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Asbestos in Talc 

Session C - Interpretation of Testing Data 

November 28, 2018 

Moderator: Matthew Sanchez 

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P R O C E E D I N G S 

MR. SANCHEZ: So just as a matter to start 

out with, my name is Matt Sanchez, I am pinch 

hitting for Micky Gunter who could not make it to the 

meeting. Brooke Mossman is the co-moderator as well 

with this session. Since what we're dealing with is 

primarily microscopy results and mineral 

identification issues, I'm going to take the lead 

because that's more my expertise than hers. 

I'm a former student of Mickey Gunter's. 

I have a Ph.D. in geology with an emphasis on 

mineralogy. I currently work for a consulting firm 

and an analytical laboratory called the RJ Lee 

Group, I've worked there about 12 years now. So 

we're heavily involved with testing materials for 

asbestos, testing building materials, testing 

industrial minerals like talcs, regardless if it's 

going into cosmetics or other purposes. So this is 

kind of my background there. 

Welcome. We have some more people, that's 

good. You missed my introduction. It's okay. 

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The goal was not from us, you know, from 

the moderators, this was from the symposium 

organizers. The goal they wanted to talk about was 

established consensus on the interpretation of 

microscopy measurements for mineral fibers in 

cosmetics containing talc. 

So I had a few things in my mind to start 

out with, and then I think we can go from there with 

questions and just see where we go with any kind of 

confusion that may be out there that we can help 

with. 

The first thing I wanted to do was in all 

the meetings this morning and all those talks, 

nobody ever defined a mineral, which I found 

interesting. I think as we evaluate any type of 

data for what we're looking at here, we're looking 

at minerals, whether we want to call them asbestos 

or not, that's another issue. 

When we're just dealing with the mineral 

identification, that has to be evaluated on any of 

the microscopy results. Does the microscopy results 

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or other test data give us enough information to 

actually identify the mineral? 

So to that end, a good working definition 

of a mineral is something that's naturally 

occurring, it has a unique crystal structure, and 

then it's got a relatively unique chemical 

composition. 

So it's very important to evaluate 

different test methods, especially microscopy 

methods, first of all, can the instruments do it, 

second, if the instruments can do it, were the 

procedures in place appropriate and adequate to 

actually do it. There's kind of two steps there. 

And just as a matter of discussion, and 

kind of maybe some more background here, there are 

lot of asbestos testing labs in the United States. 

Most of the people in these labs have -- they've 

attended five-day training courses on how to 

identify asbestos, that's -- that can lead to a 

lot -- well, it gives you a larger base of people 

that can analyze for asbestos, but it's a large base 

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of people who don't know the fundamentals of either 

the equipment they're using or the fundamentals of 

the materials they're even looking at. They've been 

taught to identify five things, generally speaking. 

So as you get into these other types of 

materials, especially environmental samples -- or I 

guess I'll limit myself to talc, when you're talking 

about things that contain talc, but more than that, 

the content -- the goal was, you know, talc in 

cosmetics, there's all sorts of other minerals that 

get thrown into cosmetics as well, depending on the 

application, there's micas, there's calcium 

carbonates, all sorts of things get used. 

And so, you know, are those other mineral 

additives being added in, are those being 

appropriately screened, are they appropriately being 

analyzed for -- so there's not misidentification. I 

think those are all very important points when we're 

looking at interpretation of testing data. 

I don't know if there's any questions. I 

can keep going all day. Does that raise any 

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questions from the audience before we move on? 

SPEAKER: I know we always talk about 

cosmetics, but I'm from the FDA so the drug testing 

has been routine for the drugs. 

MR. SANCHEZ: Well, sure, and I --

SPEAKER: -- one is a different set of 

testing than we have. 

MR. SANCHEZ: Yeah, that's a good point. 

SPEAKER: And actually I also want to 

broaden it to say that not what we want to -- but 

really there are a variety of -- not just FDA 

regulates, but that we see in commodities that may 

be outside of the FDA's certification. Should there 

be a uniform way to be able to assess talc in all of 

these products and the minerals that maybe the 

contaminants within the talc so that instead of 

narrowing it to cosmetics, broaden it out for all 

products that would contain talc itself? 

MR. SANCHEZ: Yeah, I think that's a good 

point, and part of my basis of defining what a 

mineral was is that context. It doesn't matter what 

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the material is, if you're using the technologies 

that we have appropriately, you can identify any 

mineral in any type of matrix, whether it's a talc, 

a mica, dirt outside or wherever. You know, 

depending on what those matrices are, other minerals 

may be present that complicate the analysis. 

You heard of the one this morning that 

they kept talking about, Anthophyllites in talc, but 

there's -- in other systems, there could be other 

things that look very similar, so you have to take 

additional, you know, analytical steps in what is 

standardly done by routine asbestos testing 

laboratories. 

SPEAKER: I think the most -- but what 

instrumentation do they need, what do they use to do 

their testing? 

MR. SANCHEZ: Most of them use either PLM 

or TEM, so most of the analytical laboratories are 

set up to work under the AHERA Regulation, which was 

passed back when I was young back in 1987. And the 

AHERA is meant for -- it stands for Asbestos 

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Emergency Response Act or something, so they set up 

a whole testing regime of, you know, PT rounds and 

round robins all involved with that that's 

administered through an organization of NIST called 

NVLAC. 

But the methods that are used are 

primarily an EPA test method which uses PLM, 

polarized light microscopy, so when you have the 

bulk samples as part of that protocol, you use PLM. 

Once you've identified asbestos in a room and 

they've gone in and they've removed it, then they'll 

clear -- they run the air samples using TEM to make 

sure that there was nothing left in the air so 

people can go back in and occupy it. 

When you get out there with testing talc, 

some people are only using TEM; some people are 

using PLM; some people use a combination. And I 

know there was a comment made earlier -- I forget 

who made the comment, and maybe it's not important, 

but they talked about PLM, meaning polarized light 

microscopy, as like not being a sophisticated 

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technique, and that's not true, each of the 

techniques are very sophisticated. 

You know, mineralogy is a complex science. 

You know, what you do a mineralogist is you're 

trying to describe nature. You know, nature is 

incredibly complex. So there's areas in mineralogy 

that are very -- you know, you can make general 

statements, but then when you get into some specific 

areas, there can be a lot of disagreement. 

One of the areas where there's general 

agreement in mineralogy is like what do you need to 

identify a mineral, like that -- there's general 

agreement on what you need to identify a mineral. 

Once you get into the realm of whether an individual 

particle may be asbestos or not, that's much more 

difficult to answer there at the extreme. 

So maybe I should walk through some of 

this. Historically in talc, especially with the 

cosmetic grades in the '70s, there were a few things 

that were proposed. What was eventually settled on 

for better or for worse was using powder x-ray 

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diffraction as like an initial screening tool. 

Does anybody know what that is or -- some 

general idea. I'll keep it basic, I'll just do a 

brief review. So one of the attributes of a mineral 

is the crystal structure. So to get measurements of 

the crystal structure, we generally use some 

diffraction techniques. So powder x-ray diffraction 

allows us to take measurements of the crystal 

structures of what's in the powder. 

So the approach in the '70s that was 

settled on by industry, and FDA approved of it, I 

guess, they were involved with the discussions, they 

were using powder x-ray diffraction to screen for 

any amphibole and serpentine minerals. So amphibole 

is relative to amphibole asbestos; serpentine is 

relative to chrysotile. 

So if you're seeing either of those two 

mineral phases from the crystal structure point of 

view on x-ray diffraction, you would then follow 

that test up with light microscopy, meaning 

polarized light microscopy, in order to determine if 

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those amphiboles or that serpentine was, in fact, 

asbestos or not. 

Questions? 

SPEAKER: The sensitivity of that 

technique, what percentage of amphibole and 

serpentine could it detect in the powder? 

MR. SANCHEZ: And that's a good question. 

So with XRD it really depends on the individual 

operators and how they were running their equipment 

and the type of equipment they would have had. The 

standard is like the CTFA J4, and the standard said 

you had to be at least down to a .5 percent level, 

0.5 percent. 

So I've seen test data from the '70s where 

some labs were much better, maybe down to .1 

percent, but .5 was at least the minimum standard to 

run that procedure. And that's just inherent in the 

instrument because even today we can't really get 

better than that, especially in the talc matrix. 

You can get a lot better if you can -- you know, if 

you can dissolve 90 percent of the material and 

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weight and analyze it, you'd be much better, but 

with something like talc, you can't do that. 

SPEAKER: I would ask if anybody's using, 

you know, x-ray diffraction to do this, if you get a 

negative, you don't see it, is the line drawn there 

and the sample is allowed to proceed? 

MR. SANCHEZ: Yeah, so the way the method 

is written, that CTFA method, is if you have the 

negative XRD you can stop. But in practice it would 

depend upon, you know, the people doing it whether 

they did more. So I can speak from personal 

experience that there were multiple companies in the 

'70s that were doing much more than just XRD, that's 

the minimal standard. I'm sure there are plenty of 

other people that only did that, but it would be 

specific to an entity. 

And I said that earlier I think in my 

comments, you know, depending on who the mining 

company is or who the company that may be buying the 

talc, they could be just doing the minimal 

requirement or they could be doing much more, it 

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1 really depends on the individuals. 

2 SPEAKER: Speaking of the mining 

3 companies. From what I understand, you can fairly 

4 easily tell where the deposits that are going to be 

5 getting the amphibole containing deposits versus the 

6 straight talc, how do they do -- is that -- are they 

7 using some type of like handheld device or how are 

8 they doing -- measuring those conditions when 

9 they're out there in the mines, where to stop and 

10 where to keep going? 

11 MR. SANCHEZ: Yeah, I can't talk 

12 specifics, some of the work that I do is actually 

13 going to talc mines, and I'll describe what I do 

14 when I go, if that helps. Again, I can't speak, I 

15 don't know what company A or B would do --

16 SPEAKER: It's more than I know now. 

17 MR. SANCHEZ: So as a geologist, one of 

18 the things we like to do is, you know, go outside, 

19 that's why we chose to do geology as opposed to 

20 something else, but actually what we do when we do 

21 these assessments of these mines, we go to the mine, 

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we -- depending, sometimes they're underground, most 

of them are open pit, but we actually walk the face 

of the mine, we walk the areas of the mine, we look 

at the rock that they -- you know, there was a 

comment made earlier, too, about like something 

about blasting the talc, and I've never been to a 

talc mine that didn't blast, so I don't know where 

that information is coming from. 

Most of the talc that they're mining is a 

very compact, dense rock. I have a big piece of it 

on my mantel, I think it's very beautiful. But they 

are blasting, you have piles of material that are 

loosened by blasting, you know, we climb over those 

piles, we pick through those piles, and what we're 

looking for is one of the -- you know, they use the 

term common amphiboles. 

Common amphiboles, that term is just 

referring to -- all sorts of rocks contain 

amphiboles, and so from a geologist's perspective 

and mineralogist's perspective, the idea that 

somehow just an amphibole in and of itself is 

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somehow harmful, doesn't make sense because it's 

everywhere, we're all exposed to amphiboles. We're 

just exposed to amphiboles of compositions that 

don't match the regulated specified, so nobody's 

ever looked at them, that's the kind of situation we 

live in. 

So there's this unknown quantity of how 

much amphibole people may be breathing in. I've 

tested soil samples, you know, here in D.C. outside 

the IRS building and it contains amphiboles, right, 

elongated amphiboles in the soil, they were not 

asbestos, but they were amphiboles that were 

elongated. So you know, some of these decisions are 

important, just as that as a piece of the content. 

SPEAKER: Along that line, talc that does 

not contain detectable levels of amphiboles, what are 

the methods we use whether its XRD or something 

that's more sensitive? I gather that from 

discussions this morning about amphibole type and 

serpentine type minerals are the -- in this case, the 

only source of -- potential sources for 

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asbestos. 

So if those types of minerals were 

demonstrated to be absent by appropriately sensitive 

techniques and a particular limit that someone might 

want to set, then we can say that that talc at least 

would be reasonably clean of asbestos or whatever 

standard we set. 

MR. SANCHEZ: Yeah, well it's interesting 

the language you used because that absence of 

asbestos test, I mean, that's the language --

SPEAKER: It's like proving a negative. 

MR. SANCHEZ: Yeah, I think --

SPEAKER: But we do it all the time --

MR. SANCHEZ: Well, I've seen meeting 

minutes from the 70s of you know they have talc 

miners and companies using talc and the FDA, and 

it's like the FDA were the ones that imposed that 

language early on, but all it means is within the 

parameters of that test, nothing was detected. 

So whenever we're testing for things, 

there is -- we can't test to zero, right, we have to 

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live in this de minimus world to some level where we 

find it acceptable. 

One of the issues we have on the 

analytical side is I can analyze a sample to any 

level you want me to, but the levels we operate 

under are typically for the EPA error regulations 

one percent; for OSHA labeling laws and regulations, 

it's .1 percent. So you can go a couple of orders 

of magnitude beyond that, fine, but is that enough. 

Somebody could always make the argument we 

didn't test enough. So I think from a side of --

because, yeah, I mean, it's one thing -- if I don't 

see any amphibole here of one part per million. If 

I go down to one part per billion, will I find it? 

I don't know, possibly, but does that matter. I 

think that that's an important piece that is not 

being -- I can't address that. 

SPEAKER: What I'm looking for is, you 

know, for -- I'm with FDA, but I'm on the 

methodology and office of regulatory science, and 

it's our people that are going to be doing 

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potentially some of the testing if the FDA gets into 

this. And so I'm trying to look at, you know, 

potential screening options. 

And you can look at it a couple different 

ways, you can either identify this is a problematic 

sample, put this over here, or you can try and come 

up with something and say, okay, this step within a 

certain level of tolerance, let's say from the 

morning talk, base that level of tolerance, this 

stuff is good to go into market; this stuff for 

whatever paremeter we use, whether we're testing 

amphiboles or serpentines or calcium or iron or 

whatever, this is going to need more testing. 

So you can look at either you try to 

identify the problem stuff right off the bat or try 

to identify the good stuff that's safe and get that 

in the market quicker. 

MR. SANCHEZ: Now, yeah, and I think the 

real -- again, I've been testing talc in a 

laboratory now for over 11 years and I've only had a 

couple of occasions where talc came through that 

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actually contained asbestos, and both those times 

they were imported talcs -- well, one time it was an 

imported talc out of Northern China. There's 

different areas in China that mine talc, some are --

they're very different geologically. 

And the other one was actually out of a 

Death Valley mine, I don't know if it's the same one 

that Van Gosen mentioned, but those were not for 

cosmetic purposes, they were for industrial 

purposes. But generally the talcs that I've tested 

from the United States from the operating mines have 

all been clear. 

So I think in a lot of areas of the world, 

depending on how developed they are and how tight 

the -- you know, the process controlling, I think 

there's some good -- you're talking about weeding 

out like problems, I think the question comes into 

what's coming in out of Pakistan. Like I have no 

personal knowledge of anything in Pakistan. 

Twenty-five percent importation of talc from 

Pakistan, what's in that stuff, I don't know. 

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SPEAKER: And we have -- you know, on the 

food side we have manufacturing practices, and 

taking that same approach, getting industry to 

prevent the problem before it comes out and we have 

to deal with it, that's --

You know, that prevention, preventative 

control approach, things like that, solves a lot of 

problems for the industry and for us, saving the 

American people, you know, time and money of us 

going out and collecting samples and testing them 

and all that kind of stuff. 

MR. SANCHEZ: Yeah, I know, I know. 

SPEAKER: And charging that against the 

general fund, and it also keeps the industry, you 

know, fairly fluid and, you know, minimizes the need 

for us to get in. So thinking about preventative 

controls, that's kind of what I'm getting into and 

that kind of also goes back to what you were talking 

about just then about that Chinese mine and about a 

problem, that kind of goes back to my question about 

how do we -- talc from various mines. 

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Apparently they've got ways of delineating 

clearly in the mines where to go and where not to. 

MR. SANCHEZ: Yeah, well, from just the 

mining side, so, you know, you go, you walk the 

faces, and so the formation of amphiboles depending 

on the deposit, could be something - a lot of 

deposits they mine today really don't have any 

amphibole in them, and Brad talked about the 

Southwestern Montana mines, you know, one of the 

issues is the --

So composition is only factor in what 

controls what minerals may be present somewhere, the 

other factors are temperature pressure conditions. 

So when you're in talc deposits where the pressure 

temperature conditions are very low, geologically 

speaking, you generally don't have any amphiboles 

because amphiboles don't form under those conditions. 

So where you find the amphiboles, these are 

these higher pressure temperature environments, they 

talked about the one area in Death Valley and 

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you talk about, you know, Upstate New York talcs 

where you can get quite a bit amphibole depending on 

the deposits. 

When you actually go into the mines 

themselves, generally when -- most, not all --

again, there's always exceptions to all general 

statements, I don't know if we all appreciate that, 

but generally speaking, when asbestos is forming in 

nature, it's forming as -- it's not forming as a 

primary like mineralization with the rest of the 

rock, it's usually forming at some bit of an 

alteration, some secondary mineralization affect. 

