relative difference in dispersant demand between the test pigments all with a Six Sigma data-driven approach. The goal of this work is to demonstrate how coatings formulators can find benefits in using Six Sigma tools. Then, the coatings formulator can expand with confidence the use of Six Sigma to other applicable formulating processes. ********** INTRODUCTION From a methodology standpoint The Standpoint is a newspaper published in the British Virgin Islands. It was originally published under the name Pennysaver, largely as a shopping-coupon promotional newspaper, but since emerged as one of the most influential sources of journalism in the , Six Sigma is readily applied to processing plants where product quality and consistency are key production drivers. The well-defined well-de·fined adj. 1. Having definite and distinct lines or features: a well-defined silhouette. 2. step-by-step Six Sigma methodology lends itself to manufacturing facilities and the quest for Verb 1. quest for - go in search of or hunt for; "pursue a hobby" quest after, go after, pursue look for, search, seek - try to locate or discover, or try to establish the existence of; "The police are searching for clues"; "They are searching for the reducing defects while maintaining product quality. However, Six Sigma is also a powerful methodology that can be applied to various aspects of many industries. Even though the coatings industry is mature, it is the author's opinion that this industry is prime for Six Sigma. Coatings formulators have always been keen to try new and novel methods to improve the current industry. Therefore, this work offers the coatings formulator the opportunity to see how Six Sigma can be applied to couple innovation with current good industry practice. Pigment pigment, substance that imparts color to other materials. In paint, the pigment is a powdered substance which, when mixed in the liquid vehicle, imparts color to a painted surface. is a major component of the coatings formulation formulation /for·mu·la·tion/ (for?mu-la´shun) the act or product of formulating. American Law Institute Formulation . Properly using and gaining full value of the pigment is imperative in today's cost-conscious society where cost is balanced with performance. Dispersion dispersion, in chemistry dispersion, in chemistry, mixture in which fine particles of one substance are scattered throughout another substance. A dispersion is classed as a suspension, colloid, or solution. is key to good pigment performance. (1) Poorly dispersed dis·perse v. dis·persed, dis·pers·ing, dis·pers·es v.tr. 1. a. To drive off or scatter in different directions: The police dispersed the crowd. b. pigment can be directly related to poor coatings performance. (2) Therefore, the coatings formulator would like to evaluate pigment dispersion on a routine basis and in terms that relate to his/her practical needs. A typical industry method to evaluate pigment dispersion is the dispersant demand test. The information gained by performing the dispersant demand test can be related not only to pigment dispersion, but also used as an aid to determining the initial dispersant level needed in the coatings formula. Determining the correct dispersant amount required to adequately disperse disperse /dis·perse/ (dis-pers´) to scatter the component parts, as of a tumor or the fine particles in a colloid system; also, the particles so dispersed. dis·perse v. 1. a pigment leads to good formulating practice. Six Sigma is a tool that the coatings formulator can use to aid in this process. SIX SIGMA In this work, we use Six Sigma tools to determine dispersion performance of four Ti[O.sub.2] pigments. Although we apply the Six Sigma methodology to understand the difference between pigments, this same methodology could easily be applied to understand performance aspects of other coatings raw materials or overall coatings product performance. Since this work concentrates on pigment evaluation, dispersion performance is selected as the key product attribute to be evaluated. (1) The first step is to understand the Six Sigma concept. (3) Simply put, Six Sigma is a statistically based methodology that reduces variation in a product or process to less than 3.4 defects in one million parts. It is interesting to note that our common ideas of quality generally regard 99% capability, a three-sigma capability, as an extremely viable process. However, in day-to-day day-to-day adj. 1. Occurring on a routine or daily basis: the day-to-day movements of the stock market. 2. practice this would translate into an unacceptable tolerance. A three-sigma process applied to energy would give about seven hours per month of downtime The time during which a computer is not functioning due to hardware, operating system or application program failure. . Not many customers would be willing to accept this on a routine basis. The improvement from three sigma SIGMA - A scientific visual programming environment from NASA. http://fi-www.arc.nasa.gov/fia/projects/sigma/. to Six Sigma means the virtual elimination of defects in the short term. The process moves from 99.7% to about 100%. In the long term, the process moves from 93.3% to about 99.9997%. Six Sigma's objective is to reduce variation, resulting in fewer defects. One of the main goals of Six Sigma is to derive immediate benefits from statistical analysis. It is not uncommon for businesses to receive cost benefits from Six Sigma within a relatively short time period, on the order of months. (4-6) [FIGURE 1 OMITTED] The basic components of Six Sigma are to (1) Define the problem and relate it to the internal/external