uc cein research integration · (rf) nec = 223 ± 56 ppb isochrysis galbana miller et al. es&t....

UC CEIN Research Integration

Property/Activity Relationships

Theme 1:

ENM Physical/Chemical

Characteristics

Theme 2:

HTS and Predictive Toxicology

Environmental Modeling

Theme 3:

Environmental Fate & Transport; Life Cycle Modeling

Theme 6:

Exposure Modeling; QSARs

Ecosystems Impacts

Theme 4:

Terrestrial Impacts (Food supply)

Theme 5:

Estuarine Impacts (Benthic and

Pelagic Organisms)

Societal Outputs Theme 7:

Stakeholder Engagement and Translational Activities

Theme 8: Educational Programs and Workforce

Development

Major Goals for Renewal • To develop hazard ranking and structure-activity relationships (SARs) that relate the

physicochemical properties of compositional and combinatorial ENM libraries to toxicological responses in cells, bacteria and multi-cellular organisms, with a goal to develop predictive toxicological paradigms to understand the environmental impact of nanotechnology;

• To estimate environmentally relevant exposure concentrations of high-volume and potentially high-impact ENMs (primary nanoparticles as well as commercial nano-enabled products) using life cycle assessment (LCA) and fate and transport modeling to obtain quantitative information about the uptake, bioaccumulation, and hazard of nanoparticles in terrestrial and estuarine ecosystems;

• To determine the potential of ENMs, selected through high throughput screening (HTS), SAR analysis, LCA and multimedia modeling, to impact ecosystem services in model ecosystems. These include terrestrial mesocosms with food crop plans and bacterial populations that control nutrient cycles, and estuarine mesocosms comprised of a representative natural food web;

• To use UC CEIN knowledge and environmental impact assessment tools to educate the next generation as well as to inform and engage academic, government, industrial and societal stakeholders involved in risk perception, regulatory decision-making, policy development, risk management and safe implementation of nanotechnology.

Approach

Incorporate commercial (e.g., semiconductors, catalysts, Ag, silica, CNT) and newly evolving (e.g., graphene, multifunctional) ENMs into our libraries

Use of ENM libraries and sources to develop additional safer by design strategies

More HTS on organisms, including the use of genomics to establish new predictive paradigms

Lifecycle analysis premised on commercial and manufactured nanomaterials

Develop sophisticated and predictive SAR models based on libraries, HTS and machine learning tools for environmental prediction making

3

Approach

More integrated modeling of ecosystems impact premised on critical services and ecological processes that can be used for environmental decision analysis

Improved handling of large data sets through nano informatics and decision-making tools to build missing knowledge domains in collaborative projects

Streamlined center outreach efforts to engage industry, regulators, the public and experts in nano EHS roundtable interactions

Continued development of educational tools and building of a diverse and multidisciplinary nano EHS workforce that prepare us for implementation of a sustainable technology

4

Meng et al. ACS Nano. 2009

Nel et al. Accounts Chem Res, 2012

http://www.nap.edu/catalog.php?record_id=11970

http://www.epa.gov/ncct/toxcast

“Toxicity Testing in the 21st Century: A Vision

and a Strategy”

US National Academy of

Science (2007)

• Wide coverage of toxicants

• Robust scientific platform for

screening

• Predictive tests utilizing

toxicity mechanisms

• High throughput discovery

• Connectivity to in vivo

Current: One material at a time descriptive animal testing

Proposed: Rapid mechanism-based predictive testing

100’s/year 1000’s/year 10,000’s/day 100,000’s/day

High Throughput Bacterial, Cellular, Yeast, Embryo or Molecular Screening

Immediate Relevance

Predictive Environmental Toxicology in CEIN

Prioritize in vivo testing

at increasing trophic levels

Exposure/Life cycle Approach Google Images

http://images.google.com/imgres?imgurl=http://www.chez.com/rodent/Muridae/sigmodon.gif&imgrefurl=http://www.chez.com/rodent/Muridae/Muridae.html&h=245&w=320&sz=38&tbnid=8p6yiiO4FAYJ:&tbnh=86&tbnw=113&hl=en&start=3&prev=/images?q=rat&svnum=10&hl=en&lr=

http://images.google.com/imgres?imgurl=http://www.sih.m.u-tokyo.ac.jp/chem1.gif&imgrefurl=http://www.sih.m.u-tokyo.ac.jp/chemi.html&h=210&w=313&sz=72&tbnid=jo_uhGeOKQQJ:&tbnh=75&tbnw=113&hl=en&start=1&prev=/images?q=c+elegans&svnum=10&hl=en&lr=