Typically those are occurring along fault 

zones or other areas in the rock that are undergoing 

some kind of shear or tensile strength, tensile 

pressure type of environment. So if my hands were 

the rock, where you -- you know, from like a fault 

zone what you would have is you'd have rocks that 

are slightly passed each other. 

So as you have that fissure in the rock 

and those rocks sliding past each other, the 

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temperatures and pressures in that localized zone is 

very different than just the stuff a foot away 

sometimes. So as that rock goes, the fluid flows 

all the way in and along the fault surface, it has 

the elements it needs and in the right conditions, 

and you could form asbestos in those environments. 

Other environments where the rock are just 

pulling apart like a dilation, so as that rock pulls 

apart, that interstitial area gets filled with 

fluids from the surrounding rock and then it will 

crystalize stuff out of that. So, you know, the rock 

is pulling apart, so at that second mineralization 

is where the asbestos is occurring those kind of 

features. 

So the first place you look for asbestos 

in the mines is your looking for those fault zones, 

you're looking for those features. And generally 

speaking, you know, if you go up and they blast a 

section of the mine -- I've never seen this at a 

talc deposit, I've seen this in other just like 

aggregate quarries around the United States -- if 

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there's an asbestos vein there, the rock breaks 

along the asbestos vein, it's a plane of weakness. 

So you could like walk up and there's big, 

hairy rock sitting there. The whole rock isn't 

hairy, it's just the surface where it broke away 

because you actually had like an asbestos 

formitization happening. 

So, you know, that is a lot of what goes 

in mines. I can't speak for every mine, but 

ideally, you know, they're walking the faces, 

they're evaluating, they know if there's any problem 

areas and they're not mining that area, ideally. 

Who knows, right. But I've been to talc mines where 

there's actually nothing there. 

I have been to a talc mine in South 

America where there was one zonation in the mine, a 

clear fault zone, clear offset at both sides of the 

rock along the fault zone and there was asbestiform 

everywhere, and they did not mine that area, they 

mined -- it was five meters on each side, they waste 

all that material. 

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And in the testing of the talcs from that 

deposit, we never saw that material ever in the 

talc. So it seems to me like with the testing and 

looking at the procedures, that they were adequately 

not including that in their mining product. But not 

every mine would even have that, so it really 

depends on the specific mines and their locations 

and how they handle it on the mine side. 

SPEAKER: So asbestos basically forms in 

veins? 

MR. SANCHEZ: Generally speaking, yes. 

There are other occurrences and -- you know, some of 

the debate of the -- and it's funny because we 

talked -- you know, they talked about Death Valley 

talcs and Vanderbilt -- Vanderbilt Northern New York 

talcs, none of those talcs were used for -- none of 

them are used today. 

And to my knowledge, I don't think any of 

those talcs were ever used for pharmaceutical or 

cosmetic purposes, but I could be wrong on some of 

the Death Valley ones, but my understanding is those 

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New York talcs were never used for the purposes of 

the meeting today. 

But there's a question about 

anthophyllite, whether the anthophyllite is an 

asbestiform kind or just regular, but when you 

have -- this is just a nuance, and it gets 

complicated at times, because you can have a 

mineral, let's say, in a deposit which is amphibole 

from like an earlier formation event, so it's a nice 

amphibole, again, non-asbestiform. 

But then as that rock underwent other 

metamorphic conditions and turned into like a talc 

deposit, the amphibole could be partially turned in 

to talc. And the way that those alterations happen, 

meaning the amphibole turns into talc because it was 

subjected to a different compositional pressure 

temperature environment where talc was the --

You know, the phase of the equilibrium, 

not the amphibole, you can see the amphibole is 

forming talc, if that process doesn't go to 

completion, you can create some very 

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1 interesting-looking like pseudo amphibole talc 

2 particles. And so the interpretation of those 

3 things are complex, they're not that common, but 

4 they do occur. 

5 So a lot of that gets back into are the 

6 techniques being used to identify that sufficient in 

7 order to see those nuances and to understand what 

8 you're dealing with. And, you know, what do you do 

9 with those particles? I don't know. 

10 Those talcs were used in Stanton's work, 

11 they didn't cause any problems in the rats or 

12 whatever Stanton was using. 

13 Brooke, you talked about some of the talcs 

14 from R.T. Vanderbilt in your study. 

15 MS. MOSSMAN: Right. Stanton looked at a 

16 number of the fibrous talcs, samples from that area, 

17 and someone else who has looked at it in a different 

18 species, was W. Smith, and they did the lifetime 

19 studies and showed that the fibrous talcs didn't 

20 have carcinogenic potential as did the asbestos 

21 amphiboles in their model. 

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But they made a comment actually --

MR. SANCHEZ: But those particles looked 

like asbestos. 

MS. MOSSMAN: They do. 

MR. SANCHEZ: The first time I looked at 

one, I was like, oh, my God, it's asbestos 

everywhere in this stuff. 

SPEAKER: That gets back to what you were 

saying this morning about the chemical composition, 

particularly the iron -- the presence of iron. 

MS. MOSSMAN: Right, right, that's just 

one of the differences. 

SPEAKER: And my other question is I want 

to clarify something, that talc itself does not 

contain calcium or iron. 

MR. SANCHEZ: Not in any meaningful 

amount, no. 

SPEAKER: Define meaningful amount. 

MR. SANCHEZ: Well, I mean, if I go in 

like a soil sample I'll find lead, it doesn't mean 

it's --

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SPEAKER: Yeah --

MR. SANCHEZ: So, you know, within 

anything you could find -- if you look hard enough, 

you'll find something, but generally speaking --

SPEAKER: So a true talc deposit that you 

wouldn't expect to find any amphiboles in, you're 

not going to find any -- almost -- it depends on 

what methodology you use, but almost undetectable 

levels of iron and calcium. 

MR. SANCHEZ: In the individual particles, 

yeah. It's interesting, there's some -- there's 

some -- depending on the nature of the deposit, so 

if you're looking at talcs that are derived from 

like ultramafic deposits, you're going to find --

you can find more things like chromium and stuff 

involved with those talcs relative to other types, 

and that just deals with what the original 

composition of the rock was that had formed the 

talc. 

But that doesn't necessarily get into 

these issues of health affects, but talc itself --

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so they mentioned the term solid solution -- I don't 

know -- does that mean anything to anybody? 

SPEAKER: Well, glass. 

MR. SANCHEZ: Well, let me try to -- I'll 

try to define it a little better. So the term 

tremolite and actinolite was thrown around earlier 

today. The chemical in them or chemical formula for 

tremolite is Ca2(Mg,Fe)5Si8O22(OH)2. The way it 

works is within that crystal structure I can start 

substituting iron in for the magnesium, and nothing 

changes to the crystal structure, so it remains an

amphibole. 

So between like tremolite and actinolite, 

you can just keep throwing pretty much iron in there 

and at some point mineralogists have decided that at 

that point it becomes actinolite -- you know, on one 

side of the line it's actinolite, on another side of 

the line it's tremolite. So those names for those 

two minerals are somewhat arbitrary, that's set by 

us, I mean, humans, that's part of our nomenclature. 

But then the solid solution would just 

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mean that we have three exchange of magnesium and 

iron and no really change in the mineral, it still 

remains crystal structure and amphibole. 

Talc does not have that same process, like 

there is no -- I can't just start substituting iron 

in for the magnesium, the crystal structure doesn't 

allow it, there's not enough variation and 

flexibility for the crystal structure to do that. 

So talc is either just pretty much primarily 

magnesium silicate, the other -- or it's like all 

iron, and that's another mineral called monosulfide, 

which is a -- talc act work back in the '70s, but 

there is no continuum between those. 

So some mineral groups have that solid 

solution, which makes the naming convention a little 

more -- what's the right word -- they're just 

arbitrary points we pick. Generally speaking in 

mineralogy we use the 50/50 rule, with the exception 

of the tremolite and actinolite, which is not the 

50/50 rule for historical reasons. 

Anyway, so that's the idea of the solid 

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solution. I forget why I was getting to solid 

solution though. I forget why I brought that up, I 

thought I had another point to build on. Shoot. 

SPEAKER: One question going back to 

presence of the iron and calcium, so do you 

generally screen -- and we talked a little bit about 

this before, so you do a general screen and there 

are -- minerals, does it make sense to look also for 

iron and calcium, and if you find iron and calcium 

in the sample that you're doing, that that would 

take you more towards that this is more likely going 

to be asbestos than just -- talc? 

MR. SANCHEZ: Not in and of itself. I 

think the complicating factor is in any talc that 

you look at, it's not 100 percent pure talc. So the 

phases that you would usually -- you would generally 

encounter, there's a bunch of them, but from a --

you know, how much, you encounter a mineral called 

chlorite, it's very similar in its crystal structure 

to talc, but typically in the iron in the rock would 

be in the chlorite phase. 

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So if there's a chlorite component, you're 

going to get an introduction of both aluminum and 

iron into the system. So the measurement of just 

like iron alone wouldn't give you a measurement 

specific to like an amphibole or possibility of 

amphibole. 

SPEAKER: But would the presence of 

aluminum help be a marker to disregard that iron 

presence? 

MR. SANCHEZ: Well, I mean, let me --

SPEAKER: If it's coming from a chlorite, like you 

just said, is --

MR. SANCHEZ: Yeah, but usually the 

amphibole itself could have iron or no iron, so you 

don't -- without knowing what amphibole you're 

dealing with beforehand -- so from a purely 

unknown --

So if you had a deposit where you knew 

there was an amphibole and a chlorite in there, and 

the chlorite had general -- you had a pretty constant 

composition of the ratio between the 

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aluminum and the iron and the chlorite, yeah, you 

could measure iron and chlorite -- sorry, aluminum 

and iron by the ICP methods or some other method, 

correct that out, take your remaining irons and 

assign it to the amphibole. 

But you'd have to know that that would be 

a correct assumption, and I think if you're looking 

at unknown talcs from god knows where, I don't think 

that gets you anywhere. 

We have toyed with the idea in the past of 

using calcium as a measurement for the tremolite, if 

there was tremolite present, and that's a little 

more useful, I think, but that only takes care of 

the tremolitic or calcic amphibole component, but a 

lot of talcs also -- well, all talcs will contain 

some amount of either like calcite, which is calcium 

carbonate, dolomite, which is calcium magnesium 

carbonate, and possibly some magnesite, which is 

magnesium carbonate. 

You could remove those to get an acid, you 

know, different acids will remove those out, so you 

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could remove those that way and then run like a 

calcium as an upper limit of how much tremolite 

could be there, for a calcic amphibole at least, but 

if you really want to have specifics, unfortunately 

you've got to look at the individual particles, and 

that's by microscopy so --

SPEAKER: The type of amphiboles that form 

at these various fracture points, is there a 

difference when you've got a fault zone that's 

coming -- you know, impacting or coming together 

versus one that's going apart? 

MR. SANCHEZ: No, it's --

SPEAKER: The same kind of amphiboles? 

MR. SANCHEZ: Yes, if you're going to be 

forming amphiboles in an environment, they're 

generally going to be mineralogically fibrous 

amphiboles. And you saw some of the data, and some 

the nuances here is, you know, we talked about the 

cave crocidolites being -- as you look at the 

individual fibers and those bundles being much finer 

grades than the amosites or these other types. 

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So when Ann Wiley was talking about the 

specifics of the deposits, yeah, you could have -- I 

mean, I've seen tremolite asbestos out in a quarry 

which was, you know, fine as fine can be when you 

actually start looking at the individual fiber 

width. You can go to other places and it's a very 

fibrous amphiboles still, but if you go look at the 

widths of the fibers, there's a huge range. 

So even when you have these occurrences 

along faults, we have these fibrous materials, but 

they're not necessarily equal, and there could be 

multiple stages of pulling apart. Where the first 

stage didn't create a spine of a fiber and the 

second stage did, so you could have an imprint of 

the original -- of the material at the center of the 

vein being dimensionally different and it was just 

aggregating from those outer areas. 

But these are kind of extremes, right, 

we're kind of talking about extreme exceptions. 

SPEAKER: I was just wondering if the 

different conditions at those fault zones, whether 

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it's pulling apart or coming together, you've got a 

lot of -- when they're pulling apart, is what's 

filling that gap, is that primarily from water 

intrusion and bringing in minerals and --

MR. SANCHEZ: Well, it's literally the --

believe or not rocks in the earth like -- they're 

wet, so as those gaps get opened up, it's like a 

funnel, it's like the pressure to force the water 

into those gaps, those waters -- and under those 

pressures and temperatures have -- you know, they 

have a lot of soluble elements. 

And so when these things form, they're 

forming from that solution based upon the 

temperature pressure changes that are all of a 

sudden -- you know, all of a sudden water is to dump 

all these elements, it can no longer hold them and 

they form different minerals. If the conditions are 

right, you can form asbestiform. 

Another conditions that can form is like 

sepiolite or palygorskite, which are very fibrous, 

look like asbestos morphologically, but their 

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1 crystal structure and compositionally they're 

2 different. And in the health studies, there's been 

3 no association of adverse health affects. I've seen 

4 more sepiolite in talc mines than I've ever seen 

5 asbestos. 

6 SPEAKER: They were also mentioning 

7 fibrous talc this morning. How does that form? 

8 MR. SANCHEZ: There's all -- you know, 

9 that's a -- that's a poorly defined word. In the 

10 Vanderbilt deposits we talked about where you have 

11 anthophyllite, in my experience -- and I haven't 

12 done much -- I've never really done any work 

13 directly with the Vanderbilt deposits, I have been 

14 there and looked at the rock. 

15 But you can go up to the rock and what 

16 looks like a -- doesn't have any appearance of having 

17 any fibrocity or asbestiform character, it's an 

18 amphibole -- looks like an amphibole, but then you 

19 can scratch it with your finger, meaning it's talc. 

20 So we call that -- so talc can almost 

21 completely replace that amphibole, and that 

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replacement prospect creates a very fibrous talc. 

So in the context of the R.T. Vanderbilt talks of 

fibrous talc, that's what it's talking about, it's a 

replacement texture of the talc after the amphibole, 

it creates a very funky material. 

SPEAKER: Sounds like a 

long identification process. 

MR. SANCHEZ: Yeah, another thing to think 

about talc, and this is what I more commonly see in 

samples, talc is like a plate mineral, so if I 

literally like sheets of like a ream of paper like 

this, and as you crush it, it just like rips apart, 

right, and you create all these kind of plate-like 

particles. 

Talc has this perfect cleavage in this 

direction, so it just wants to break along them; 

however, if you were to take this, turn it up on 

edge and then look down on it, right -- so this is 

how you typically would look at it on the microscope 

in this orientation, very clean, if it was held up 

on edge and then it was bent a little bit, you would 

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see the separations of those cleavage planes, and 

that looks like -- that looks very asbestiform. 

So fibrous talc, depending on what it 

actually is you're looking at, could be very 

different things, it could actually just be normal 

talc up on edge or it could be these fibrous talcs 

as a replacement texture from the alteration. And 

if you're just looking at a sample without more 

knowledge, you can't always make those distinctions. 

And, you know, these are very detailed 

types of analysis to do it, and really just to tell 

that apart you would perform very precise 

diffraction analysis by TEM and look for both phases 

being present in the particles. And most people 

can't -- that's not a routine analysis. 

SPEAKER: I mean, my questions are geared 

toward trying to identify a potential screening 

process. So the last thing we have to do is go down 

that very detailed type of analysis to determine 

whether or not you've got a problem. We don't want 

to be doing that for each sample. 

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MR. SANCHEZ: Well, you wouldn't find the 

people to be able to do it. 

SPEAKER: Exactly. 

MR. SANCHEZ: Yeah, and I think from a 

practical standpoint -- and I gave a couple 

presentations a few years ago at SME, Society of 

Mining Engineers meeting, about either, you know, 

when you're using PLM or TEM to do these analysis, 

what kind of data must be required to do be reported 

with the results. 

So if you look at the methods for 

polarized light microscopy that exist, there's very 

stringent rules of what optical properties by PLM 

you have to measure. So you have to measure the 

refractive index in two directions, that'll 

differentiate talc from serpentine; that'll 

differentiate -- mainly talc from serpentine, as the 

system we're dealing with here. But then you get 

into other measurements, something called bayer 

cohesins, you look at the morphology, you look at 

something called the extinction. 

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You know, Ann Wiley was talking about how 

easy it is by PLM to tell talc and anthophyllite 

apart, by in PLM, that's based upon refractive 

indices; however, when you look at the difference in 

the refractive indices of anthophyllite and 

tremolite, they have the same range. But -- but 

using -- but there's another measurement called the 

extinction angle which you can use to differentiate 

tremolite from anthophyllite. 

So there's a lot of nuances to these 

analysis based upon the minerals you're looking at 

and what data must be recorded. And most of the 

standard methods require all these things to be 

reported, it's just a matter of the analysts that 

are supposed to record all these things, do they 

even know what it means in context of the mineral 

identification. 