customer, (2) Measure how the process is performing and determine defects, (3) Analyze the root causes for poor performance/defects, (4) Improve the process and, (5) Control the problem so it does not recur. (7) This is called the DMAIC DMAIC Define, Measure, Analyze, Improve, Control DMAIC Design, Measure, Analyze, Improve, Control (5 stages of Six Sigma Quality Improvement and Assurance) Process. (7) The DMAIC process is a data-driven methodology that can be used virtually everywhere to improve a product or a process, but the work reported here takes only certain elements from Six Sigma and applies them to understand differences in dispersion performance between the selected pigments. IDENTIFYING KEY PARAMETERS The first tool that can be used quite readily by the coatings formulator is the SIPOC SIPOC Supply, Input, Process, Output, Customer Diagram or high-level process map. It is an important tool that shows the formulator the large overview of the project. In general, the coatings formulator would like to see what his/her process would entail entail, in law, restriction of inheritance to a limited class of descendants for at least several generations. The object of entail is to preserve large estates in land from the disintegration that is caused by equal inheritance by all the heirs and by the ordinary to evaluate dispersion performance of several pigments. A generic process map is shown in Figure 1 with a more relevant map given in Figure 2. [FIGURE 2 OMITTED] From the SIPOC, the formulator gets a large overview of what resources are required to complete the dispersion performance evaluation Performance evaluation The assessment of a manager's results, which involves, first, determining whether the money manager added value by outperforming the established benchmark (performance measurement) and, second, determining how the money manager achieved the calculated return . The SIPOC gives direction to the formulator as to what information needs to be collected to get his/her desired outcome--output Y. If one thinks of the output Y as a function of input variables, X, then one can use a process to determine the measurable inputs needed. This connection between inputs and outcomes is highlighted in the Process section of the SIPOC in Figure 1. Therefore, one starts by defining possible Xs. It may not be obvious what the Xs and Ys are for in our dispersion performance example. However, another Six Sigma tool can be used to help determine the input variables or Xs for our desired Y of dispersion performance. The cause and effect matrix can help determine which potential Xs are important for a given Y. Here is an example of the matrix (Figure 3) that a formulator could use for determining the potential Xs that are important to the desired outcome, Y. Using the cause and effect matrix, we are able to rank the order of importance for the input Xs and then focus our work on collecting data for these Xs to statistically determine which are the vital few Xs that determine dispersant demand and ultimately dispersion performance. MEASURING IMPORTANT PARAMETERS Now that the coatings formulator knows what information must be obtained, he/she must determine how to collect that information. Another Six Sigma tool is the data collection plan. This helps to keep all operators grounded on what to measure, how often to measure, and how to collect the data. The data collection plan is essential in obtaining good information. An example of a generic data collection plan is given in Figure 4. With the data collection plan at hand, the data can be collected and then analyzed an·a·lyze tr.v. an·a·lyzed, an·a·lyz·ing, an·a·lyz·es 1. To examine methodically by separating into parts and studying their interrelations. 2. Chemistry To make a chemical analysis of. 3. using a statistical program. For this particular work, a data collection plan was used and data collected for the four different pigments. Since we wanted to compare the different pigments in terms of dispersion performance, getting enough samples to analyze statistically was not an issue. The relevant data is given Table 1. DATA ANALYSIS After the data is collected, the coatings formulator is ready to analyze the data. This can be analyzed using any basic statistical software. For this example, data was analyzed using the Minitab Minitab is a computer program designed to perform statistical functions. It was developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr. and Brian L. Joiner in 1972. [R] program. (8) The first type of analysis is basic descriptive analysis. From this analysis, the weight percent dispersant mean and standard deviation In statistics, the average amount a number varies from the average number in a series of numbers. (statistics) standard deviation - (SD) A measure of the range of values in a set of numbers. are obtained for each sample. From the initial analysis, it appears that there is a difference in the pigments. Sample D requires the minimum amount of dispersant, followed by Sample A, then by Samples C and B, respectively. A more definitive type of analysis is Analysis of Variance The discrepancy between what a party to a lawsuit alleges will be proved in pleadings and what the party actually proves at trial. In Zoning law, an official permit to use property in a manner that departs from the way in which other property in the same locality (ANOVA anova see analysis of variance. ANOVA Analysis of variance, see there ). ANOVA can be used to determine if there are differences between the sample populations (as evidenced by the pigment's dispersion performance) by comparing the means of the populations.