Nano-Ecotoxicology Concerns

CO2 N2

• Bioavailability, bioaccumulation, & biomagnification

• Ecosystem “services”, including: – food production

– nutrient cycling

• Predictive capacity – mechanistic understanding

– modeling

population

Assays (growth;

mechanisms)

Mesocosms (interactions)

ecosystem

Microcosms (predator/prey;

biodiversity)

community

Modeling (dynamic energy budget)

Ecological Nanotoxicology: scales & approaches Holden, Nisbet, Lenihan, Miller, Cherr, Schimel, Gardea-Torresdey, 2013, ACR

Dynamic Energy Budget (DEB) Modeling

Populations

Communities

CONCEPT

Changes in

ENERGY

Generation

Transduction

Investment

Individual

Organisms

Bio-Effects

Ecosystems

Bacterial Membrane

Cytoplasm

NADH cyt

cyt

cyt O2 + H+

H2O

e-

e-

e-

e-

H+

H+

H+

H+

ADP

ATP

NP

•Electron Transport Chain (ETC) •Lyon & Alvarez. ES&T (2008)

•Membrane Potential (MP) Lyon & Alvarez. ES&T (2008)

NP

ROS H2O2

OH• O2

-

•Membrane Integrity (MI) Priester et al. ES&T (2009), Su et al. Biomaterials (2009)

•Reactive Oxygen Species (ROS) Nel et al. Science (2006) Priester et al. ES&T (2009)

4. Assays: stress/damage

Allison Horst

Bacterial Nanotoxicology Approaches 1. Bacterial strain selection

3. NM Dispersion

Horst, et al. J.Nanopart. Res. 2012

Horst et al. Small. 2013

2. Oligotrophic media

Direct Interference

Indirect Interference

HCS: Assessment of Fluorescence & Colorimetric Assay Interferences.

Horst et al. Small. 2012

Bacteria: Modeling Population Growth and Effects Mechanisms By

Dynamic Energy Budget (DEB)

Klanjscek et al. 2012, PLoS ONE

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 20 40 60 80 100

Lo

g (

RO

S w

ith

QD

/RO

S w

ith

dis

so

lved

Cd

)

Total Cd (mg/L)

Data

Best model

Other model 1

Other model 2

Klanjscek et al. 2013, Ecotoxicology

Modeling Cd(II) effects on Population Growth Modeling CdSe QD-specific effects

Population Scale

hydroponic crop plants

planktonic bacteria

Community Scale soil microbial communities

Ecosystem Scale agricultural plants in soil

plant-microbe root symbioses soil microbial communities

Priester et al. 2012. PNAS.

Hernandez-Viezcas et al.

2013. ACS Nano. in press

ENMs in Agriculture • Working on

determining bioavailability of ENMs to different plants

• Understand different ENM application methods and releases

NP stability and mobility in suspension is strong function of aqueous chemistry

Nanoparticle Freshwater Groundwater Seawater

TiO2

CeO2

ZnO

CuO

Ag – citrate

Ag – PVP

Pt

Pd

Fe(0) - coated

Most Me & MOx NPs are stable in freshwater due to NOM

Lower stability in groundwater due to high Ca2+

Most NPs are unstable in seawater due to high ionic strength

Longer polymeric coating (e.g. PVP) increase stability

Deposition of unstable solutions occurs in min to hr

Stability is a strong function of surface charge, which is a function of [NOM], pH, and ionic strength

Keller et al. ES&T. 2010; Thio et al. ES&T. 2011; Thio et al. J HazMat. 2011

Suspensions are

considered stable

when the particle

concentration

remains constant

CeO2

18

NSF: DBI-0830117

Flow of ENMs through global economy

Arturo Keller, Sangwon Suh, Sheetal Gavankar University of California Santa Barbara

• Starting with an ENM market survey, the mass flow of ENMs through the global economy was estimated

• 65-90% of ENMs will end up in landfills

• ~85% ENMs that pass through a WWTP will end up in biosolids, which may be applied to soils

• Fraction of ENMs going to water bodies and atmosphere are small, and dominated by TiO2, SiO2, Fe oxides and ZnO

(all flows in metric tons/yr, 2010 estimates from Future Markets, Inc.)