Because there is it has this, it has this, 

it has this, therefore, it must be it, but there's 

some nuances in there that they -- some pitfalls if 

you don't understand the system. 

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So from the PLM perspective, and this is 

what we're working on in the USP Expert Panel, I 

don't know if I disclosed I was part of that, is a 

PLM method that requires, you know, photograph -- so 

if you see an amphibole in the sample, you would 

photograph it in all sorts of orientations on the 

PLM and then in different light modes, in essence, 

that you use on polarized light microscopy. 

Where we'd actually take color photographs 

to document those features; therefore, if somebody's 

looking at that report, they can see what the 

morphology of the particle was, they can -- and you 

can see it clearly that by PLM again it's -- the 

scale of PLM is such that the distinction between 

asbestiform amphibole and non-asbestos amphibole is 

pretty trivial in most cases. 

So you can see whether or not it was 

asbestiform, you can check the refractive indices 

measurement to see if it was a reasonable conclusion 

based upon what they called it, you can check that 

extinction angle to make sure that it was actually a 

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tremolite versus an anthophyllite. So the idea would 

be that the methods require much more of the backup 

data as part of them. 

SPEAKER: It seems like the sample 

preparation might be critical because depending on 

how you -- I don't know how much sample prep is 

involved with this --

MR. SANCHEZ: For PLM with an already 

ground powder, very little. You're literally just 

taking little scoops and putting them on glass 

slides and looking at them. 

SPEAKER: Okay. Then, you know, they 

talked a lot about particle size this morning, have 

you all ever looked at a sample prep as potentially 

a way to possibly enhance the percentage of 

amphiboles in your sample? 

MR. SANCHEZ: Yeah, we have, and you can. 

And there's not -- the consequence of doing 

something like that, there's a lot -- if you're just 

looking for amphibole, that's fine. I have found 

though -- because to go through like you have your 

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heavy liquid separation, for instance, 

centrifugation, you know, washing it, centrifuging 

it again, it's not like those are hard steps, but 

those steps take time. 

So let's say you take ten milligrams of 

sample, put it in the little centrifuge vial, spin 

it a couple times, whatever you do to get it, you 

could just physically look at that same ten 

milligrams and be done before you got done with your 

prep. Does that make sense? 

So if I'm only looking at ten milligrams, 

I can look at ten milligrams on two to three slide 

mounts and I would have looked at everything that I 

could have concentrated by centrifugation. 

SPEAKER: Well, what I'm looking is that 

can you use that centrifugation step as a way to 

potentially have a way of having enriched or 

enhanced isolation of the amphibole, potentially 

asbestos containing amphiboles, and use that as a 

potential way to enhance your ability to properly 

quantitate the --

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MR. SANCHEZ: I think you're on the right 

step there. Because in all these analysis we have, 

there's the identification issue, then there's the 

quantification issue. 

SPEAKER: Right. 

MR. SANCHEZ: In my experience using real 

world samples of talc, I don't need to centrifuge 

them and heavy liquid separate them to find 

amphiboles if they're present. But in order to get 

much more reliable quantitation of those materials, 

doing something like a centrifugation would 

definitely get you there, you'd be able to constrain 

your quantitation and reduce any errors of that 

measurement. 

SPEAKER: So actually in our conversation 

when I was at NIST -- Paul Brown from the FDA -- I'm 

a toxicologist, so I'm thinking I need 

quantification information in order to use this to 

measure because it's important to figure out what 

are the safe particles, what are the unsafe 

particles. 

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That's what we're to potentially use this 

analytical data is okay, a consumer product has this 

much talc in it, we need to now how much fiber is in 

there so I need to do the calculation to see is that 

safe or not. And whatever methodology in the 

interpretation of data is guiding that 

quantification, I heard in this morning's session --

but having that sort of data for us is critical, 

it's that quantitative aspect so that we can do that 

daily dose calculation and risk. 

MR. SANCHEZ: Yeah, there's a few points 

there. Let me go -- I'll loose my points if I -- if 

you go again, sorry. You know, the concept of 

whether it's fibers per gram, fibers per particles, 

I mean, it doesn't really matter, you would analyze 

it the same way, it's just literally how you report 

out the data. 

But, you know, as we know, a number means 

nothing without a comparative purpose. So right now 

the only regulations we have to deal with are all 

weight percents. So to move away from that, there's 

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no way to compare what you have with any existing 

regulation. 

So without something to -- that meaningful 

comparison of a fiber per gram, I don't see the 

utility in doing it, right, there's no -- yeah, you 

get a concentration, but I can use the same data and 

calculate out a weight percent. It's the same --

you would analyze it in the same fashion to get 

either sets of data. 

So there's really not a pro or con of 

either one, it's just a matter of what do you have 

to compare it with. The issue with doing -- you 

know, if you have an unknown sample that comes in, 

you have to screen it for chrysotile still -- or 

chrysotile, however you say it, people always 

correct me if I say it that way --

You know, you can't -- the density 

differences between talc and chrysotile are nil, 

there's no way to separate those two out. So if 

you're going to separate your amphiboles or heavy 

minerals from the talc, you're also not going to be 

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able to analyze for the chrysotile. 

So from a routine standard of analyzing 

these samples, to go straight to that centrifuge 

technique eliminates your -- or only to do that 

would eliminate your ability to test for chrysotile, 

which is also a concern for people. 

But I think as a -- you know, you've 

analyzed the sample, you've identified something's 

there, if you need better quantitative data, 

especially if you have the amphibole component, then 

the next step is do like a centrifugation or 

something makes a lot of sense to get much more 

quantitative data of what you know is already there. 

The other thing I wanted to mention was 

right now we're -- so, well, let's go with yours --

SPEAKER: Getting back to the discussion 

about the chemical composition, to do that kind of 

centrifugation step and look at the various layers, 

and you could do a elemental analysis and we could 

quantitate how much calcium, how much iron and other 

elements are in those various layers. 

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And based upon the discussions that I 

heard this morning, those that had calcium and iron, 

you want to focus on counting those particles in 

that layer because that's going to give you the best 

opportunity to detect and quantitate any asbestiform 

fibers in that sample. If calcium and iron are not 

in those various layers, then the particles that are 

in that layer aren't going to be an issue. 

MR. SANCHEZ: Well, you could have 

anthophyllite that has no iron, you could have 

another mineral phase coming from that that doesn't 

have any iron, and you'd miss it if you relied on 

the chemical technique there. Really, unfortunately 

we're -- I mean, the -- it's sounds like the 

strength and weakness of microscopy. 

Microscopy is like the only analysis that 

allows you to see the particle and then get specific 

information of the particle. The problems is, based 

on time constraints you just can't look at that many 

particles. 

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SPEAKER: See, that's what I'm trying to 

get at is, you know, you do that separation, one, 

you increase the chance you're going to find 

something, and so that should make the microscopy 

easier, so that's kind of what I'm looking at. And 

the chemical analysis, so what you're saying is that 

there are some amphibole particles that do not 

contain calcium or iron? 

MR. SANCHEZ: Or very little iron. No 

calcium and very little iron. 

SPEAKER: Do those also produce 

asbestiform particles? 

MR. SANCHEZ: Potentially, yes. 

MS. MOSSMAN: In which types? 

MR. SANCHEZ: Specifically anthophyllite 

would be one, anthophyllite Mg2Mg5Si8O22(OH)2. 

There's generally some iron in it, but there are 

known locations where you have very like -- no iron 

anthophyllites, so you could be dealing with an 

amphibole with, you know, less than a weight percent 

of iron in it, you know, just a few weight percents 

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of iron there would be a very small amount of iron 

in that type of analysis. 

Cummingtonite is another amphibole with 

generally the same -- has the same chemical 

composition as anthophyllite, it's just the crystal 

structures are different. But from a compositional 

standpoint, they'd be -- they'd be identical. 

SPEAKER: The refractive index of those 

minerals versus talc, are they the same? 

MR. SANCHEZ: The refractive indices of --

you could tell those apart from talc, no problem. 

When you get into looking at the refractive indices, 

the cummingtonite is what's called a monoclinic 

amphibole, so if you were just looking at PLM data, 

it would probably report out as if it was present as 

a tremolite, if you only had the PLM data. 

Refractive indices-wise it overlaps with 

tremolite, it overlaps with anthophyllites, that 

extinction angle I measured would be the same as 

tremolite. 

SPEAKER: That would be different than 

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talc? 

MR. SANCHEZ: Yes, it would be different 

than talc, that's not a problem. 

So, I mean, right now in some samples that 

I've been analyzing, we actually do have -- these 

are some historic samples, but there are some 

cummingtonite amphiboles there, not asbestiform, but 

they're cummingtonite, but all the testing records 

of the people that are testing these things always 

reported out whenever they found something, as 

actinolite. But it was only the optical data, but 

if you actually go isolate these particles obtained 

in the compositional information, they don't have any 

calcium. 

So there's a lot of -- to get back into it, 

were the analytical methods enough to really be that 

specific in their identification, sometimes they're 

not, I mean, if you evaluate like older data and try 

to -- and for me what's important is when I see like 

discrepancies between results, I'm always trying to 

resolve those, right. If I'm seeing like PLM in something that's 

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clearly not an asbestos amphibole, but then 

somebody's reporting on TEM finding asbestos 

amphiboles, like that's a discrepancy, are they just 

counting elongated fragments of amphibole in 

asbestos, I don't know. 

So these are all these kind of -- you 

know, I'm constantly trying to rectify conflicting 

data sets, whether it's historical data reporting 

one type of amphibole, but then actually looking at 

older samples and it's another type of amphibole, 

there's usually logical reasons why these -- why 

these misidentifications occur, but it's like a 

constant issue that I deal with. 

SPEAKER: But amphiboles that do not 

contain calcium or very little iron, are they 

generally -- I know you've looked at a lot of talc, 

are those primarily in industrial talcs, have you 

seen any cosmetic pharmaceutical-grade talcs? 

MR. SANCHEZ: I have not -- generally I've 

only ever seen anthophyllite as a general rule in 

the standard ilk talcs we were talking about. There 

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are other talc mines in the U.S. though that report 

out anthophyllites, these have all been closed for 

decades, so I don't think they're really that 

germane, but the issue is that if the unknown talc 

sources coming in, you know, it's like you've got to 

be looking for it to make sure -- if you don't --

Pakistan sending us talc, I don't -- you 

know, maybe some of the Pakistani talc is really 

good, maybe other stuff is really crappy and really 

bad stuff. And so without knowing more, it's like 

you've got to be looking for all these things when 

you encounter these unknown samples. 

And from a laboratory, most of the talc we 

get in, we don't know where it comes from, we just 

get some talc sample, we don't know if it's 

originated from Pakistan, India, China, U.S., South 

America, Europe, we don't know. We just -- we run 

it as a blind sample to us and report out our 

findings. 

Or it's not uncommon -- I'm not saying it 

always happens, but it's not uncommon for things to 

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be blended to get to certain desirable properties 

for these uses. A lot of times that's controlled by 

the color, so a lot of stuff using like ceramics or 

paints, they need a certain whiteness of the talc. 

So if you have too much like chlorite and 

these other minerals, the whiteness isn't where you 

need it, so you'd blend in a much whiter talc into 

it in enough proportions to get your talc to the 

spec that they need pass it for. 

So there's all sorts of reasons to blend. 

It's always portrayed some evil dilution issue, and 

that's not the case, it's been my experience it's 

usually to meet some other -- some physical 

requirement of the end user. 

I'm not sure what time it is. 

MS. MOSSMAN: Yeah, we're over. 

MR. SANCHEZ: We got started about 15 

minutes late. 

MS. MOSSMAN: Right, and it's 2:35. I 

guess the question that I would have is, what do we 

want to do a group regarding recommendations. 

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And, again, this is not exactly my 

bailiwick, so it's totally naivete, but it seems 

that there is expertise in this room in terms of 

either identification or what Michael was trying to 

get at, it would be nice to have a tiered approach 

to finding this material where you can have 

different tiers to say, look, this is reasonable to 

stop here, this won't be a problem. And maybe 

that's just an idealistic solution. 

MR. SANCHEZ: I don't think it is because 

let me just talk back to the USP Panel, because 

right now we're looking at the -- the real change 

we're looking to put forward to the expert committee 

for approval is we will be doing x-ray diffraction 

still, because it provides much more information 

about all the minerals present than just doing the 

microscopy analysis alone. 

And there's other attributes of talc that 

people are concerned with other than just simply the 

asbestos side. But we are going to do -- there will 

be a mandatory microscopy methodology, and right now 

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the idea would be PLM, and I think TEM will be on 

there as well. I don't see any reason not to do 

them both. They both look at very different 

fractions of the size of the particulate. 

So one of the issues is, you know, as Ann 

was saying, she pulled out this old body powder and 

it's big particle sizes, so the concern of like 

missing small stuff is very minimalized when you're 

dealing with courser grinding talcs that are 

typically used for body application. 

When you get into the cosmetics, I'm not 

sure what you all use, if they're courser or finer. 

When you start getting to the pharmaceuticals and 

the peels -- a lot of times they're using like what 

they call these micronized talcs where they're 

ground to very, very fine powders. 

So a testing methodology for like a body 

powder type using like XRD and PLM, I think would be 

very sufficient, I mean, you would get the 

information you need from that. But once you move 

into all those micronized talcs, I think you'd have 

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to get into the realm of electron microscopy, you 

know, to do your due diligence to rule out -- just 

based on the particle size differences. 

But that's the approach we're taking in 

the USP expert panel right now, and we're also 

working on another methodology for the 

quantification of the asbestos or amphibole in the 

talc sample where we're hoping right now -- we've 

created a series of standards and we're just waiting 

for the bureaucracy to move, which has been five 

months, in order to get these things separated to 

different labs and those people that are involved, 

to do like a round robin to see how reliable it is 

and how well that will work for these lower level 

concentrations. 

And we're up to pretty low levels, I think 

the lowest fiber maybe is like 0.0004 percent. So 

we're taking this orders of magnitude lower on this 

particle kind of method validation. And the idea 

there is the way the quantification will be done is 

you would be scanning over a known amount of 

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material, so you'll weigh the amount you put on your 

slide, and then scanning over the minimum of three 

slides, any particle you'll see, you'll measure the 

length and the width. 

And then based upon other things, you can 

calculate out its volume, apply density and you can 

actually build a mass. So we could actually get a 

mass by mass concentration, which by doing it by 

that methodology, you can get a very low kind of a 

sensitivity by doing it that way, but again, the 

question is these low levels of homogeneity and how 

reliable and how reproducible, I don't know. 

But we're looking into that to try to draw 

a way of quantifying to much lower levels so we're 

not just left with it's less than .1 percent. I 

mean, it could be parts per trillion, it could be 

parts per billion, but they're being reported out as 

less than .1 percent, so there's a lot of unknown in 

those kind of data. 

SPEAKER: I work for the FDA also. We're 

kind of all over. I have 15 years in the asbestos 

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research for the last 28 years with the FDA, and one 

of the concerns I have -- and I'll just take it one 

go way back out the door to the practical side, this 

gentleman here alluded to it already, we want to be 

able to screen, we want to be able to identify 

quickly -- correct me if I'm wrong -- and we do 

consider -- I deal certainly -- I'm with the 

Forensic Chemistry Center, and we deal with 

counterfeits, you know, paper stuff like that. 

But we handle all ports of entry, we have 

special agents that are there, we send out --we 

actually go from our lab, we take it and fly out to 

these different places, use devices that we've 

designed in our laboratory to screen these cargo 

containers. Take one scientist who's never seen a 

cargo container, put him in -- or her in -- and tell 

them to sample everything in there, that's the rest 

of their career in some cases, I mean, it really, 

truly is. 

So take think one of those -- well, 

somebody says, we'll take that container and that 

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one up there, you open it up and it's front to back 

with materials, say, okay, take that box and that 

box, that's all you have time to do. 

So we need something, and I don't -- I 

agree totally about the XRD, that's got to be some 

way that we can make that portable --

MR. SANCHEZ: PLM you could do -- I could 

do PLM for you in that cargo ship right there. 

SPEAKER: Exactly, and I'm the same way, 

as a microscopist I couldn't endorse more the use of 

PLM, I mean, that's my go-to, but how many places 

outside are going to be able to have a TEM to haul 

around with them, there's no such thing. 

MR. SANCHEZ: Well, the TEM is very 

impractical for these things. 

SPEAKER: It is. I couldn't agree more. 

MR. SANCHEZ: You know, you can make 

calculations on sensitivities in different ways, and 

I could make PLM look better than TEM from a 

sensitivity perspective, but that's not the point. 

The true sensitivity of a microscopy method is how 

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many particles you actually look at. 

So I made the comment earlier, and by PLM 

you're able to screen -- again, it's not a lot of 

material, but you're still -- you know, as I said, 

you could screen ten milligrams a sample in an hour, 

no problem. You could not do that by TEM, there's 

no way. 