One-way ANOVA: Wt%Dis versus Ti[O.sub.2]
Analysis of Variance for Wt% Dis
Source DF SS MS F P
Ti[O.sub.2] 3 0.15550 0.05183 34.23 0.000
Error 68 0.10298 0.00151
Total 71 0.25848
Individual 95% CIs For Mean
Based on Pooled StDev
Level N Mean St Dev ------*------*------*------*
Sample A 18 0.12059 0.03001 (---*---)
Sample B 18 0.21472 0.06266 (---*---)
Sample C 18 0.12838 0.02226 (---*---)
Sample D 18 0.08944 0.02710 (---*---)
------*------*------*------*
Pooled StDev = 0.03891 0.100 0.150 0.200 0.250
From the data shown above, it can be seen that to a 95% confidence level, the means of at least one of these pigment samples is different. Now if one would like to determine whether Sample D and Sample A are truly statistically different, another tool can be employed to distinguish between these two pigment samples: the two-sample t-test t-test, n an inferential statistic used to test for differences between two means (groups) only. This statistic is used for small samples (e.g., N < 30). Also called t-ratio, stu-dent's t. as shown below. Two-Sample T-Test and CI: Wt%Dis_1, Ti[O.sub.2]_1 Two-sample T for Wt%Dis_1 Ti[O.sub.2]_1 N Mean StDev SE Mean Sample A 18 0.1206 0.0300 0.0071 Sample D 18 0.0894 0.0271 0.0064 Difference = mu (Sample A) - mu (Sample D) Estimate for difference: 0.03115 95% CI for difference: (0.01176, 0.05054) T-Test of difference = 0 (vs not =): T-Value = 3.27 P-Value = 0.003 DF = 33 In this case, with the P-value p-value, n in statistics, the probability that a random variable will be found to have a value equal to or greater than the observed value by chance alone. This value provides an objective basis from which to assess the relative change in the data. < 0.05, the two-sample t-test indicates that the mean Wt% Dis of Sample D and Sample A are not equal. Using Six Sigma tools, we have with confidence determined that there is a statistical difference between the pigments. Now, the coatings formulator can use this information to adjust the dispersant level in the formula based on the pigment in the formula. In this study, we have used dispersion viscosity as a proxy for quality of dispersion. While in our experience this approach provides good direction for formulation development, the ultimate test remains evaluation of a fully formulated for·mu·late tr.v. for·mu·lat·ed, for·mu·lat·ing, for·mu·lates 1. a. To state as or reduce to a formula. b. To express in systematic terms or concepts. c. coating under typical application and drying conditions. CONCLUSIONS Six Sigma is a powerful methodology that has been successfully applied to many disciplines. Our goal with this work was to enable the coatings formulator to use Six Sigma in a lab situation. Now, the coatings formulator should see the value of Six Sigma as a powerful tool that can help in making statistically based decisions. In particular, this work has demonstrated how to define which parameters are important to measure, how to perform the measurements, analyze the data, and make conclusions regarding the relative difference in dispersant demand between four test pigments.
Figure 3 -- Cause and effect matrix.
Importance to Customer 9 3
Output
Number 1 2
Amt of
Process Input (cause) Dispersant Viscosity
TiO2 Type 9 9
Dispersant Type 9 9
Weight Percent Solids of Dispersion 6 6
Dispersing Test Equipment 0 3
Operator 0 0
TiO2 Lot 3 3
Viscosity Test Equipment 0 9
Scales 3 0
Importance to Customer 9
Output
Number 3
Time for TOTAL
Pigment to
Process Input (cause) Disperse
TiO2 Type 9 189
Dispersant Type 9 189
Weight Percent Solids of Dispersion 9 153
Dispersing Test Equipment 9 90
Operator 9 81
TiO2 Lot 3 63
Viscosity Test Equipment 0 27
Scales 0 27
Cause and Effect Relationships Scale Ratings
No relationship = 0 to Strong relationship = 9 or Weak = 1;
Moderate = 3; Stong = 9
Figure 4 -- Generic data collection plan.