Keller, Suh et al., 2013

Theme 5: Marine and Freshwater Ecosystems Impact – Hunter Lenihan

Marine Pelagic

Freshwater Stream

Marine Benthic 2° Production

Phytoplankton 1° Production

Zooplankton

Higher

consumers

(Seafood)

Bio-accumulate

ENMs

Research Emphasis on:

Ecosystem services: Food webs,

Biodiversity

Ecological Processes: Production,

Trophic-transfer

Sunlight

Marine Pelagic

Daphnia

20

ZnO mg L-1 (ppm)

RF

ZnO mg L-1 (ppm)

Hypothesis: ZnO disrupts membrane function, produces ROS leading to

cell death, which leads to reduced population growth

Reactive oxygen species (ROS)

production

Membrane permeability (Cell death ) Mitochondrial membrane potential

ZnO mg L-1 (ppm)

Dynamic Energy Budget

(DEB) modeling of NEC

NEC = 223 ± 56 ppb

Rela

tive f

luo

resc

en

ce (

RF

)

Isochrysis galbana

Miller et al. ES&T. 2012

21

Kahru and Dubourguier, 2009 (Toxicology)

Algae

Crustaceans

Fish

Bacterial

Dose-dependent effects on diatom growth – ZnO

Med

ium

L(E

)C5

0 (

pp

m)

0.01

0.10

1

10

100

1000

10000

100000

Use of Nanoecotoxicity Meta analysis Data to plan Marine Studies

NEC = 223 ug L-1

Dose-dependent effects on diatom growth – TiO2



Theme 1:


Characteristics

Theme 2:



Theme 3:


Theme 6:


Ecosystems Impacts

Theme 4:


Theme 5:


Pelagic Organisms)




Development

Copper Products

• Cu NPs, 40nm

• Cu Bulk, <60 µm

• CuO NPs <50nm

• CuO Bulk, <5µm

• Cu(OH)2 DuPont kocide 2005 (Fungicide/Bactericide)

• Cu(OH)2 DuPont kocide 3000 (Minimal dose recommended: 84 mg/m2)

• CuCl2

Exposure Characterization

• Batch studies

• Aggregation

• Dissolution

• Changes in pH, IS, NOM

• Effect of sediments

• Cycles of salinity and flow

• LCA

• Exposure modeling

HTS Characterization

• Individual cells

• Zebra fish embryos

HCS Characterization

• Herring/Killifish

• Clams

• Oysters

• Annelid worms

Estuarine mesocosm studies

• Selection of Cu ENMs

to evaluate

(applications)

• Selection of

environmental

conditions

• Selection of organisms

and endpoints

• Selection of exposure

concentrations

Hypotheses to be tested with regards to

• Actual exposure • Hazards

Hypotheses

Predicted Initial [ENM] in SF Bay

Estimates of ENM concentrations at point of release indicate ng/L to ug/L levels to be expected

Alicia and Prof. Sharon Walker

Samples:

#1: Diluted Colon Effluent (directly from colon)

#2: Diluted Greywater Fluid (from synthetic greywater stock)

Directly from septic tank effluent:

#3: Baseline Septic Tank (no nanoparticles added)

#4: Septic Tank (week 1, nano Cu)

#5: Septic Tank (week 2, nano Cu)

#6: Septic Tank (post week 1, nano Cu)

#7: Septic Tank (post week 2, nano Cu(OH)2 CuPro)

#8: Septic Tank (post week 3, nano Cu)

Aims:

• Toxicity evaluations using zebrafish embryos HTS

• Quantify the Cu contents from these effluents

• Identify Cu speciation

• Correlate the properties of Cu-formulations and toxicological outcomes with the

aim to establish SARs

Artificial Colon and Septic Tank Effluent

-20.0%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

0 2 4 6 8 10

nano Cu Micro Cu nano CuO

Micro CuO CuPro Kocide

% h

atc

hin

g

% h

atc

hin

g

Cu Concentrations (mg/L) Nominal Particle

Concentrations (mg/L)