And then when you're actually looking at 

the particles you're at, 2,000 X, 10 to 20,000 X by 

TEM. Most of the particles in the talc sample are 

too big to even analyze, so you're only ever looking 

at the finest, the smallest of the small particles, 

and then based on the constraints and how they have 

to be laid out, you're not looking at very many of 

them. 

But then you go through these calculations 

and these scale ups to get to these big numbers when 

you've only ever looked at 1,000 particles total, if 

that -- I'm just saying, I mean, 100 discrete 

particles in a TEM grid opening, you do ten grid 

openings, you've looked at 1,000 particles, your 

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true sensitivity was one particle out of 1,000. But 

then you're going to try to turn that into some kind 

of a part per million analysis. 

SPEAKER: And you're going to spend a 

couple days doing it. 

MR. SANCHEZ: Well, you could, depending 

on what you see, it could be very time consuming. 

Where the PLM, you're physically looking at so much 

more material, so many more particles, it's a much 

better measurement. But again, you know, the TEM 

can see small particles, especially if there's some 

chrysotile present, PLM could miss chrysotile more 

likely, that's not necessarily the case for 

amphiboles. 

But I think there's reason to do both, but 

PLM looks at a lot of material and I think it's a 

much better general instrument for analyzing for 

asbestos or amphiboles or the things in a talc 

sample. It's not the only answer, but I think it's 

the most robust tool to use. 

Does that help? 

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SPEAKER: Yes. 

MR. SANCHEZ: I've just been kind of 

going. 

MS. MOSSMAN: We should have moved groups. 

MR. SANCHEZ: Nobody's come in, so I 

don't -- I'm waiting for someone to stop us. 

Well, let me just back up, so the other 

thing with the TEM though with our approach is again 

the transparency of the data where if you're seeing 

something, there's always going to be the image of 

it. When you get into measuring the composition, 

that's done by EDS, there's a readout for that, that 

would be saved. 

And then when you get into the diffraction 

work, which for certain minerals is critical, the 

anthophyllite talc issue, the sepiolite and talc 

issue and anthophyllite differentiations, all of 

those things are done by the crystal structures, you 

have to do the diffraction work. 

And measuring 5.3 per gross bases does not 

do it, you need to manipulate the particles, make 

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measurements, compare them to standards. 

SPEAKER: Question about the technology. 

There are optical scanners that have been developed 

to help do things like filth analysis, pick out 

particles and insect parts in a batch of rice or 

whatever. Has that kind of technology been applied 

to scanning these SEM monographs and helped with the 

quantitation process? 

MR. SANCHEZ: Not necessarily that 

technology. We've employed on numerous occasions 

some automated SEM techniques to measure particles. 

We were able to take an image and then obtain the 

EDS, the chemical compositional information. 

When we're dealing with low 

concentrations, we get the same issue when we were 

talking about with TEM, you can make the dispersions 

of these powders, we go through and analyze 10,000 

particles and we don't find any. So 10,000 

particles isn't a lot of particles when you're 

dealing with something that's been ground to like 

20, 30 micron medium-sized diameter. 

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So the amount of like memory and computing 

power to run those SEM automations to a 100,000 

particles in order to detect something at much lower 

concentrations is somewhat of an obstacle for us, 

you know, the machine's tied up for ten hours, and 

then all our computers crash when we try to like 

summarize the data. 

SPEAKER: Well, you could also have that 

sort of angle with like sampling, proper sampling 

protocols and things along those lines because, you 

know, you can sample everything, but at some point 

you get a case of diminishing returns, so at that 

point you stop and then you're going to get the most 

and best data out of it. So you can kind of 

mitigate those issues with proper sampling. 

MR. SANCHEZ: Yeah, again, if you're only 

looking for amphiboles, you could do those 

separations and help that out, but then --

SPEAKER: From my point of view, I'm 

looking for that screening and if we can use 

something, a couple methodologies, elemental analysis 

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along with, you know, some way to detect some of 

anthophyllite amphiboles as well, if you can 

eliminate the presence of that, then we've got an 

initial screen. 

And all that would do within certain 

parameters would tell us that this talc is 

reasonably safe and should go to be put out on the 

market. Anything that fell outside of those 

parameters, then you would go to --

MR. SANCHEZ: Do extra. Yeah, do more 

work when you have something outside --

SPEAKER: Confirmation analysis. 

MR. SANCHEZ: Yeah, and we've looked at 

using bulk chemical composition to actually try to 

trace -- there was a paper done by Mickey Gunter's 

student, it was also -- Marty Rifkin gave the talk, 

who was his undergraduate student, Marian Buzon was 

her name, she's down at a university down in 

Atlanta, I forget the name of it. 

But she was getting talc samples from all 

over the world looking at the bulk chemical 

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composition and trying to see if you could cluster 

them and get some kind of indication of where they 

may come from, you know, just based on the bulk 

analysis like that. 

I think you need to get more specific but, 

yeah, there's all sorts of ideas, you know, how to 

do these things. But I think -- I mean, PLM, as far 

as just like a quick screening method is probably 

the best based upon the data, you can do more or not 

do more depending on how confident you are in what 

you saw or what you didn't, if you're talking about 

quick, you know, routine screenings. 

SPEAKER: You can set up an EDS or WES 

system for prep, but my experience with the EDS 

mostly, but if you can -- rather than crashing your 

computer looking at tens of thousands of particles, 

you can specify, I want this particle type, I want 

it to be iron rich to within these percentages, I 

want --

MR. SANCHEZ: Oh, yeah, we --

SPEAKER: And you can also set it with a 

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top discriminator, once you get ten of these 

particles or a hundred whatever, you know, stop. 

MR. SANCHEZ: And we have played with that 

because you can -- yeah, you can set up thresholds. 

The issue at hand though is it still needs to stop 

on a particle collection of data to know whether to 

reject or accept it. 

SPEAKER: And you can set the dwell time, 

it's not -- with gunpowder residue, and I know this 

for a fact, I'm on the NIST committee for that -- it 

is done exactly the same way, you set it to particle 

type --

MR. SANCHEZ: It's funny you mentioned 

that because the company I work for, they do a lot 

of gunshot residue and a lot of the technologies 

they use for the automated analysis looking over the 

bariatric material more typically --

SPEAKER: Bariatric --

MR. SANCHEZ: But it's kind of going along 

with --

SPEAKER: But it works. I mean, you can 

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do a particle -- to answer your yes or no question 

in microseconds with the new SDD detectors on these 

things, so it is very, very fast. 

And again, you set all your 

discriminators, how many particles, what -- and you 

can have it throw out everything else that comes 

along, and you could log up to multiple mounds of 

preparations and put it in -- like most of them in 

the crime labs, they'll set it up before they go 

home, and they come back the next night. 

And then you say, well, how do I know the 

machine really got that, each particle is identified 

and it's up to the operator which one of those --

you get the new kid on the block who's just training 

to go in there, and they have to go back to this 

particular amount to this particular coordinate, 

there's a particle, put the needle on it and confirm 

that it's there. Because everything we do goes to 

court. 

But it could be -- this could be done that 

way, at least I can't see a reason right now why it 

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couldn't. 

MR. SANCHEZ: Well, the real issue is 

the -- with the SEM alone you don't get any of the 

crystallographic information. 

SPEAKER: Correct. 

MR. SANCHEZ: So any time you have a 

longer, thinner talc particle, what is it, is it 

talc or anthophyllite. You can't answer that 

question with EDS alone. There's another technique, 

and we've been trying to work on it, we've had some 

success here and there by SEM, it's an older 

technique, it's called electronic backscatter 

diffraction. 

SPEAKER: Oh, yeah. 

MR. SANCHEZ: It's typically been used in 

like metal analysis, we have nice polished surfaces, 

they can determine orientation of grains, strain 

rates and all sorts of good things by changes in the 

crystal structures. We've had success on some 

particles being able to use EBSD on our filter 

preparations in talc in order to get that 

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information. 

The issue is we don't have any -- right 

now there's no way to have that part of the 

automation. So you go back and you look at 

particles and then you apply the EBSD, but it's like 

it either works or it doesn't. But there's other 

techniques that could be developed definitely, and I 

think get much better as time goes on. 

SPEAKER: EBSD wants to do with big data, 

yeah, do this building, but it's still not there. 

MR. SANCHEZ: Yeah, and for this type of 

analysis, this is a pretty novel approach when you 

get the types of particle analysis like this. 

So I'm not sure if we should break or wait 

for them to come. I don't know what you guys want 

to do. 

MS. MOSSMAN: We probably should. 

SPEAKER: I just -- I'm also from FDA, so 

I'm a toxicologist, and depending on whatever 

methods biochemical chemists determine is going to 

be the best method, is someone either on USP expert 

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panel or with this group, we need someone to 

integrate with the toxicologists to determine that 

whatever sensitivity you have for the analytical 

essays, if we take our drug products or cosmetic 

products that have the highest exposure, that we 

need to find a sensitivity that's going to assure 

that the amount of asbestos that the person is going 

to be exposed to is going to be okay. 

So I just want to put that in there 

because I can see there were a lot of work that has 

to be done on the analytical side, but --

MR. SANCHEZ: Well, that's another 

issue --

SPEAKER: What's really important that we 

need from you is a target. Because the testing has 

to meet the regulatory expectations and 

requirements. So what we need from you guys is a 

limit of concern, a level of tolerance, whatever 

terminology you want to use, give us that target, 

and then it's up to us to come up with something to 

meet that. 

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MR. SANCHEZ: What we're trying to do 

right now is create a methodology with 

quantification that takes us orders of magnitude 

further down the chain from .1 percent where we're 

kind of living now -- from the x-ray diffraction 

standpoint alone. 

A lot of people have been doing microscopy 

analysis for a long time, but that industrial 

standard of the USP current one is the x-ray 

diffraction, then you follow it up with microscopy 

if you see something. We're going to -- with the 

benefit of microscopy which will introduce -- we'll 

get much lower than that, but ultimately, you know, 

you could have something in the material, but that 

doesn't mean it generates an exposure for a 

toxicological affect. 

SPEAKER: So .1 might be okay, but is 

someone working with you on the USP expert panel to 

figure out if that's okay? 

MR. SANCHEZ: No, we're just simply doing 

a methodology to a reasonable level that we view 

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much below any kind of a current regulatory level 

for quantification purposes and reporting purposes. 

SPEAKER: So when do you get people 

involved in that --

MR. SANCHEZ: I mean, frankly if you say 

you want something at ten parts per million, we 

can -- we'll do what we can to get a method that's 

reliable at ten parts per million. 

SPEAKER: It might be that .1 or 1 is 

actually perfectly fine, we could probably figure 

that out to an accepted --

MR. SANCHEZ: Well, there was work done --

SPEAKER: The FDA creates -- and so maybe 

you and I can get together and I can help --

MR. SANCHEZ: You're Jeff. I didn't 

know -- now I make the connection, okay. I 

appreciate your help a few months ago. I'm sorry it 

didn't work out. I'm just sitting on all those 

samples. I don't know what to do with them. 

SPEAKER: Actually, what I was going to 

say is that that's part of the reason why there's a 

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need for a work group, to try to address some of 

these questions that will arise as a result of this 

meeting for the agency. 

And not just FDA, but for all of 

government to sit together and to try to come up 

with what seems to be a reasonable level that we 

should use to have some kind of consistency amongst 

agencies to use to say that is a limit or threshold 

that we should be using for our products, for EPA to 

do standards for, whatever else, so that we're all 

at least working on a consistent detection as 

possible and we need our data to be consistent 

because of the varied needs and products that we're 

all looking at and how to approach it. 

Because, you know, there's not -- not 

every application can fit into a little mold, so 

that's part of the discussion we need to have. 

MR. SANCHEZ: Yeah, I think we've said 

this in the USP meeting, it's like -- yeah, we're 

trying to design something that would be efficient 

and down to very low levels, whatever that means in 

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the broader world, we can't answer. 

SPEAKER: Our focus has been to come up 

with the best method, not to look at the toxic part, 

but it's an important part, we need to know that. 

SPEAKER: There's a -- in my office is if 

you don't have to go down to -- because that's going 

to be way over what --

MR. SANCHEZ: Well, the plaintiff lawyers 

will say you never do enough. 

SPEAKER: You even said this morning, 

you've got to know the particles that are .1 microns 

to five microns, I mean, that's pretty sensitive, 

and when you need to be able to test those 

particles, and what percentage of those particles is 

in there that's causing cancer, so you've got be 

able to detect those particles. And what percentage 

of those particles is in there that's causing 

cancer, that's what we're trying to determine, and 

in order to do that, we're consolidating --

MR. SANCHEZ: It's not --

SPEAKER: -- and then you take the tox 

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part from that to determine, you know, if -- percent 

of these particles, is that going to be cancer 

causing, I don't know. 

MR. SANCHEZ: It's a fascinating thing 

because most of the epidemiological studies and 

things have been associated with some of these talc 

mines, particular the ones in Italy, I know there's 

been ones in Vermont done, Norway -- I think there 

was one done in Norwegian talc miners, and they 

don't see disease, and some of those deposits do 

contain amphiboles, not asbestos, but they do 

contain amphiboles --

MS. MOSSMAN: Yeah, there's no 

mesothelioma, although the workers get mild 

chalcosis indicating that they're levels of exposure 

are high, historically. 

SPEAKER: So then it comes down to a 

combination, and is the combination --

MR. SANCHEZ: Well, are there other -- are 

there other like specific mineralogical questions 

you guys may have that I can help with? Or I mean, 

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I can talk more detailed about methodologies, you 

know, something like a PLM, what it can and can't 

do, try to get some more information there. 

SPEAKER: How quick are some of these 

methods to actual products where they're --

MR. SANCHEZ: Yeah, that's a good 

question. The biggest issue is if you're dealing 

with some kind of a cosmetic, which has a lot of 

like -- you know, I don't wear makeup, but there's a 

lot of other organic things added to those, right, 

you know, for masking purposes, a lot of those 

things can help mask the particles and make it 

difficult to see things like the refractive indices. 

So whenever we're dealing with like 

cosmetics, which these are different organic binders 

and different colorants added in, you know, we're 

always like ashing these things, so we're putting 

them into low temperature ashing conditions to burn 

them off. 

Occasionally you'll get particles along 

like titanium biopsy, samples along like titanium 

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dioxide, depending on how fine that is and how much 

is in there, that could also create difficulties in 

seeing natural particles themselves and getting 

clean measurements from them. 

But those are the main -- and that is this 

pharmaceutical cosmetic grade talc and uses, I think 

those are the main two issues that are typically 

with the cosmetic side where they're used makeup. 

SPEAKER: The low temp grade and if you 

were just getting rid of the organics, and for the 

purpose of doing it -- the low temperature ashing 

will cause the fibers to fracture. 

MR. SANCHEZ: Especially if you're doing 

any --

SPEAKER: And they will break just as the 

process of --

MR. SANCHEZ: But -- and if you're 

actually dealing with an iron amphibole, it can 

actually change -- it changes the oxidization state 

of the iron and can change the refractive indices 

measurement slightly with that change. 

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So there's a lot of papers like on heat 

treated like amebocyte, because it looks very 

different once you've heat treated it, and so if you 

don't know what it looks like as it changes through 

that process, but when you're dealing with things 

like anthophyllite, tremolite, it's not necessarily 

a big concern but there could be some subtle changes 

that get made that should be accounted for. 

It's mainly an issue with chrysotile or 

really high iron. 

SPEAKER: So is there a plan to come up 

with a separate testing method? 

MR. SANCHEZ: The USP is only 

pharmaceutical grade talc. So we're not in the --

we're not thinking about end views or formulations, 

accounting for that. So the USP guys are -- they 

are specific in the meetings that we're only talking 

about pharmaceutical grade talc. 

SPEAKER: So you can control the quality 

of the talc at the level of the drugs --

MR. SANCHEZ: So to design certain 

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preparation procedures would be beyond what USP 

understands their role to be. Not that we couldn't 

do it if we were asked to, but that's not 

necessarily on our radar to take into account those 

complications. They made a distinction that I don't 

know is a real distinction in the working world. 

I think we should go ahead and break and 

see if we're done. 

(Session concluded at 3:03 p.m.) 

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Page 84 

1 State of Maryland, to wit: 

2 

3 I, Jean M. Townsend, a Notary Public of 

4 the County of Montgomery, do hereby certify that the 

5 within-named witness, personally appeared before me 

6 at the time and place herein set out, and after 

7 having been duly sworn by me, according to law, was 

8 examined by counsel. 

9 I further certify that the examination was 

10 recorded stenographically by me and this transcript 

11 is a true record of the proceedings. 

12 I further certify that I am not of counsel 

13 to any of the parties, nor in any way interested in 

14 the outcome of this action. 