Data Collection Plan
Data Collection Objective:
"To collect data to
Measure Dispersant Demand
(purpose, goal or expected outcome)
Develop Operational
Definitions and How to
What to Measure Measure
Operational Definition
Type
Measure Measure Type Data Stratification What How
Wt% Amt in Mettler
Dispersant Output Continuous Operator grams Scale
Viscosity Output Continuous Operator Amt in
cps Brookfield
for the Four Sample Pigments
(process or product)
Develop Operational
Definitions and How to
What to Measure Measure
Sampling Plan
How
Measure What Where When Many
take
readings
after each
addition and
then take 4
readings
after
Dispersant measure at minimum
wt% Amount each time of viscosity
Dispersant Added sample addition reached
take
readings
after each
addition and
then take 4
readings
after
1 min after minimum
each each viscosity
Viscosity cps reading sample addition reached
for the Four Sample Pigments
(process or product)
Develop Operational
Definitions and How to
What to Measure Measure
Collection
Collection
Measure Method Employee Name
wt%
Dispersant Manual Operator 1
Viscosity Manual Operator 2
Table 1 -- Dispersion data for four Ti[O.sub.2] pigments.
Operator TiO2 Lot Replicate Wt%Dis
O1 SampleA L1 R1 0.102
O1 SampleA L1 R2 0.121
O1 SampleA L1 R3 0.208
O1 SampleA L2 R1 0.154
O1 SampleA L2 R2 0.109
O1 SampleA L2 R3 0.126
O1 SampleA L3 R1 0.113
O1 SampleA L3 R2 0.162
O1 SampleA L3 R3 0.116
O1 SampleB L1 R1 0.180
O1 SampleB L1 R2 0.177
O1 SampleB L1 R3 0.222
O1 SampleB L2 R1 0.196
O1 SampleB L2 R2 0.226
O1 SampleB L2 R3 0.184
O1 SampleB L3 R1 0.250
O1 SampleB L3 R2 0.247
O1 SampleB L3 R3 0.439
O1 SampleC L1 R1 0.111
O1 SampleC L1 R2 0.119
O1 SampleC L1 R3 0.132
O1 SampleC L2 R1 0.128
O1 SampleC L2 R2 0.134
O1 SampleC L2 R3 0.151
O1 SampleC L3 R1 0.116
O1 SampleC L3 R2 0.147
O1 SampleC L3 R3 0.175
O1 SampleD L1 R1 0.054
O1 SampleD L1 R2 0.084
O1 SampleD L1 R3 0.077
O1 SampleD L2 R1 0.123
O1 SampleD L2 R2 0.119
O1 SampleD L2 R3 0.122
O1 SampleD L3 R1 0.084
O1 SampleD L3 R2 0.132
O1 SampleD L3 R3 0.127
O2 SampleA L1 R1 0.097
O2 SampleA L1 R2 0.111
O2 SampleA L1 R3 0.095
O2 SampleA L2 R1 0.090
O2 SampleA L2 R2 0.086
O2 SampleA L2 R3 0.132
O2 SampleA L3 R1 0.110
O2 SampleA L3 R2 0.136
O2 SampleA L3 R3 0.102
O2 SampleB L1 R1 0.211
O2 SampleB L1 R2 0.181
O2 SampleB L1 R3 0.151
O2 SampleB L2 R1 0.178
O2 SampleB L2 R2 0.182
O2 SampleB L2 R3 0.171
O2 SampleB L3 R1 0.213
O2 SampleB L3 R2 0.238
O2 SampleB L3 R3 0.221
O2 SampleC L1 R1 0.124
O2 SampleC L1 R2 0.129
O2 SampleC L1 R3 0.126
O2 SampleC L2 R1 0.118
O2 SampleC L2 R2 0.139
O2 SampleC L2 R3 0.104
O2 SampleC L3 R1 0.093
O2 SampleC L3 R2 0.169
O2 SampleC L3 R3 0.096
O2 SampleD L1 R1 0.056
O2 SampleD L1 R2 0.068
O2 SampleD L1 R3 0.062
O2 SampleD L2 R1 0.101
O2 SampleD L2 R2 0.104
O2 SampleD L2 R3 0.102
O2 SampleD L3 R1 0.060
O2 SampleD L3 R2 0.061
O2 SampleD L3 R3 0.075
Descriptive Statistics: Wt% Dis by Ti[O.sub.2]
Variable Ti[O.sub.2] N Mean Median TrMean StDev
Wt% Dis Sample A 18 0.12059 0.11180 0.11727 0.03001
Sample B 18 0.2147 0.2032 0.2047 0.0627
Sample C 18 0.12838 0.12715 0.12768 0.02226
Sample D 18 0.08944 0.08360 0.08901 0.02710
Variable Ti[O.sub.2] SE Mean Minimum Maximum Q1 Q3
Wt% Dis Sample A 0.00707 0.08640 0.20790 0.10085 0.13300
Sample B 0.0148 0.1507 0.4386 0.1795 0.2288
Sample C 0.00525 0.09290 0.17500 0.11463 0.14072
Sample D 0.00639 0.05360 0.13210 0.06175 0.11948
Variable: Wt%Dis
Sample A
Anderson-Darling Normality Test
A-Squared: 0.803
P-Value: 0.030