-20.0%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

0 2 4 6 8 10

nano Cu Micro Cu nano CuO

Micro CuO CuPro Kocide

Previous Converted dose

ZHE1

Zn2+

% hatching of zebrafish

embryos at 72 hpf

Effects of CuO NM on Herring Development

0

20

40

60

80

100

0 0.005 0.01 0.05 0.5 5 25 50 100 200

% a

bn

orm

al

ppm Cu in CuO NM

CuO NM: Percent dead and abnormal (day 11pf)

Hatched Abnormal

Hatched Dead

UnhatchedAbnormal

0

20

40

60

80

100

0 0.005 0.01 0.05 0.5 5 25 50 100 200

% a

bn

orm

al

ppm Cu in CuSO4

CuSO4: Percent dead and abnormal (day 11pf)

Hatched Abnormal

Hatched Dead

UnhatchedAbnormal

Images: E. Fairbairn E.A. Fairbairn & G.N. Cherr

water

sediment

ENMs

Salinity

Estuarine MendNano Model

Estuarine Mesocosm f r e s h w a t e r i n f l o w + C u N P s ; C u N P - c o n t a m i n a t e d p h y t o p l a n k t o n ; C u N P c o n t a m i n a t e

p a r c u l a t e o r g a n i c m aterial r C u - N P b o a t - b o o m p a i n t

Copper Project-Terrestrial Plants

Alfalfa and Lettuce Hydroponic Study

Alfalfa Lettuce

Jie Hong, PhD Student Jorge Gardea-Torresdey, Department of Chemistry Environmental Science & Engineering PhD Program

University of Texas at El Paso

Theme 4



Theme 1:


Characteristics

Theme 2:



Theme 3:


Theme 6:


Ecosystems Impacts

Theme 4:


Theme 5:


Pelagic Organisms)




Development

Tools to Develop Predictive Toxicology: Composition and Property-based Nanomaterial Libraries

TiO2, ZnO, CuO, NiO, Cr2O3 etc

Transition MOx

RE Oxides

Amorphous Fumed

Crystalline Mesoporous

Silica

SWCNT

MWCNT

fCNT

Carbon Nanotubes

Compositions

Godwin et al, EST. 2009 Thomas et al. ACS Nano. 2011 Nel et al. Small. 2012

Crystal Structure

ENMs -

- - -

Size

Shape AR

Metals

Surface chemistry

Eg

CB

VB

Eg

CB

VB

Eg CB

VB

Band Gap

Dissolution chemistry

-

+ - +

+

+ + +

Surface Functionalization

CO

OH

COO

H

HOO

C

COOH

COO

H

HO

OC

N

NH 2

CN

Ts

MOx

Silic

a

Surface Charge

Nel et al. Nature Materials. 2008 Xia et al. ACS Nano. 2008 George et al. ACS Nano. 2011

33

Cu, Ag, Pt, Co

Metals

CeO2, GdO3, La2O, SbO3 etc

DynaPro

Plate Reader

Materials of interest OECD-WPMN (2008)

SWCNTs MWCNTs Ag nanoparticles Fe nanoparticles Ti dioxide Al oxide Ce oxide Zn oxide Si dioxide Nanoclays Dendrimers Au nanoparticles Fullerenes (C60)

No Agent

Time (h)