15 As witness my hand this 28th day of 

16 November, 2018. 

17 

18 Jean M. Townsend 

19 Notary Public 

20 My Commission expires: 

21 October 8, 2021 

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[0.0004 - apart] Page 1 

0 8 ago 41:6 76:17 

agree 62:5,16 

agreement 9:11 

9:13 

ahead 83:7 

ahera 7:19,21 

air 8:12,13 

allow 31:7 

allowed 12:6 

allows 10:8 50:17 

alluded 61:4 

alteration 22:12 

40:7 

alterations 26:14 

aluminum 33:2,8 

34:1,2 

amebocyte 82:2 

america 24:16 

55:17 

american 20:9 

amosites 35:21 

amount 28:17,18 

34:16 52:1 59:21 

60:1 67:1 71:16 

74:7 

amphibole 10:14 

10:14,15 11:5 

13:5 14:21 15:8 

15:19 17:13 21:8 

22:2 26:8,10,13 

26:15,19,19 27:1 

30:12 31:3 33:5,6 

33:14,15,19 34:5 

34:14 35:3 38:18 

38:18,21 39:4 

43:5,15,15 44:20 

45:18 49:10 51:7 

51:20 52:3,14 

54:1,4,9,10 59:7 

81:18 

amphiboles 11:1 

14:16,17,19 15:2 

15:3,10,11,12,16 

18:12 21:5,17,17 

21:19 27:21 29:6 

35:7,13,15,17 

36:7 44:16 45:19 

46:9 48:20 53:7 

54:3,14 64:14,18 

67:17 68:2 79:11 

79:12 

analysis 7:6 40:11 

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42:11 46:2 49:19 

50:16 51:6 52:2 

57:17 64:3 66:4 

67:21 68:12 69:4 

70:16 72:16 73:12 

73:13 75:8 

analysts 42:14 

analytical 2:13 

7:11,18 17:4 47:2 

53:15 74:3,11 

analyze 4:21 12:1 

17:4 47:15 48:8 

49:1 63:11 66:17 

analyzed 5:17 

49:8 

analyzing 49:2 

53:5 64:17 

angle 42:8 43:21 

52:19 67:9 

ann 36:1 42:1 58:5 

answer 9:16 64:19 

71:1 72:8 78:1 

anthophyllite 26:4 

26:4 38:11 42:2,5 

42:9 44:1 50:10 

51:15,16 52:5 

54:20 65:16,17 

68:2 72:8 82:6 

anthophyllites 7:8 

51:19 52:18 55:2 

anybody 10:2 30:2 

anybody's 12:3 

anyway 31:21 

apart 23:8,9,12 

35:11 36:12 37:1 

0.0004 59:17 

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8 84:21 

9 

1 90 11:21 

1 11:15 17:8 60:15 

60:18 75:4,17 

76:9,9 78:11 

1,000 63:18,21 

64:1 

10 63:9 

10,000 66:17,18 

100 32:15 63:19 

100,000 67:2 

11 18:20 

12 2:14 

15 56:17 60:21 

17807 84:17 

1987 7:20 

a 

ability 45:20 49:5 

able 6:14 41:2 

46:12 49:1 61:5,5 

62:12 63:3 66:12 

72:20 78:13,16 

absence 16:9 

absent 16:3 

accept 70:7 

acceptable 17:2 

accepted 76:11 

account 83:4 

accounted 82:8 

accounting 82:16 

acid 34:20 

acids 34:21 

act 8:1 31:12 

actinolite 30:6,13 

30:16,17 31:19 

53:11 

action 84:14 

actual 80:5 

added 5:15 80:10 

80:16 

additional 7:11 

additives 5:15 

address 17:17 

77:1 

adequate 4:12 

adequately 25:4 

administered 8:4 

adverse 38:3 

affect 22:12 75:16 

agencies 77:8 

agency 77:3 

agents 61:11 

aggregate 23:21 

aggregating 36:17 

2 

2 30:8 51:16 

2,000 63:9 

20 66:21 

20,000 63:9 

2018 1:5 84:16 

2021 84:21 

28 1:5 61:1 

28th 84:15 

2:35 56:19 

3 

30 66:21 

3:03 83:9 

5 

5 11:12,16 

5.3 65:20 

50/50 31:18,20 

5si8o22 30:8 

7 

70s 9:19 10:10 

11:14 12:13 16:15 

31:12 

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42:3 52:11 

apparently 21:1 

appearance 38:16 

appeared 84:5 

application 5:12 

58:10 77:16 

applied 66:6 

apply 60:6 73:5 

appreciate 22:7 

76:17 

approach 10:10 

20:3,7 57:5 59:4 

65:8 73:12 77:14 

appropriate 4:12 

appropriately 

5:16,16 7:2 16:3 

approval 57:14 

approved 10:11 

arbitrary 30:19 

31:17 

area 21:21 23:9 

24:12,19 27:16 

areas 9:6,9,10 

14:3 19:4,13 

22:14 24:12 36:17 

argument 17:10 

asbestiform 24:18 

26:5,10 37:18 

38:17 40:2 43:15 

43:18 50:5 51:12 

53:7 

asbestos 1:3 2:16 

3:17 4:16,19,21 

7:12,21 8:10 9:15 

10:15 11:2 15:12 

16:1,6,10 19:1 

22:8 23:6,13,15 

24:1,2,6 25:9 

27:20 28:3,6 

32:12 36:3 37:21 

38:5 43:15 45:19 

54:1,2,5 57:20 

59:7 60:21 64:18 

74:7 79:11 

ashing 80:17,18 

81:11 

asked 83:3 

aspect 47:9 

assess 6:14 

assessments 13:21 

assign 34:5 

associated 79:6 

association 38:3 

assumption 34:7 

assure 74:6 

atlanta 68:19 

attended 4:18 

attributes 10:4 

57:18 

audience 6:1 

automated 66:11 

70:16 

automation 73:4 

automations 67:2 

b 

b 13:15 

back 7:20,20 8:14 

20:18,20 27:5 

28:8 31:12 32:4 

49:16 53:14 57:11 

61:3 62:1 65:7 

71:10,15 73:4 

background 2:19 

4:15 

backscatter 72:12 

backup 44:2 

bad 55:10 

bailiwick 57:2 

bariatric 70:17,18 

base 4:20,21 18:9 

based 37:13 42:3 

42:11 43:20 50:1 

50:18 59:3 60:5 

63:13 69:3,9 

bases 65:20 

basic 10:3 

basically 25:9 

basis 6:20 

bat 18:15 

batch 66:5 

bayer 41:19 

beautiful 14:11 

believe 37:6 

benefit 75:12 

bent 39:21 

best 50:4 67:14 

69:9 73:21 78:3 

better 9:21 11:15 

11:19,20 12:1 

30:5 49:9 62:19 

64:10,17 73:8 

beyond 17:9 83:1 

big 14:10 24:3 

58:7 63:11,17 

73:9 82:7 

biggest 80:7 

billion 17:14 

60:17 

binders 80:15 

biochemical 73:20 

biopsy 80:21 

bit 22:2,11 32:6 

39:21 

blast 14:7 23:18 

blasting 14:6,12 

14:13 

blend 56:7,10 

blended 56:1 

blind 55:18 

block 71:14 

body 58:6,10,17 

box 62:2,3 

brad 21:8 

break 39:16 73:14 

81:15 83:7 

breaks 24:1 

breathing 15:8 

brief 10:4 

bringing 37:4 

broaden 6:10,17 

broader 78:1 

broke 24:5 

brook 2:5 27:13 

brought 32:2 

brown 46:16 

build 32:3 60:7 

building 2:16 

15:10 73:10 

bulk 8:9 68:14,21 

69:3 

bunch 32:17 

bundles 35:20 

bureaucracy 

59:10 

burn 80:18 

buying 12:19 

c 

c 1:4 2:1 

ca2 30:8 

calcic 34:14 35:3 

calcite 34:16 

calcium 5:12 

18:12 28:15 29:9 

32:5,9,9 34:11,16 

34:17 35:2 49:20 

50:2,6 51:8,10 

54:15 

calculate 48:7 

60:6 

calculation 47:4 

47:10 

calculations 62:18 

63:16 

call 3:17 38:20 

58:15 

called 2:13 8:4 

31:11 32:18 41:19 

41:21 42:7 43:20 

52:13 72:12 

cancer 78:15,18 

79:2 

carbonate 34:17 

34:18,19 

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carcinogenic 

27:20 

care 34:13 

career 61:18 

cargo 61:14,16 

62:8 

case 15:20 56:12 

64:13 67:12 

cases 43:16 61:18 

cause 27:11 81:12 

causing 78:15,17 

79:3 

cave 35:19 

center 36:15 61:8 

centrifugation 

45:2,14,16 46:11 

49:11,18 

centrifuge 45:6 

46:7 49:3 

centrifuging 45:2 

ceramics 56:3 

certain 18:8 56:1 

56:4 65:15 68:5 

82:21 

certainly 61:7 

certification 6:13 

certify 84:4,9,12 

chain 75:4 

chalcosis 79:15 

chance 51:3 

change 31:2 57:12 

81:19,20,21 

changes 30:11 

37:14 72:18 81:19 

82:4,7 

character 38:17 

charging 20:13 

check 43:18,20 

chemical 4:6 28:9 

30:7,7 49:17 

50:13 51:6 52:4 

66:13 68:14,21 

chemistry 61:8 

chemists 73:20 

china 19:3,4 55:16 

chinese 20:19 

chloride 56:5 

chlorite 32:19,21 

33:1,11,19,20 

34:1,2 

chose 13:19 

chromium 29:15 

chrysotile 10:16 

48:14,15,18 49:1 

49:5 64:12,12 

82:9 

clarify 28:14 

clean 16:6 39:20 

81:4 

clear 8:12 19:12 

24:17,17 

clearly 21:2 43:13 

54:1 

cleavage 39:15 

40:1 

climb 14:13 

closed 55:2 

cluster 69:1 

cohesins 41:20 

collecting 20:10 

collection 70:6 

color 43:9 56:3 

colorants 80:16 

combination 8:17 

79:18,18 

come 18:6 65:5 

69:3 71:10 73:15 

74:20 77:5 78:2 

82:11 

comes 19:17 20:4 

48:13 55:14 71:6 

79:17 

coming 14:8 19:18 

33:11 35:10,10 

37:1 50:11 55:5 

comment 8:18,19 

14:5 28:1 63:2 

comments 12:18 

commission 84:20 

committee 57:13 

70:10 

commodities 6:12 

common 14:16,17 

27:3 

commonly 39:9 

compact 14:10 

companies 12:12 

13:3 16:16 

company 12:19,19 

13:15 70:14 

comparative 

47:19 

compare 48:1,12 

66:1 

comparison 48:4 

completely 38:21 

completion 26:21 

complex 9:3,6 

27:3 

complicate 7:6 

complicated 26:7 

complicating 

32:14 

complications 

83:5 

component 33:1 

34:14 49:10 

composition 4:7 

21:11 28:9 29:18 

33:21 49:17 52:5 

65:11 68:14 69:1 

compositional 

26:16 52:6 53:13 

66:13 

compositionally 

38:1 

compositions 15:3 

computer 69:16 

computers 67:6 

computing 67:1 

con 48:10 

concentrated 

45:14 

concentration 

48:6 60:8 

concentrations 

59:15 66:15 67:4 

concept 47:13 

concern 49:6 58:7 

74:18 82:7 

concerned 57:19 

concerns 61:2 

concluded 83:9 

conclusion 43:19 

conditions 13:8 

21:13,15,18 23:5 

26:12 36:21 37:17 

37:19 80:18 

confident 69:10 

confirm 71:17 

confirmation 

68:12 

conflicting 54:7 

confusion 3:10 

connection 76:16 

consensus 3:4 

consequence 

44:18 

consider 61:7 

consistency 77:7 

consistent 77:11 

77:12 

consolidating 

78:19 

constant 33:21 

54:13 

constantly 54:7 

constrain 46:12 

constraints 50:19 

63:13 

consulting 2:12 

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202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[consumer - diffraction] Page 4 

consumer 47:2 

consuming 64:7 

contain 5:8 6:18 

14:18 15:16 28:15 

34:15 51:8 54:15 

79:11,12 

contained 19:1 

container 61:16 

61:21 

containers 61:15 

containing 3:6 

13:5 45:19 

contains 15:10 

contaminants 

6:16 

content 5:9 15:14 

context 6:21 39:2 

42:16 

continuum 31:13 

control 20:7 82:19 

controlled 56:2 

controlling 19:15 

controls 20:17 

21:12 

convention 31:15 

conversation 

46:15 

coordinate 71:16 

correct 34:4,7 

48:16 61:6 72:5 

cosmetic 9:19 19:9 

25:20 54:18 74:4 

80:8 81:6,8 

cosmetics 2:18 3:6 

5:10,11 6:3,17 

58:11 80:15 

counsel 84:8,12 

counterfeits 61:9 

counting 50:3 

54:4 

county 84:4 

couple 17:8 18:4 

18:21 41:5 45:7 

64:5 67:21 

courser 58:9,12 

courses 4:18 

court 71:19 

crappy 55:9 

crash 67:6 

crashing 69:15 

create 26:21 36:13 

39:13 75:2 81:2 

created 59:9 

creates 39:1,5 

76:13 

crime 71:9 

critical 44:5 47:8 

65:15 

crush 39:12 

crystal 4:5 10:5,6 

10:8,18 30:9,11 

31:3,6,8 32:19 

38:1 52:5 65:18 

72:19 

crystalize 23:11 

crystallites 35:19 

crystallographic 

72:4 

ctfa 11:11 12:8 

cummingtonite 

52:3,13 53:7,8 

current 75:9 76:1 

currently 2:12 

days 64:5 

de 17:1 

deal 20:5 47:20 

54:13 61:7,8 

dealing 2:6 3:19 

27:8 33:16 41:18 

51:19 58:9 66:14 

66:20 80:7,14 

81:18 82:5 

deals 29:17 

death 19:7 21:21 

25:14,21 

debate 25:13 

decades 55:3 

decided 30:15 

decisions 15:13 

define 28:18 30:5 

defined 3:14 38:9 

defining 6:20 

definitely 46:12 

73:7 

definition 4:3 

delineating 21:1 

demonstrated 

16:3 

dense 14:10 

density 48:17 60:6 

depend 12:10 

depending 5:11 

7:5 12:18 14:1 

19:14 21:5 22:2 

29:12 40:3 44:5 

64:6 69:10 73:19 

81:1 

depends 11:8 13:1 

25:7 29:7 

deposit 21:6 23:20 

25:2 26:8,13 29:5 

29:12 33:18 

deposits 13:4,5 

21:7,14 22:3 

29:14 36:2 38:10 

38:13 79:10 

derived 29:13 

describe 9:5 13:13 

design 77:20 

82:21 

designed 61:14 

desirable 56:1 

detailed 40:10,19 

80:1 

detect 11:6 50:5 

67:3 68:1 78:16 

detectable 15:16 

detected 16:19 

detection 77:11 

detectors 71:2 

determine 10:21 

40:19 72:17 73:20 

74:2 78:18 79:1 

developed 19:14 

66:3 73:7 

device 13:7 

devices 61:13 

diameter 66:21 

difference 35:9 

42:4 

differences 28:12 

48:18 59:3 

different 4:9 6:6 

18:4 19:4,5 23:2 

26:16 27:17 34:21 

36:16,21 37:17 

38:2 40:5 43:7 

52:6,21 53:2 57:7 

58:3 59:12 61:13 

62:18 80:15,16 

82:3 

differentiate 

41:16,17 42:8 

differentiations 

65:17 

difficult 9:16 

80:13 

difficulties 81:2 

diffraction 10:1,7 

10:7,13,19 12:4 

d 

d 2:1 

d.c. 15:9 

daily 47:10 

data 1:4 3:16 4:1 

5:19 11:14 35:17 

41:9 42:12 44:3 

47:2,6,8,17 48:6,9 

49:9,13 52:14,16 

53:11,17 54:8,8 

60:19 65:9 67:7 

67:14 69:9 70:6 

73:9 77:12 

day 4:18 5:21 

84:15 

www.CapitalReportingCompany.com 

202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[diffraction - fda] Page 5 