Mean 0.120586
StDev 0.030008
Variance 9.00E-04
Skewness 1.62963
Kurtosis 3.18029
N 18
Minimum 0.086400
1st Quartile 0.100850
Median 0.111800
3rd Quartile 0.133000
Maximum 0.207900
95% Confidence Interval for Mu
0.105663 0.135508
95% Confidence Interval for Sigma
0.022517 0.044986
95% Confidence Interval for Median
0.102122 0.129148
Variable: Wt%Dis
Sample B
Anderson-Darling Normality Test
A-Squared: 1.715
P-Value: 0.000
Mean 0.214717
StDev 0.062664
Variance 3.93E-03
Skewness 2.88037
Kurtosis 10.2207
N 18
Minimum 0.150700
1st Quartile 0.179475
Median 0.203200
3rd Quartile 0.228750
Maximum 0.438600
95% Confidence Interval for Mu
0.183554 0.245879
95% Confidence Interval for Sigma
0.047023 0.093943
95% Confidence Interval for Median
0.180363 0.223835
Variable: Wt%Dis
Sample C
Anderson-Darling Normality Test
A-Squared: 0.258
P-Value: 0.676
Mean 0.128378
StDev 0.022262
Variance 4.96E-04
Skewness 0.518415
Kurtosis 0.176405
N 18
Minimum 0.092900
1st Quartile 0.114625
Median 0.127150
3rd Quartile 0.140725
Maximum 0.175000
95% Confidence Interval for Mu
0.117307 0.139448
95% Confidence Interval for Sigma
0.016705 0.033373
95% Confidence Interval for Median
0.116840 0.136373
Variable: Wt%Dis
Sample D
Anderson-Darling Normality Test
A-Squared: 0.576
P-Value: 0.117
Mean 8.94E-02
StDev 2.71E-02
Variance 7.35E-04
Skewness 0.224976
Kurtosis -1.49325
N 18
Minimum 0.053600
1st Quartile 0.061750
Median 0.083600
3rd Quartile 0.119475
Maximum 0.132100
95% Confidence Interval for Mu
0.075961 0.102917
95% Confidence Interval for Sigma
0.020338 0.040631
95% Confidence Interval for Median
0.065104 0.110830
ACKNOWLEDGMENTS See About this product. The author thanks William William, crown prince of Germany William or Frederick William, 1882–1951, crown prince of Germany, son of William II. In World War I he commanded (1914) an army on the Western Front and was nominal commander in the German attack DeCostanza and Mark Hoffman for generating the data set and working diligently dil·i·gent adj. Marked by persevering, painstaking effort. See Synonyms at busy. [Middle English, from Old French, from Latin d to generate the information need for this paper. A special note of thanks to Marian Mar·i·an 1 adj. 1. Of or relating to the Virgin Mary, her cult, or her theology. 2. Of or relating to Mary I of England or Mary Queen of Scots. Adj. 1. Henze Hen·ze , Hans Werner Born 1926. German composer. Best known for his opera The Bassarids (1965), he has also composed symphonies, ballets, and lieder. , the author's Six Sigma Black Belt mentor Mentor, in Greek mythology Mentor (mĕn`tər, –tôr'), in Greek mythology, friend of Odysseus and tutor of Telemachus. , who gave invaluable support during the author's Black Belt and Green Belt training. And finally, the author thanks Mike Diebold For the electronic voting machines, see . Diebold, Inc. (NYSE: DBD) (pronounced DEE-bold) is a United States-based security systems corporation that is engaged primarily in the sale, manufacture, installation and service of self-service transaction systems (such as , the project sponsor and Carlos Carlos, prince of the Asturias Carlos, 1545–68, prince of the Asturias, son of Philip II of Spain and Maria of Portugal. Don Carlos, who seems to have been mentally unbalanced and subject to fits of homicidal mania, was imprisoned by his father in Verdejo, the Global Coatings Offering Manager, for their continued support. References (1) Parfitt Parfitt or Parfit is a surname, and may refer to:
, G.D, Dispersion of Powder in Liquids With Special Reference to Pigments, Applied Science, London London, city, Canada London, city (1991 pop. 303,165), SE Ont., Canada, on the Thames River. The site was chosen in 1792 by Governor Simcoe to be the capital of Upper Canada, but York was made capital instead. London was settled in 1826. , 1981. (2) Patton Pat·ton , Charley 1881-1934. American blues singer and guitarist who wrote several blues standards, including "Mississippi Boll Weevil Blues," and helped pioneer the Mississippi blues style. , T.C., Paint Flow and Pigment Dispersion, John Wiley John Wiley may refer to:
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of , 1964. (3) Anderson Anderson, river, Canada Anderson, river, c.465 mi (750 km) long, rising in several lakes in N central Northwest Territories, Canada. It meanders north and west before receiving the Carnwath River and flowing north to Liverpool Bay, an arm of the Arctic , M.J. and Whitcomb, Patrick J., "Achieving Six Sigma Objectives for Variability Reduction in Coating Formulation and Processing," Paint & Coatings Industry, November November: see month. 2001. (4) "Six Sigma Marshaling See data marshalling and marshal. (spelling) marshaling - Alternative US spelling of "marshalling". an Attack on Costs," Chemical Week, March 1, 2000. (5) "Hitting the Target," European European emanating from or pertaining to Europe. European bat lyssavirus see lyssavirus. European beech tree fagussylvaticus. European blastomycosis see cryptococcosis. Chemical News, April 3-9, 2000. (6) Challener, C., "Quality Initiatives: Six Sigma at Work in the Chemical Industry," Chemical Market Reporter, September September: see month. 9, 2002. (7) American American, river, 30 mi (48 km) long, rising in N central Calif. in the Sierra Nevada and flowing SW into the Sacramento River at Sacramento. The discovery of gold at Sutter's Mill (see Sutter, John Augustus) along the river in 1848 led to the California gold rush of Society of Quality, www.asq.org See .org. (networking) org - The top-level domain for organisations or individuals that don't fit any other top-level domain (national, com, edu, or gov). Though many have .org domains, it was never intended to be limited to non-profit organisations. RFC 1591. . (8) Ryan Ryan may refer to: Places
n. 1. A carpenter, especially a cabinetmaker. 2. Informal A person given to joining groups, organizations, or causes. , Brian The name Brian (sometimes spelled Bryan) comes from an Irish backround. It is of Celtic origin and its meaning may be "hill" or "strong, noble, and high"[1]. L., Minitab Handbook
This article is about reference works. For the subnotebook computer, see .
by Mary E. Koban DuPont Titanium titanium (tītā`nēəm, tĭ–) [from Titan], metallic chemical element; symbol Ti; at. no. 22; at. wt. 47.88; m.p. 1,675°C;; b.p. 3,260°C;; sp. gr. 4.54 at 20°C;; valence +2, +3, or +4. Technologies* Presented at the 81st Annual Meeting of the Federation of Societies for Coatings Technology, November 12-14, 2004, Philadelphia, PA. *Chestnut chestnut, name for any species of the genus Castanea, deciduous trees of the family Fagaceae (beech or oak family) widely distributed in the Northern Hemisphere. They are characterized by thin-shelled, sweet, edible nuts borne in a bristly bur. Run Plaza 709-242, Wilmington, DE 19880-0709; 302.999.2692; Email: mary.e.koban@usa.dupont.com. |
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