BSA

2 mg/mL

2% FBS

100 nm

500 nm

1000 nm

5% FBS

1% FBS

0 1 2 3 4 5 6 8 10 12 14 16 18 20 22 24 H 2 O

BEGM DMEM

LB TSB

SD YPD H 2 O

BEGM DMEM

LB TSB

SD YPD H 2 O

BEGM DMEM

LB TSB

SD YPD H 2 O

BEGM DMEM

LB TSB

SD YPD H 2 O

BEGM DMEM

LB TSB

SD YPD

TEM

DLS/ ZetaSizer

MALLS

SEC

Groupings: Properties/SARS Toxicological mechanisms Usage/exposure

Appropriate Physicochemical Characterization

etc

Properties

Intrinsic

Material as acquired or synthesized

Extrinsic

Altered properties in

biological medium

Tox SAR

Properties proximately

associated with injury

High Throughput DLS

Tools to Develop Predictive Toxicology

Silica Types: SAR - strained silanol rings and surface OH -

COOH-MWCNTs

sw-NH2-MWCNTs

NH2-MWCNTs

PEG-MWCNTs

PEI-MWCNTs

Raw

COOH

R1 R3

R5 R7 R6

R0

{111

}

{111

}

Tools: Examples of Libraries

CeO2 shape and AR library: SAR – Long aspect ratio and Ce valency

R1 R3

R5 R7 R6

R0

{111

}

{111

}

Surface Functionalized MWCNT Library

Mitochondrial damage

ROS generation

Stress response

Cellular apoptosis

Reporter genes for

sublethal effects

Cell growth

Assessment of Inflammation

RBC lysis

Tools: Cellular High Throughput Screening

George et al. ACS Nano. 2010


Nel et al. ACR. 2012

Zebrafish HTS platform automated imaging of

developmental abnormalities and transgenic responses

Hatching Start feeding

NP

s

NP

s

Embryonic development Larval effects

0 4 24 48 72 120

Image acquisition @ 24 hr intervals

HTS Brightfield

(Developmental, morphological abnormalities)

Ctrl

CuO

ZnO

NiO

Co3O4

Ag

CuO

HTS Screening

Transgenic Fish)

neg

pos He

at

sh

oc

k p

rote

in 7

0 Robotic pick-and-plate system

Xia et al. ACS Nano. 2011

Lin et al. ACS Nano. 2011


Newport Green

Dissolution, shedding

toxic Ions, e.g., ZnO, CuO

Metal Metal ions

Nucleus

Cationic toxicity

e.g., cationic polystyrene,

PEI-MSNP

+ +

+ +

+

+ +

+ +

+

+ +

+ +

+

+

+

+ +

+

+

+ +

+ +

+

+

mitochondria

lysosome +

+

+

Inflammasome

activation

e.g., CNT, CeO2 rods

Nucleus

Inflammasome

IL-1β

IL-1β

pro-IL-1β

NALP3

O2· – O2

e–

h+

Redox activity and ROS

e.g., TiO2, CuO, CoO

A B C

D

Photoactivation

e.g., TiO2

Conduction Band

Valence Band

-

+

ΔEg

hν

N

P

O O

O O P

O O

O O

P

O O

O O

P

O O

O O

N N

N

Si O

O Si

O

O Si

O

O

Si O

Membrane Lysis

e.g., SiO2 nanoparticle,

Ag-plates

Silica

Cell membrane

O O

F E

Tools: Mechanistic Toxicological Pathways in Cells

for Predictive Toxicological Modeling

Nel et al. Nature Material, 2009

Xia et al, ACS Nano, 2008




George et al JACS 2011


Xia et al ACS Nano. 2009

Zhang et al ACS Nano 2011

Wang et al. ACS Nano. 2010

Wang et al ACS Nano. 2011

ZHE1 Hatching Enzyme

Abiotic ZHE 1 Assay for MOx Dissolution Predicts Zebrafish

Embryo Hatching Interference


Lin et al. Small. 2012

Polyhistidine tagged

ZHE1 Plasmid BL21 E. coli

strain

pET3c-ZHE1

IPTG induction

MOx ions

Flu

oro

gen

ic S

ub

str

ate

Abiotic Enzyme Assay

Automated Zebrafish HTS

En

zym

atic a

ctivity

* *

* *

1-4

0

0.2

0.4

0.6

0.8

1

1.2

5 24

En

zym

atic a

ctivity

* *

* *

1-4

0

0.2

0.4

0.6

0.8

1

1.2

5 24

Bacterial HTS: Cationic PS MNMs Destabilize Membranes and Inhibit through ROS

(toxicogenomics)

Ivask et al. ES&T. 2012.

CEIN Approaches, Models and Nanoinformatics Tools

Developed for ENMs Environmental Impact Analysis

Exposure Likelihood

Environmental Hazard Ranking

EHR-Nano

Environmental Impact Evaluation

- ENMs F&T prop. - Geographical & meteorological info. - Emissions

Multimedia Analysis M

en

d-N

an

o

ENM Concentrations

& Mass Distribution

ENMs biota uptake parameters HTS/LTS Analysis Tools

Inter-Plate Normalization

Inter-Plate Normalization

Normalized Activity

Smallest Value*

X

X

X

1st Quartile

3rd Quartile

Median

Largest Value*

Outliers

Outlier

Knowledge Extraction:

Toxicity Metrics & QSARs

Data

Studies HTS/LTS



Theme 1:


Characteristics

Theme 2:



Theme 3:


Theme 6:


Ecosystems Impacts

Theme 4:


Theme 5:


Pelagic Organisms)




Development

Standard rodent

Toxicological tests

10-100/year

Biochemical and cell-based

in vitro assays

> 10,000/day

Alternative

Animal models

100-10,000/year

Human experience

1-3 studies/year

Predict

Knowledge

Computational toxicology Critical toxicology pathways

Immediate human relevance High Throughput

Francis Collins et al. Science. 2008

HTS Genome

Proteome Epigenome

Transcriptome

Disease Pathology Phenome

Fig. 2

A

Systems Toxicology at EPA and Tox-21

Disorder Categories being Probed

by Toxcast Assays

In vitro/in vivo datasets Data integration Signatures Computational Modeling

Meng et al. ACS Nano. 2009

Nel et al. Accounts Chem Res, 2012

Nanomaterial Predictive Toxicology

(proportional weighted discovery)

In Vivo Adverse Outcomes

• mechanism of injury

• toxicological pathway

ENM Libraries

of different

composition

and accentuated

Physchem

Properties

Validation

(102 observations

days-months)

(102 – 105 observations/day

by HCS and HTS

approaches

Cellular or Bio-molecular

Endpoints

Mechanistic

Toxicological

pathway

ATS widely accepted to prioritize ENM hazard assessment but not yet ready for quantitative risk assessment or regulation

Hazard ranking and grouping of ENMs could assist regulatory and occupational decision making

ATS and predictive toxicological paradigms can be used to establish hazard categories and material grouping as a 1st tier of testing, which is used to prioritize more costly and elaborate animal studies

Any framework that considers ATS for regulatory purposes needs to be transparent, participatory and engage a broad stakeholder community

A predictive toxicological approach for CNT is potentially helpful for hazard ranking, prioritizing animal experiments, and grouping of materials

The development of hazard ranking, material grouping and SARs can become an integral part of new product development

It is important to consider dose-response extrapolation and exposure scenarios that link mechanistic and predictive toxicological assessment to risk assessment

Provisional Consensus about ATS use for nano EHS

“IMPLEMENTATION OF ALTERNATIVE TESTING METHODS.—To promote the

development and timely incorporation of new testing methods that are not

laboratory animal-based…..”:

‘‘(A) ….develop a strategic plan to promote the development and

implementation of alternative test methods and testing strategies to generate

information used for any safety-standard determination made that reduce,

refine, or replace the use of laboratory animals, including toxicity pathway-

based risk assessment, in vitro studies, systems biology, computational

toxicology, bioinformatics, and high-throughput screening”

‘‘(B) beginning on the date …and every 5 years thereafter, submit to Congress

a report that describes the progress ……”

‘‘(C) fund and carry out research, development, performance assessment, and

translational studies to accelerate the development of test methods and testing

strategies that reduce, refine, or replace the use of laboratory animals in any

safety-standard”

IN THE SENATE OF THE UNITED STATES: a bipartisan bill to modernize title I of the Toxic Substances Control 14 Act (15 U.S.C. 2601 et seq.) –

May 24 2013

Toxicity explained by Dissolution and Conduction Energy

(statistical testing of scientific hypothesis)

Dissolution in BEGM < 13.05

Al2O3, CeO2,

Gd2O3, HfO2,

In2O3, La2O3,

NiO, Sb2O3,

SiO2, SnO2,

TiO2, Yb2O3,

Y2O3, ZrO2

Fe2O3

Fe3O4

WO3

CoO

Co3O4

Cr2O3

Mn2O3

Ni2O3

ZnO

CuO

Ec < -4.80

Ec < -4.22

Metal dissolution in BEGM < 13.05 Al2O3

SiO2

Y2O3

La2O3

Gd2O3

HfO2

Yb2O3

ZrO2

In2O3 NiO

Sb2O3 CeO2

SnO2

TiO2

Ni2O3

Cr2O3

Mn2O3

CoO

Co3O4

CuO

ZnO

Fe2O3 Fe3O4

WO3

-4

-3

-2

-5

Ec (

eV

)

20 10 0 30 40

Metal Dissolution (%)