40:13 57:14 65:14 

65:19 72:13 75:5 

75:10 

dilation 23:8 

diligence 59:2 

dilution 56:11 

dimensionally 

36:16 

diminishing 67:12 

dioxide 81:1 

direction 39:16 

directions 41:15 

directly 38:13 

dirt 7:4 

disagreement 9:9 

disclosed 43:3 

discrepancies 

53:19 

discrepancy 54:3 

discrete 63:19 

discriminator 

70:1 

discriminators 

71:5 

discussion 4:14 

49:16 77:17 

discussions 10:12 

15:19 50:1 

disease 79:10 

dispersions 66:16 

disregard 33:8 

dissolve 11:21 

distinction 43:14 

83:5,6 

distinctions 40:9 

document 43:10 

doing 12:10,13,20 

12:21 13:8 17:21 

32:10 40:21 44:18 

46:11 48:5,12 

57:14,16 60:8,10 

64:5 75:7,20 

81:11,13 

dolomite 34:17 

door 61:3 

dose 47:10 

draw 60:13 

drawn 12:5 

drug 6:3 74:4 

drugs 6:4 82:20 

due 59:2 

duly 84:7 

dump 37:15 

dwell 70:8 

e 

e 2:1,1 

earlier 8:18 12:17 

14:5 26:9 30:6 

63:2 

early 16:18 

earth 37:6 

easier 51:5 

easily 13:4 

easy 42:2 

ebsd 72:20 73:5,9 

edge 39:18,21 

40:6 

eds 65:12 66:13 

69:13,14 72:9 

efficient 77:20 

either 5:1 7:17 

10:17 18:5,14 

31:9 34:16 41:7 

48:9,11 57:4 73:6 

73:21 

elbow 49:19 67:21 

electron 59:1 

electronic 72:12 

elements 23:5 

37:11,16 49:21 

eliminate 49:5 

68:3 

eliminates 49:4 

elongated 15:11 

15:13 54:4 

emergency 8:1 

emphasis 2:11 

employed 66:10 

encounter 32:17 

32:18 55:12 

endorse 62:10 

engineers 41:7 

enhance 44:15 

45:20 

enhanced 45:18 

enriched 45:17 

entity 12:16 

entry 61:10 

environment 

22:16 26:17 35:15 

environmental 5:6 

environments 

21:20 23:6,7 

epa 8:7 17:6 77:9 

epidemiological 

79:5 

equal 36:11 

equilibrium 26:18 

equipment 5:2 

11:9,10 

error 17:6 

errors 46:13 

especially 4:9 5:6 

9:18 11:19 49:10 

64:11 81:13 

essays 74:4 

essence 43:7 

established 3:4 

europe 55:17 

evaluate 3:15 4:8 

53:17 

evaluated 3:20 

evaluating 24:11 

event 26:9 

eventually 9:20 

evil 56:11 

exactly 41:3 57:1 

62:9 70:11 

examination 84:9 

examined 84:8 

exception 31:18 

exceptions 22:6 

36:19 

exchange 31:1 

exist 41:12 

existing 48:1 

expect 29:6 

expectations 

74:16 

experience 12:12 

38:11 46:6 56:12 

69:14 

expert 43:2 57:13 

59:5 73:21 75:18 

expertise 2:9 57:3 

expires 84:20 

exposed 15:2,3 

74:8 

exposure 74:5 

75:15 79:15 

extinction 41:21 

42:8 43:21 52:19 

extra 68:10 

extreme 9:16 

36:19 

extremes 36:18 

f 

face 14:2 

faces 21:5 24:10 

fact 11:1 70:10 

factor 21:11 32:14 

factors 21:13 

fairly 13:3 20:15 

far 69:7 

fascinating 79:4 

fashion 48:8 

fast 71:3 

fault 22:13,17 

23:4,16 24:17,18 

35:9 36:21 

faults 36:10 

fda 6:3,11 10:11 

16:16,17 17:19 

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202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[fda - gunter] Page 6 

18:1 46:16 60:20 flexibility 31:8 

61:1 73:18 76:13 flows 23:3 

further 75:4 84:9 

84:12 

63:16 66:17 68:7 

68:9 71:9,15,15 

73:4 78:6 83:7 

goal 3:1,3 5:9 

god 28:6 34:8 

goes 20:18,20 23:3 

24:8 71:18 73:8 

going 2:8,18 5:21 

13:4,10,13 17:21 

18:13 20:10 29:7 

29:14 32:4,11 

33:2 35:11,14,16 

48:20,21 50:4,8 

51:3 57:20 62:12 

64:2,4 65:3,10 

67:13 70:19 73:20 

74:6,7,8 75:11 

76:20 78:6 79:2 

good 2:21 4:3 6:8 

6:19 11:7 18:10 

18:16 19:16 55:9 

72:18 80:6 

gosen 19:8 

government 77:5 

grade 54:18 81:6 

81:9 82:14,18 

grades 9:19 35:21 

grains 72:17 

gram 47:14 48:4 

grid 63:20,20 

grinding 58:9 

gross 65:20 

ground 44:9 58:16 

66:20 

group 2:14 56:21 

74:1 77:1 

groups 31:14 65:4 

guess 5:7 10:12 

56:20 

guiding 47:6 

gunpowder 70:9 

gunshot 70:15 

gunter 2:4 

77:4 fluid 20:15 23:3 

fda's 6:13 fluids 23:10 

fe 30:8 fly 61:12 

features 23:14,17 focus 50:3 78:2 

43:10 follow 10:19 75:10 

fell 68:8 food 20:2 

fiber 36:5,13 47:3 foot 23:2 

48:4 59:17 force 37:8 

fibers 3:5 35:20 forensic 61:8 

36:8 47:14,14 forget 8:18 32:1,2 

50:6 81:12 68:19 

fibrocity 38:17 form 21:17 23:6 

fibrous 27:16,19 35:7 37:12,17,18 

35:16 36:7,10 37:19 38:7 

37:20 38:7 39:1,3 formation 21:5 

40:3,6 26:9 

figure 46:19 75:19 formed 29:18 

76:10 former 2:10 

filled 23:9 forming 22:8,9,9 

filling 37:3 22:11 26:20 35:15 

filter 72:20 37:13 

filth 66:4 formitization 24:7 

find 17:2,14 21:19 forms 25:9 

28:20 29:3,4,6,7 formula 30:7 

29:14,15 32:9 formulations 

41:1 46:8 51:3 82:15 

66:18 74:6 forward 57:13 

finding 54:2 57:6 found 3:14 44:20 

findings 55:19 53:10 

fine 17:9 36:4,4 fractions 58:4 

44:20 58:16 76:10 fracture 35:8 

81:1 81:12 

finer 35:20 58:12 fragments 54:4 

finest 63:12 frankly 76:5 

finger 38:19 front 62:1 

firm 2:12 fund 20:14 

first 3:12 4:10 fundamentals 5:1 

23:15 28:5 36:12 5:2 

fissure 22:20 funky 39:5 

fit 77:16 funnel 37:8 

five 4:18 5:4 19:20 funny 25:13 70:13 

24:20 59:10 78:12 

g 

g 2:1 

gap 37:3 

gaps 37:7,9 

gather 15:18 

geared 40:16 

general 9:7,10,12 

10:3 20:14 22:6 

32:7 33:20 54:20 

64:17 

generally 5:4 10:6 

19:10 21:16 22:5 

22:8 23:17 25:11 

29:4 31:17 32:6 

32:16 35:16 51:17 

52:4 54:16,19 

generates 75:15 

gentleman 61:4 

geologically 19:5 

21:16 

geologist 13:17 

geologist's 14:19 

geology 2:11 

13:19 

germane 55:4 

getting 13:5 20:3 

20:17 32:1 49:16 

58:13 68:20 81:3 

81:10 

give 4:1 33:4 50:4 

74:19 

gives 4:20 

glass 30:3 44:10 

go 3:8,9 8:14 

13:14,18,21 17:8 

17:14 18:10 21:2 

21:4 22:4 23:18 

26:20 28:19 36:6 

36:7 38:15 40:18 

44:21 47:12,13 

49:3,15 53:12 

61:3,12 62:11 

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202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[gunter's - kind] Page 7 

gunter's 2:10 

68:15 

guys 73:15 74:17 

79:21 82:16 

hoping 59:8 indices 42:4,5 

hour 63:5 43:18 52:10,12,17 

hours 67:5 80:13 81:20 

huge 36:8 individual 9:14 

humans 30:20 11:8 29:10 35:5 

hundred 70:2 35:20 36:5 

i individuals 13:1

industrial 2:17 icp 34:3 19:9 54:17 75:8 idea 10:3 14:20 industry 10:11 31:21 34:10 44:1 20:3,8,14 58:1 59:19 information 4:1 idealistic 57:9 14:8 46:18 50:18 ideally 24:10,12 53:13 57:15 58:20 ideas 69:6 66:13 72:4 73:1 identical 52:7 80:3 identification 2:8 inherent 11:17 3:20 39:7 42:17 initial 10:1 68:4 46:3 53:16 57:4 insect 66:5 identified 8:10 instance 45:1 49:8 71:12 instrument 11:18 identify 4:2,19 5:4 64:17 7:2 9:12,13 18:5 

33:3,4,8,14,14 

34:1,2,3 49:20 

50:2,6,10,12 51:8 

51:9,10,17,18,21 

52:1,1 54:15 

69:18 81:18,20 

82:10 

irons 34:4 

irs 15:10 

isolate 53:12 

isolation 45:18 

issue 3:18 46:3,4 

48:12 50:8 54:13 

55:4 56:11 65:16 

65:17 66:15 70:5 

72:2 73:2 74:13 

80:7 82:9 

issues 2:8 17:3 

21:10 29:21 58:5 

67:15 81:7 

italy 79:7 

h 

hairy 24:4,5 

hand 70:5 84:15 

handheld 13:7 

handle 25:8 61:10 

hands 22:16 

happen 26:14 

happening 24:7 

happens 55:21 

hard 29:3 45:3 

harmful 15:1 

haul 62:12 

health 29:21 38:2 

38:3 

heard 7:7 47:7 

50:2 

heat 82:1,3 

heavily 2:15 j 

heavy 45:1 46:8 

48:20 

held 39:20 

instrumentation 18:15,16 27:6 7:15 40:17 61:5 instruments 4:10 ilk 54:21 

j4 11:11 

jean 84:3,18 

jeff 76:15 

help 3:10 33:8 

64:21 66:4 67:18 

76:14,17 79:21 

80:12 

helped 66:7 

helps 13:14 

high 79:16 82:10 

higher 21:20 

highest 74:5 

historic 53:6 

historical 31:20 

54:8 

historically 9:18 

79:16 

hitting 2:4 

hold 37:16 

home 71:10 

homogeneity 

60:11 

4:11 image 65:10 66:12 integrate 74:2 impacting 35:10 interested 84:13 important 4:8 interesting 3:15 5:18 8:19 15:14 16:8 27:1 29:11 17:16 46:19 53:18 interpretation 1:4 74:14 78:4 3:4 5:19 27:2 47:6 importation 19:20 interstitial 23:9 imported 19:2,3 introduce 75:12 imposed 16:17 introduction 2:21 impractical 62:15 33:2 imprint 36:14 intrusion 37:4 including 25:5 involved 2:15 8:3 increase 51:3 10:12 29:16 44:7 incredibly 9:6 59:12 76:4 index 41:15 52:8 iron 18:12 28:10 india 55:16 28:10,15 29:9 indicating 79:15 30:10,14 31:2,5 indication 69:2 31:11 32:5,9,9,20 

k 

keep 5:21 10:3 

13:10 30:14 

keeps 20:14 

kept 7:8 

kid 71:14 

kind 2:19 3:9 4:13 

4:15 15:5 20:11 

20:17,18,20 22:15 

23:13 26:5 35:13 

36:18,19 39:13 

41:9 49:17 51:5 

54:6 59:19 60:9 

60:19,21 64:2 

65:2 66:6 67:14 

69:2 70:19 75:5 

76:1 77:7 80:8 

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202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[knew - market] Page 8 

knew 33:18 

know 3:1 5:1,9,14 

5:20 6:2 7:4,11 

8:2,18 9:3,4,5,7 

10:2 11:20 12:4 

12:10,18 13:15,16 

13:18 14:4,7,13 

14:15 15:9,13 

16:15 17:15,19 

18:2 19:7,15,21 

20:1,6,9,12,12,15 

20:15 21:4,9 22:1 

22:7,17 23:11,18 

24:8,10,11 25:12 

25:14 26:18 27:8 

27:9 29:2 30:2,16 

32:18 34:6,21 

35:10,18 36:4 

37:10,15 38:8 

40:10 41:7 42:1 

42:16 43:3,4 44:6 

44:12 45:2 47:13 

47:18,18 48:13,17 

l 

lab 61:12 

labeling 17:7 

laboratories 7:13 

7:18 

laboratory 2:13 

18:20 55:13 61:14 

labs 4:16,17 11:15 

59:12 71:9 

laid 63:14 

language 16:9,10 

16:18 

large 4:21 

larger 4:20 

late 56:18 

law 84:7 

laws 17:7 

lawson 2:5 

lawyers 78:8 

layer 50:4,8 

layers 49:18,21 

50:7 

lead 2:8 4:19 

liquid 45:1 46:8 

literally 37:5 

39:11 44:9 47:16 

little 30:5 31:15 

32:6 34:12 39:21 

44:9,10 45:6 51:9 

51:10 54:15 77:16 

live 15:6 17:1 

living 75:5 

localized 23:1 

locations 25:7 

51:18 

log 71:7 

logical 54:11 

long 39:7 75:8 

longer 37:16 72:7 

look 7:10 14:3 

18:2,4,14 23:15 

29:3 32:8,15 35:5 

35:19 36:7 37:21 

39:18,19 40:13 

41:11,20,20 42:4 

45:8,12 49:18 

looks 38:15,18 

40:2,2 64:16 82:2 

82:4 

loose 47:12 

loosened 14:13 

lot 4:16,20 9:9 

11:20 19:13 20:7 

21:6 24:8 27:5 

34:15 37:2,11 

42:10 44:13,19 

49:12 53:14 54:16 

56:2,3 58:14 

60:18 63:3 64:16 

66:19 70:14,15 

74:10 75:7 80:8 

80:10,11 82:1 

low 21:15 59:16 

60:9,11 66:14 

77:21 80:18 81:9 

81:11 

lower 59:14,18 

60:14 67:3 75:13 

lowest 59:17 

49:7,13 51:2,20 

51:21 54:5,7,16 

55:5,8,14,15,17 

58:5 59:2 60:12 

61:9 62:17 63:4 

64:10 67:5,11 

68:1 69:3,6,12 

70:2,6,9 71:11 

73:15 75:13 76:16 

76:19 77:15 78:4 

78:11 79:1,3,7 

80:2,9,11,16 82:4 

83:6 

knowing 33:15 

55:10 

knowledge 19:19 

25:18 40:9 

known 51:18 

59:21 

knows 24:13 34:8 

28:20 

lee 2:13 

left 8:13 60:15 

length 60:4 

level 11:12 17:1,5 

18:8,9 59:14 

74:18 75:21 76:1 

77:6 82:20 

levels 15:16 17:5 

29:9 59:16 60:11 

60:14 77:21 79:15 

lifetime 27:18 

light 8:8,20 10:20 

10:21 41:12 43:7 

43:8 

limit 5:7 16:4 35:2 

74:18 77:8 

line 12:5 15:15 

30:17,18 

lines 67:10 

50:19 57:7 58:3 

62:19 63:1 73:4 

78:3 

looked 15:5 27:15 

27:17 28:2,5 

38:14 44:14 45:13 

54:16 63:18,21 

68:13 

looking 3:16,16 

5:3,19 14:15 

17:18 23:16,17 

25:4 27:1 29:13 

34:7 36:5 40:4,8 

42:11 43:11 44:11 

44:20 45:11,15 

51:5 52:12,14 

54:9 55:6,11 

57:12,13 60:13 

63:8,11,14 64:8 

67:17,20 68:21 

69:16 70:16 77:14 

m 

m 84:3,18 

machine 71:12 

machine's 67:5 

magnesite 34:18 

magnesium 30:10 

31:1,6,10 34:17 

34:19 

magnitude 17:9 

59:18 75:3 

main 81:5,7 

makeup 80:9 81:8 

mandatory 57:21 

manipulate 65:21 

mantel 14:11 

manufacturing 

20:2 

marian 68:17 

marker 33:8 

market 18:10,17 

68:8 

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202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[marty - mold] Page 9 

marty 68:16 

maryland 84:1 

mask 80:12 

masking 80:11 

maslac 8:5 

mass 60:7,8,8 

match 15:4 

material 7:1 11:21 

14:12 24:21 25:2 

36:15 39:5 57:6 

60:1 63:4 64:9,16 

70:17 75:14 

materials 2:15,16 

5:3,6 36:10 46:10 

62:2 

matrices 7:5 

matrix 7:3 11:19 

matt 2:3 

matter 2:2 4:14 

6:21 17:15 42:14 

47:15 48:11 

matthew 1:6 

mean 16:10 17:12 

28:19,20 30:2,20 

31:1 33:10 36:3 

40:16 47:15 50:14 

53:4,17 58:19 

60:16 61:18 62:11 

63:19 69:7 70:21 

75:15 76:5 78:12 

79:21 

meaning 8:20 

10:20 26:15 38:19 

meaningful 28:16 

28:18 48:3 

means 16:18 

42:16 47:18 77:21 

meant 7:21 

measure 34:2 

41:14,14 46:19 

60:3 66:11 

measured 52:19 

measurement 

33:3,4 34:11 42:7 

43:19 46:14 64:10 

81:21 

measurements 3:5 

10:5,8 41:19 66:1 

81:4 

measuring 13:8 

65:11,20 

medium 66:21 

meet 56:13 74:16 

74:21 

meeting 2:5 16:14 

26:2 41:7 77:3,19 

meetings 3:13 

82:17 

memory 67:1 

mention 49:14 

mentioned 19:8 

30:1 70:13 

mentioning 38:6 

mesothelioma 

79:14 

metal 72:16 

metamorphic 

26:12 

meters 24:20 

method 8:7 12:7,8 

34:3 43:4 59:19 

62:21 69:8 73:21 

76:7 78:3 82:12 

methodologies 

67:21 80:1 

methodology 

17:20 29:8 47:5 

57:21 58:17 59:6 

60:9 75:2,21 

methods 4:9,10 

8:6 15:17 34:3 

41:11 42:13 44:2 

53:15 73:20 80:5 

mg 30:8 

mg2mg5si8o22 

51:16 

mica 7:4 

micas 5:12 

michael 57:4 

mickey 2:10 68:15 

micky 2:4 

micron 66:21 

micronized 58:15 

58:21 

microns 78:11,12 

microscope 39:19 

microscopist 

62:10 

microscopy 2:7 

3:5,21,21 4:9 8:8 

8:21 10:20,21 

35:6 41:12 43:8 

50:15,16 51:4 

57:17,21 59:1 

62:21 75:7,10,12 

microseconds 

71:2 

mild 79:14 

milligrams 45:5,9 

45:11,12 63:5 

million 17:13 64:3 

76:6,8 

mind 3:7 

mine 13:21 14:3,3 

14:7 19:4,7 20:19 

21:7 23:19 24:9 

24:15,16,19 25:6 

25:8 

mined 24:20 

mineral 2:7 3:5,14 

3:19 4:2,4 5:14 

6:21 7:3 9:12,13 

10:4,18 26:8 31:2 

31:11,14 32:18 

39:10 42:16 50:11 

mineralization 

22:10,12 23:12 

mineralogical 

79:20 

mineralogically 

35:16 

mineralogist 9:4 

mineralogist's 

14:20 

mineralogists 

30:15 

mineralogy 2:12 

9:3,6,11 31:18 

minerals 2:17 

3:17 5:10 6:15 7:5 

10:14 15:20 16:2 

21:12 30:19 32:8 

37:4,17 42:11 

48:21 52:9 56:6 

57:16 65:15 

miners 16:16 79:9 

mines 13:9,13,21 

19:11 20:21 21:2 

21:9 22:4 23:16 

24:9,13 25:7 38:4 

55:1 79:7 

minimal 12:14,20 

minimalized 58:8 

minimizes 20:15 

minimum 11:16 

60:2 

minimus 17:1 

mining 12:18 13:2 

14:9 21:4 24:12 

25:5 41:7 

minutes 16:15 

56:18 

misidentification 

5:17 

misidentifications 

54:12 

missed 2:21 

missing 58:8 

mitigate 67:15 

model 27:21 

moderator 1:6 2:5 

moderators 3:2 

modes 43:7 

mold 77:16 

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Session C Interpretation of testing data November 28, 2018 

[money - part] Page 10 

money 20:9 need 7:15 9:11,13 numerous 66:10 options 18:3 

order 10:21 27:7 

46:9,18 59:11 

67:3 72:21 78:19 

orders 17:8 59:18 

75:3 

organic 80:10,15 

organics 81:10 

organization 8:4 

organizers 3:3 

orientation 39:20 

72:17 

orientations 43:6 

original 29:17 

36:15 

originated 55:16 

osha 17:7 

outcome 84:14 

outer 36:17 

outside 6:13 7:4 

13:18 15:9 62:12 

68:8,11 

overlaps 52:17,18 

oxidization 81:19 

monoclinic 52:13 

monographs 66:7 

monosulfide 31:11 

montana 21:9 

montgomery 84:4 

months 59:11 

76:17 

morning 3:13 7:7 

15:19 18:9 28:9 

38:7 44:13 50:2 

78:10 

morning's 47:7 

morphologically 

37:21 

morphology 41:20 

43:12 

mossman 27:15 

28:4,11 51:14 

56:16,19 65:4 

73:17 79:13 

mounds 71:7 

mounts 45:13 

move 6:1 47:21 

18:13 20:15 46:7 

46:17 47:3,4 49:9 

56:4,7,9 58:20 

62:4 65:21 69:5 

74:1,6,15,17 77:1 

77:12,17 78:4,13 

needle 71:17 

needs 23:5 70:5 

77:13 

negative 12:5,9 

16:11 

never 14:6 23:19 

25:2 26:1 38:12 

61:15 78:9 

new 22:1 25:15 

26:1 71:2,14 

nice 26:9 57:5 

72:16 

night 71:10 

nil 48:18 

nist 8:4 46:16 

70:10 

nobody's 15:4 

o 

o 2:1 

obstacle 67:4 

obtain 66:12 

obtained 53:12 

occasionally 80:20 

occasions 18:21 

66:10 

occupy 8:14 

occur 27:4 54:12 

occurrences 25:12 

36:9 

occurring 4:5 

22:13 23:13 

october 84:21 

office 17:20 78:5 

offset 24:17 

oh 28:6 30:8 51:16 

69:20 72:14 

okay 2:21 18:7 

44:12 47:2 62:2 

74:8 75:17,19 

76:16 58:20 59:10 

moved 65:4 

multiple 12:12 

36:12 71:7 

65:5 

nomenclature 

30:20 

non 26:10 43:15 

normal 40:5 

northern 19:3 

25:15 

norway 79:8 

norwegian 79:9 

notary 84:3,19 

novel 73:12 

november 1:5 

84:16 

nuance 26:6 

nuances 27:7 

35:18 42:10,20 

number 27:16 

47:18 

numbers 63:17 

old 58:6 

older 53:17 54:10 

72:11 

once 8:10 9:14 

58:20 70:1 82:3 

ones 16:17 25:21 

79:7,8 

open 14:2 62:1 

opened 37:7 

opening 63:20 

openings 63:21 

operate 17:5 

operating 19:11 

operator 71:13 

operators 11:9 

opportunity 50:5 

opposed 13:19 

optical 41:13 

53:11 66:3 

p 

p 2:1 

p.m. 83:9 

paints 56:4 

pakistan 19:18,19 

19:21 55:7,16 

pakistani 55:8 

palygorskite 

37:20 

panel 43:2 57:11 

59:5 74:1 75:18 

paper 39:11 61:9 

68:15 

papers 82:1 

parameters 16:19 

68:6,9 

paremeter 18:11 

part 6:20 8:9 

17:13,14 30:20 

43:3 44:3 64:3 

n 

n 2:1 

naivete 57:2 

name 2:3 68:18,19 

named 84:5 

names 30:18 

naming 31:15 

narrowing 6:17 

natural 81:3 

naturally 4:4 

nature 9:5,5 22:9 

29:12 

necessarily 29:20 

36:11 64:13 66:9 

82:6 83:4 

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202-857-3376 
Session C Interpretation of testing data November 28, 2018 

[part - problem] Page 11 

73:3 76:21 77:17 

78:3,4 79:1 

partially 26:13 

particle 9:15 

43:12 44:13 50:17 

50:18 58:7 59:3 

59:19 60:3 64:1 

69:17 70:6,11 

71:1,12,17 72:7 

73:13 

particles 27:2,9 

28:2 29:10 35:5 

39:14 40:14 46:20 

46:21 47:14 50:3 

50:7,20 51:7,12 

53:12 63:1,9,10 

63:12,18,20,21 

64:9,11 65:21 

66:5,11,18,19,19 

67:3 69:16 70:2 

71:5 72:20 73:5 

78:11,14,14,16,17 

79:2 80:12,20 

81:3 

particular 16:4 

71:16,16 79:7 

particularly 28:10 

particulate 58:4 

parties 84:13 

parts 60:16,17 

66:5 76:6,8 

pass 56:9 

passed 7:20 22:19 

paul 46:16 

peels 58:14 

people 2:20 4:17 

4:20 5:1 8:14,16 

8:16,17 12:10,15 

15:8 17:21 20:9 

40:14 41:2 48:15 

49:6 53:9 57:19 

59:12 75:7 76:3 

percent 11:12,13 

11:16,21 17:7,8 

19:20 32:15 48:7 

51:20 59:17 60:15 

60:18 75:4 79:1 

percentage 11:5 

44:15 78:14,16 

percentages 69:18 

percents 47:21 

51:21 

perfect 39:15 

perfectly 76:10 

perform 40:12 

person 74:7 

personal 12:11 

19:19 

personally 84:5 

perspective 14:19 

14:20 43:1 62:20 

ph.d. 2:11 

pharmaceutical 

25:19 54:18 81:6 

82:14,18 

pharmaceuticals 

58:13 

phase 26:18 32:21 

50:11 

phases 10:18 

32:16 40:13 

photograph 43:4 

43:6 

photographs 43:9 

physical 56:13 

physically 45:8 

64:8 

pick 14:14 31:17 

66:4 

piece 14:10 15:14 

17:16 

piles 14:12,14,14 

pinch 2:3 

pit 14:2 

pitfalls 42:20 

place 4:12 23:15 

84:6 

places 36:6 61:13 

62:11 

plaintiff 78:8 

plan 82:11 

plane 24:2 

planes 40:1 

plate 39:10,13 

played 70:3 

plenty 12:14 

plm 7:17 8:7,9,17 

8:20 41:8,13 42:2 

42:3 43:1,4,7,13 

43:14 44:8 52:14 

52:16 53:21 58:1 

58:18 62:7,8,11 

62:19 63:2 64:8 

64:12,16 69:7 

80:2 

point 6:8,20 10:18 

30:15,16 32:3 

62:20 67:11,13,19 

points 5:18 31:17 

35:8 47:11,12 

polarized 8:8,20 

10:21 41:12 43:8 

polished 72:16 

poorly 38:9 

portable 62:6 

portrayed 56:11 

ports 61:10 

possibility 33:5 

possible 77:12 

possibly 17:15 

34:18 44:15 

potential 15:21 

18:3 27:20 40:17 

45:20 

potentially 18:1 

44:14 45:17,18 

47:1 51:13 

powder 9:21 10:7 

10:9,13 11:6 44:9 

58:6,18 

powders 58:16 

66:17 

power 67:2 

practical 41:5 

61:3 

practice 12:9 

practices 20:2 

precise 40:12 

prep 44:6,14 

45:10 69:14 

preparation 44:5 

83:1 

preparations 71:8 

72:21 

presence 28:10 

32:5 33:7 68:3 

present 7:6 21:12 

34:12 40:14 46:9 

52:15 57:16 64:12 

presentations 

41:6 

pressure 21:13,14 

21:20 22:16 26:16 

37:8,14 

pressures 23:1 

37:10 

pretty 30:14 31:9 

33:20 43:16 59:16 

73:12 78:12 

prevent 20:4 

preventative 20:6 

20:16 

prevention 20:6 

primarily 2:7 8:7 

31:9 37:3 54:17 

primary 22:10 

pro 48:10 

probably 52:15 

69:8 73:17 76:10 

problem 18:15 

20:4,20 24:11 

40:20 52:11 53:3 

57:8 63:6 

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Session C Interpretation of testing data November 28, 2018 

[problematic - review] Page 12 

problematic 18:5 

problems 19:17 

20:8 27:11 50:18 

procedure 11:17 

procedures 4:12 

25:4 83:1 

proceed 12:6 

proceedings 84:11 

process 19:15 

26:20 31:4 39:7 

40:18 66:8 81:16 

82:5 

produce 51:11 

product 25:5 47:2 

products 6:15,18 

74:4,5 77:9,13 

80:5 

proper 67:9,15 

properly 45:20 

properties 41:13 

56:1 

proportions 56:8 

proposed 9:20 

prospect 39:1 

protocol 8:9 

protocols 67:10 

provides 57:15 

proving 16:11 

pseudo 27:1 

pt 8:2 

public 84:3,19 

pulled 58:6 

pulling 23:8,12 

36:12 37:1,2 

pulls 23:8 

pure 32:15 

purely 33:16 

purpose 47:19 

81:11 

purposes 2:18 

19:9,10 25:20 

26:1 76:2,2 80:11 

put 18:6 45:6 

57:13 60:1 61:16 

68:7 71:8,17 74:9 

putting 44:10 

80:17 

puzone 68:17 

readout 65:12 

real 18:19 46:6 

57:12 72:2 83:6 

really 6:11 11:8 

11:18 13:1 21:7 

25:6 31:2 35:4 

38:12 40:11 47:15 

48:10 50:13 53:15 

55:3,8,9,9 61:18 

71:12 74:14 82:10 

realm 9:14 59:1 

ream 39:11 

reason 58:2 64:15 

71:21 76:21 

reasonable 43:19 

57:7 75:21 77:6 

reasonably 16:6 

68:7 

reasons 31:20 

54:11 56:10 

recommendations 

56:21 

record 42:15 

84:11 

recorded 42:12 

84:10 

records 53:8 

rectify 54:7 

reduce 46:13 

referring 14:18 

refractive 41:15 

42:3,5 43:18 52:8 

52:10,12,17 80:13 

81:20 

regarding 56:21 

regardless 2:17 

regime 8:2 

regular 26:5 

regulated 15:4 

regulates 6:12 

regulation 7:19 

48:2 

regulations 17:6,7 

47:20 

regulatory 17:20 

74:16 76:1 

reject 70:7 

relative 10:15,16 

29:16 

relatively 4:6 

reliable 46:10 

59:13 60:12 76:8 

relied 50:12 

remaining 34:4 

remains 31:3 

remove 34:20,21 

35:1 

removed 8:11 

replace 38:21 

replacement 39:1 

39:4 40:7 

report 43:11 

47:16 52:15 55:1 

55:18 

reported 41:9 

42:14 53:10 60:17 

reporting 54:2,8 

76:2 

reproducible 

60:12 

require 42:13 44:2 

required 41:9 

requirement 

12:21 56:14 

requirements 

74:17 

requires 43:4 

research 61:1 

residue 70:9,15 

resolve 53:20 

response 8:1 

rest 22:10 61:17 

result 77:2 

results 2:7 3:21,21 

41:10 53:19 

returns 67:12 

review 10:4 

q 

quality 82:19 

quantification 

46:4,18 47:7 59:7 

59:20 75:3 76:2 

quantifying 60:14 

quantitate 45:21 

49:20 50:5 

quantitation 

46:10,13 66:8 

quantitative 47:9 

49:9,13 

quantity 15:7 

quarries 23:21 

quarry 36:3 

question 11:7 

19:17 20:20 26:3 

28:13 32:4 56:20 

60:11 66:2 71:1 

72:9 80:7 

questions 3:9 5:20 

6:1 11:3 40:16 

77:2 79:20 

quick 69:8,12 80:4 

quicker 18:17 

quickly 61:6 

quite 22:2 

r 

r 2:1 

r.t. 27:14 39:2 

radar 83:4 

raise 5:21 

range 36:8 42:6 

rates 72:18 

ratio 33:21 

rats 27:11 

ray 9:21 10:7,13 

10:19 12:4 57:14 

75:5,9 

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Session C Interpretation of testing data November 28, 2018 

[rice - similar] Page 13 

rice 66:5 

rich 69:18 

rid 81:10 

rifkin 68:16 

right 15:10 16:21 

18:15 23:5 24:13 

27:15 28:11,11 

31:16 36:18 37:18 

39:13,18 46:1,5 

47:19 48:5 49:15 

53:4,20 56:19 

57:12,21 59:5,8 

62:8 71:21 73:2 

75:2 80:10 

rips 39:12 

risk 47:10 

rj 2:13 

robin 59:13 

robins 8:3 

robust 64:20 

rock 14:4,10 

22:11,14,17,20 

23:3,7,8,10,11 

24:1,4,4,18 26:11 

29:18 32:20 38:14 

38:15 

rocks 14:18 22:18 

22:21 37:6 

role 83:2 

room 8:10 57:3 

round 8:3 59:13 

rounds 8:2 

routine 6:4 7:12 

40:15 49:2 69:12 

rule 31:18,20 

54:20 59:2 

rules 41:13 

run 8:12 11:17 

35:1 55:17 67:2 

running 11:9 

sample 12:6 17:4 

18:6 28:20 32:10 

40:8,21 43:5 44:4 

44:6,14,16 45:6 

48:13 49:8 50:6 

55:15,18 59:8 

61:17 63:5,10 

64:19 67:11 

samples 5:6 8:9,12 

15:9 20:10 27:16 

39:10 46:7 49:3 

53:4,6 54:10 

55:12 68:20 76:19 

80:21 

sampling 67:9,9 

67:15 

sanchez 1:6 2:2,3 

6:5,8,19 7:17 11:7 

12:7 13:11,17 

16:8,12,14 18:18 

20:12 21:3 25:11 

28:2,5,16,19 29:2 

29:10 30:4 32:13 

33:10,13 35:12,14 

37:5 38:8 39:8 

41:1,4 44:8,17 

46:1,6 47:11 50:9 

51:9,13,15 52:10 

53:2 54:19 56:17 

57:10 62:7,14,17 

64:6 65:2,5 66:9 

67:16 68:10,13 

69:20 70:3,13,19 

72:2,6,15 73:11 

74:12 75:1,20 

76:5,12,15 77:18 

78:8,20 79:4,19 

80:6 81:13,17 

82:13,21 

saved 65:13 

saving 20:8 

saw 25:2 35:17 

69:11 

saying 28:9 51:6 

55:20 58:6 63:19 

says 61:21 

scale 43:14 63:17 

scanners 66:3 

scanning 59:21 

60:2 66:7 

science 9:3 17:20 

scientist 61:15 

scoops 44:10 

scratch 38:19 

screen 10:13 32:6 

32:7 48:14 61:5 

61:14 63:3,5 68:4 

screened 5:16 

screening 10:1 

18:3 40:17 67:20 

69:8 

screenings 69:12 

sdd 71:2 

second 4:11 23:12 

36:14 

secondary 22:12 

section 23:19 

see 3:9 6:12 12:5 

17:13 26:19 27:7 

39:9 40:1 43:5,11 

43:13,17,19 47:4 

48:4 50:17 51:1 

53:19 58:2 59:13 

60:3 64:7,11 69:1 

71:21 74:10 75:11 

79:10 80:13 83:8 

seeing 10:17 53:21 

65:9 81:3 

seen 11:14 16:14 

23:19,20 36:3 

38:3,4 54:18,20 

61:15 

sem 66:7,11 67:2 

72:3,11 

send 61:11 

sending 55:7 

sense 15:1 32:8 

45:10 49:12 

sensitive 15:18 

16:3 78:12 

sensitivities 62:18 

sensitivity 11:4 

60:10 62:20,21 

64:1 74:3,6 

separate 46:8 

48:19,20 82:12 

separated 59:11 

separation 45:1 

51:2 

separations 40:1 

67:18 

sepiolite 37:20 

38:4 65:16 

series 59:9 

serpentine 10:14 

10:15 11:1,6 

15:20 41:16,17 

serpentines 18:12 

session 1:4 2:6 

47:7 83:9 

set 6:6 7:19 8:1 

16:5,7 30:19 

69:13,21 70:4,8 

70:11 71:4,9 84:6 

sets 48:9 54:8 

settled 9:20 10:11 

shear 22:15 

sheets 39:11 

ship 62:8 

shoot 32:3 

showed 27:19 

side 17:4,11 20:2 

21:4 24:20 25:8 

30:17,17 57:20 

61:3 74:11 81:8 

sides 24:17 

signature 84:17 

silicon 31:10 

similar 7:10 32:19 

s 

s 2:1 

safe 18:16 46:20 

47:5 68:7 

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Session C Interpretation of testing data November 28, 2018 

[simply - takes] Page 14 

simply 57:19 

75:20 

sit 77:5 

sitting 24:4 76:18 

situation 15:5 

size 44:13 58:4 

59:3 

sized 66:21 

sizes 58:7 

slide 45:12 60:2 

slides 44:11 60:3 

sliding 22:21 

slightly 22:19 

81:21 

small 52:1 58:8 

63:12 64:11 

smallest 63:12 

sme 41:6 

smith 27:18 

society 41:6 

soil 15:9,11 28:20 

solid 30:1,21 

31:14,21 32:1 

soluble 37:11 

solution 30:1,21 

31:15 32:1,2 

37:13 57:9 

solves 20:7 

somebody 17:10 

61:21 

somebody's 43:10 

54:2 

something's 49:8 

somewhat 30:19 

67:4 

sophisticated 8:21 

9:2 

sorry 34:2 47:13 

76:17 

sort 47:8 67:9 

sorts 5:10,13 

14:18 43:6 56:10 

69:6 72:18 

sounds 39:6 50:14 

source 15:21 

sources 15:21 55:5 

south 24:15 55:16 

southwestern 21:9 

speak 12:11 13:14 

24:9 

speaker 6:2,6,9 

7:14 11:4 12:3 

13:2,16 15:15 

16:11,13 17:18 

20:1,13 25:9 28:8 

28:13,18 29:1,5 

30:3 32:4 33:7,11 

35:7,13 36:20 

38:6 39:6 40:16 

41:3 44:4,12 

45:15 46:5,15 

49:16 51:1,11 

52:8,21 54:14 

60:20 62:9,16 

64:4 65:1 66:2 

67:8,19 68:12 

69:13,21 70:8,18 

70:21 72:5,14 

73:9,18 74:14 

75:17 76:3,9,13 

76:20 78:2,5,10 

78:21 79:17 80:4 

81:9,15 82:11,19 

speaking 5:4 13:2 

21:16 22:8 23:18 

25:11 29:4 31:17 

spec 56:9 

special 61:11 

species 27:18 

specific 9:8 12:16 

25:7 33:5 50:17 

53:16 69:5 79:20 

82:17 

specifically 51:15 

specifics 13:12 

35:4 36:2 

specified 15:4 

specify 69:17 

spend 64:4 

spin 45:6 

spine 36:13 

stage 36:13,14 

stages 36:12 

standard 11:11,11 

11:16 12:14 16:7 

42:13 49:2 54:21 

75:9 

standardly 7:12 

standards 59:9 

66:1 77:10 

standpoint 41:5 

52:7 75:6 

stands 7:21 

stanton 27:12,15 

stanton's 27:10 

start 2:2 3:7 30:9 

31:5 36:5 58:13 

started 56:17 

state 81:19 84:1 

statements 9:8 

22:7 

states 4:16 19:11 

23:21 

stenographically 

84:10 

step 18:7 45:16 

46:2 49:11,18 

steps 4:13 7:11 

45:3,4 

stop 12:9 13:9 

57:8 65:6 67:13 

70:2,5 

straight 13:6 49:3 

strain 72:17 

strength 22:15 

50:15 

stringent 41:13 

structure 4:5 10:5 

10:6,18 30:9,11 

31:3,6,8 32:19 

38:1 

structures 10:9 

52:6 65:18 72:19 

student 2:10 68:16 

68:17 

studies 27:19 38:2 

79:5 

study 27:14 

stuff 18:10,10,15 

18:16 19:21 20:11 

23:2,11 28:7 

29:15 55:9,10 

56:3 58:8 61:9 

subjected 26:16 

substituting 30:10 

31:5 

subtle 82:7 

success 72:11,19 

sudden 37:15,15 

sufficient 27:6 

58:19 

summarize 67:7 

supposed 42:15 

sure 6:5 8:13 

12:14 43:21 55:6 

56:15 58:12 73:14 

surface 23:4 24:5 

surfaces 72:16 

surrounding 

23:10 

sworn 84:7 

symposium 3:2 

system 33:3 41:18 

42:21 69:14 

systems 7:9 

t 

take 2:8 7:10 10:8 

32:11 34:4 39:17 

43:9 45:4,5 61:2 

61:12,15,20,21 

62:2 66:12 74:4 

78:21 83:4 

takes 34:13 75:3 

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Session C Interpretation of testing data November 28, 2018 

[talc - tremolite] Page 15 

talc 1:3 3:6 5:7,8,9 

6:14,16,18 7:3,8 

8:15 9:18 11:19 

12:2,20 13:6,13 

14:6,7,9 15:15 

16:5,15,16 18:19 

18:21 19:4,20 

20:21 21:14 23:20 

24:13,15 25:3 

26:12,14,15,17,20 

27:1 28:14 29:5 

29:19,21 31:4,9 

31:12 32:12,14,15 

32:20 38:4,7,19 

38:20 39:1,3,4,9 

39:10,15 40:3,6 

41:16,17 42:2 

46:7 47:3 48:18 

48:21 52:9,11 

53:1,3 54:16 55:1 

55:4,7,8,13,15 

56:4,7,8 57:18 

59:8 63:10 64:18 

65:16,16 68:6,20 

72:7,8,21 79:6,9 

81:6 82:14,18,20 

talcs 2:17 19:2,10 

22:1 25:1,15,16 

25:16,19 26:1 

27:10,13,16,19 

29:13,16 34:8,15 

34:15 40:6 54:17 

54:18,21 58:9,15 

58:21 

talk 3:3 6:2 13:11 

18:9 19:3 22:1 

57:11 68:16 80:1 

talked 8:20 21:8 

21:21 25:14,14 

27:13 32:6 35:18 

38:10 44:13 

talking 5:7 7:8 

19:16 20:18 36:1 

36:19 39:3 42:1 

54:21 66:16 69:11 

82:17 

talks 3:13 39:2 

target 74:15,19 

taught 5:4 

technique 9:1 11:5 

49:4 50:13 72:9 

72:12 

techniques 9:2 

10:7 16:4 27:6 

66:11 73:7 

technologies 7:1 

70:15 

technology 66:2,6 

66:10 

tell 13:4 40:11 

42:2 52:11 61:16 

68:6 

tem 7:18 8:12,16 

40:13 41:8 54:2 

58:1 62:12,14,19 

63:6,10,20 64:10 

65:8 66:16 

temp 81:9 

temperature 

21:13,15,20 26:17 

37:14 80:18 81:11 

temperatures 23:1 

37:10 

ten 45:5,8,11,12 

63:5,20 67:5 70:1 

76:6,8 

tens 69:16 

tensile 22:15,15 

term 14:16,17 

30:1,5 

terminology 74:19 

terms 57:3 

test 4:1,9 8:7 

10:20 11:14 16:10 

16:19,21 17:11 

49:5 78:13 

tested 15:9 19:10 

testing 1:4 2:15,16 

2:16 4:16 5:19 6:3 

6:7 7:12,16 8:2,15 

16:20 18:1,11,13 

18:19 20:10 25:1 

25:3 33:9 53:8,9 

58:17 74:15 82:12 

texture 39:4 40:7 

thing 3:12 17:12 

39:8 40:18 49:14 

62:13 65:8 79:4 

things 3:7 5:4,8,13 

7:10 9:19 13:18 

16:20 20:7 27:3 

29:15 37:12 40:5 

42:13,15 53:9 

55:11,21 59:11 

60:5 62:15 64:18 

65:18 66:4 67:10 

69:7 71:3 72:18 

79:6 80:10,12,13 

80:17 82:5 

think 3:8,15 5:18 

6:19 7:14 12:17 

14:11 16:12 17:11 

17:16 18:18 19:13 

19:15,17 25:18 

32:14 34:7,8,13 

39:8 41:4 46:1 

49:7 55:3 57:10 

58:1,18,21 59:16 

61:20 64:15,16,19 

69:5,7 73:8 77:18 

79:8 81:6 83:7 

thinking 20:16 

46:17 82:15 

thinner 72:7 

thought 32:3 

thousands 69:16 

three 31:1 45:12 

60:2 

threshold 77:8 

thresholds 70:4 

throw 71:6 

throwing 30:14 

thrown 5:11 30:6 

tied 67:5 

tiered 57:5 

tiers 57:7 

tight 19:14 

time 16:13 19:2 

20:9 28:5 45:4 

50:19 56:15 62:3 

64:7 70:8 72:6 

73:8 75:8 84:6 

times 19:1 26:7 

45:7 56:2 58:14 

titanium 80:21,21 

today 11:18 21:7 

25:17 26:2 30:7 

tolerance 18:8,9 

74:18 

tool 10:1 64:20 

top 70:1 

total 63:18 

totally 57:2 62:5 

townsend 84:3,18 

tox 78:21 

toxic 78:3 

toxicological 

75:16 

toxicologist 46:17 

73:19 

toxicologists 74:2 

toyed 34:10 

trace 68:15 

training 4:18 

71:14 

transcript 84:10 

transparency 65:9 

treated 82:2,3 

tremolite 30:6,8 

30:13,18 31:19 

34:11,12 35:2 

36:3 42:6,9 44:1 

52:16,18,20 82:6 

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Session C Interpretation of testing data November 28, 2018 

[tremolitic - workers] Page 16 

tremolitic 34:14 

trillion 60:16 

trivial 43:16 

true 9:1 29:5 

underground 14:1 

understand 13:3 

27:7 42:21 

understanding 

v waters 37:9 

way 6:14 12:7 

23:4 26:14 30:8 

35:1 44:15 45:16 

validation 59:19 

valley 19:7 21:21 

25:14,21 62:21 64:1 84:11 

truly 61:19 

try 18:6,14,15 

30:4,5 53:18 

60:13 64:2 67:6 

25:21 

understands 83:2 

underwent 26:11 

undetectable 29:8 

unfortunately 

van 19:8 

vanderbilt 25:15 

25:15 27:14 38:10 

38:13 39:2 

variation 31:7 

45:17,20 47:16 

48:1,16,19 59:20 

60:10,14 61:3 

62:6,9 63:7 68:1 

70:11 71:21 73:3 

68:14 77:1,5 80:3 

trying 9:5 18:2 

40:17 51:1 53:20 

54:7 57:4 69:1 

35:4 50:13 

uniform 6:14 

unique 4:5,6 

united 4:16 19:11 

varied 77:13 

variety 6:11 

various 20:21 35:8 

49:18,21 50:7 

78:7 84:13 

ways 18:5 21:1 

62:18 

we've 59:8 61:13 

72:10 75:1 77:20 

78:18 

turn 39:17 64:2 

turned 26:12,13 

23:21 

university 68:18 

unknown 15:7 

33:17 34:8 48:13 

vein 24:1,2 36:16 

veins 25:10 

vermont 79:8 

versus 13:5 35:11 

66:10 68:3,13 

72:10,10,19 77:18 

weakness 24:2 

50:15 

turns 26:15 

twenty 19:20 

two 4:13 10:17 

30:19 41:15 45:12 

55:4,12 60:18 

unsafe 46:20 

upper 35:2 

ups 63:17 

44:1 52:9 

vial 45:6 

view 10:19 67:19 

75:21 

wear 80:9 

weeding 19:16 

weigh 60:1 

weight 12:1 47:21 

48:19 81:7 

type 3:15 7:3 

11:10 13:7 15:19 

15:20 22:16 35:7 

40:19 52:2 54:9 

54:10 58:18 69:17 

70:12 73:11 

types 5:5 16:2 

29:16 35:21 40:11 

51:14 73:13 

typically 17:6 

22:13 32:20 39:19 

58:10 70:17 72:15 

81:7 

upstate 22:1 

use 7:15,17 8:9,17 

10:6 14:15 15:17 

18:11 29:8 31:18 

42:8 43:8 45:16 

45:19 46:18 47:1 

48:6 58:12 61:13 

62:10 64:20 67:20 

70:16 72:20 74:19 

77:7,8 

useful 34:13 

user 56:14 

uses 8:7 56:2 81:6 

usp 43:2 57:11 

59:5 73:21 75:9 

75:18 77:19 82:13 

82:16 83:1 

usually 22:11 

32:16 33:13 54:11 

56:13 

utility 48:5 

views 82:15 

volume 60:6 

48:7 51:20,21 

welcome 2:20 

wes 69:13 

wet 37:7 

whiteness 56:4,6 

whiter 56:7 

width 36:6 60:4 

widths 36:8 

wiley 36:1 42:1 

wise 52:17 

wit 84:1 

witness 84:5,15 

wondering 36:20 

word 31:16 38:9 

work 2:12 7:19 

13:12 27:10 31:12 

38:12 59:14 60:20 

65:15,19 68:11 

70:14 72:10 74:10 

76:12,18 77:1 

worked 2:14 

workers 79:14 

w 

w 27:18 

wait 73:14 

waiting 59:9 65:6 

walk 9:17 14:2,3 

21:4 24:3 

walking 24:10 

want 3:17 6:9,10 

16:5 17:5 28:13 

35:4 40:20 50:3 

56:21 61:4,5 

69:17,17,19 73:15 

74:9,19 76:6 

wanted 3:3,12 

49:14 

wants 39:16 73:9 

washing 45:2 

waste 24:20 

water 37:3,8,15 

u 

u.s. 55:1,16 

ultimately 75:13 

ultramafic 29:14 

uncommon 55:20 

55:21 

undergoing 22:14 

undergraduate 

68:17 

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Session C Interpretation of testing data November 28, 2018 

[working - zones] Page 17 

working 4:3 43:2 

59:6 75:18 77:11 

83:6 

works 30:9 70:21 

73:6 

world 17:1 19:13 

46:7 68:21 78:1 

83:6 

worse 9:21 

written 12:8 

wrong 25:20 61:6 

x 

zones 22:14 23:16 

36:21 

x 9:21 10:7,13,19 

12:4 57:14 63:9,9 

75:5,9 

xrd 11:8 12:9,13 

58:18 

xrf 62:5 

xrm 15:17 

y 

yeah 6:8,19 12:7 

13:11 16:8,12 

17:12 18:18 20:12 

21:3 29:1,11 

33:13 34:1 36:2 

39:8 41:4 44:17 

47:11 48:5 56:16 

67:16 68:10,13 

69:6,20 70:4 

72:14 73:10,11 

77:18,19 79:13 

80:6 

years 2:14 18:20 

41:6 60:21 61:1 

york 22:1 25:15 

26:1 

young 7:20 

z 

zero 16:21 

zonation 24:16 

zone 22:18 23:1 

24:17,18 35:9 

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