Low/no Toxic Highly Toxic

George e al. ACS Nano. 2010


Zhang et al. ACS Nano. 2012

Regression Tree

Epithelial

mesenchymal

fibroblast

cellular axis

(progressive,

chronic)

Cathepsin B

Lysosome

-sw-NH2

-NH2

-PEI

+f-

TGF-β1 , PDGF

Lysosome injury Intact lysosome

Lysosome

PF108-CNTs BSA-CNTs

In vitro hazard

ranking of extensive

batches of CNT

materials to prioritize

animal testing and in

vivo hazard ranking

Predictive Toxicological Profiling

NALP3 inflammasome activation

in macrophages (subacute)

IL-1β

-PEG --f- -COOH

BSA-coated

Multiple CNTs libraries

SG65

Arc

Hipco

-NH2

-sw-NH2

-PEI

-PEG (-)

(+++)

Raw MWCNTs

Purified MWCNTs

COOH-MWCNT

f-

CheapTube®

acid treat

carboxylate

Functionalized

(++)

(+/-)

MWCNT

SWCNT Raw-SWCNT

Purified-

SWCNT

Density column purification after BSA and

polymer coating of SWCNT and MWCNTs

Several

suppliers

Pleuronic-coated lung fibrosis

Predictive Toxicology Approaches allows Large Numbers of

Materials to be grouped in Hazard Band Categories

CeO2 Gd2O3

La2O Sb2O3

Yb2O3 Y2O3

etc George e al. ACS Nano. 2010


Zhang et al. ACS Nano. 2012

Nel et al. ACR. 2012

lung injury

SWCNT & MWCNT

Libraries (>5 batches)

Lysome injury

Harmful SARs

Ostwald Ripening

{111

}

{111

}

LAR Metal oxides (2) Rare Earth Oxides (>10)

NLRP3

CoO

Co3O4

Cr2O3

Mn2O3

Ni2O3

etc

Al2O3, HfO2

In2O3, NiO

SnO2, TiO2

ZrO2 etc Strained

siloxane rings H-bonded silanols

High and Low Temp Silicas

(>5 Si types)

+

Oxidative

stress

Inflamma-

tion

Transition MOx’s (>30)

NLRP3

CB

VB

Cellular SAD mg/cm2

In Vivo Dose in Mouse

Extrapolated Human SAD (mg/cm2)

Lung Alveolar SAD mg/cm2

Total Alveolar area = 0.05 m2

brain

lung skin

Total Alveolar area = 102 m2

NIOSH REL for CNTs

= 1 mg/m3

Worker alveolar exposure levels Chronic: 8hr/day x 40wks/yr x 45yr Acute: 24 hr

Dose-response Extrapolations

?

Intracellular dose determination by ISSD modeling

•1st tier – In vitro – Predictive assays to study specific mechanisms of injury

– Rank potency of test materials vs well-defined positive and

negative controls from libraries – Develop quantitative SAR analysis for in silico predictions

•2nd tier – short term in vivo

– Test selected materials within a category/mechanism/SAR – Focused/limited animal studies

– Validate mechanism and potency within a group

– In vivo hazard ranking (pathophysiology of disease outcome)

•3rd tier – short-term or 90 day inhalation studies

– Test the most potent materials within a tier 2 category/group

– Dose-response extrapolation using benchmark materials

to allow risk assessment

– Establish OEL’s

– Use for read-across regulatory decision making

Tiered Approach Using Predictive Toxicological

Modeling for Hazard Ranking and Risk Translation



Theme 1:


Characteristics

Theme 2:



Theme 3:


Theme 6:


Ecosystems Impacts

Theme 4:


Theme 5:


Pelagic Organisms)




Development

UC CEIN Publications

Results found: 177

Sum of the Times Cited : 3214

Average Citations per Item : 18.16

h-index : 27

Publications by Year

Citations by Year

Source: Web of Knowledge

Theme 1 (plus 2 and 6) Publications

Results found: 54



h-index : 20


Citations by Year

Source: Web of Knowledge Includes Cross-Theme Publications

Theme 2 (plus 1 and 6) Publications

Results found: 45



h-index : 19


Citations by Year


Theme 6 Publications (independent of 1 and 2)

Results found: 18

Sum of the Times Cited: 193


h-index : 8


Citations by Year


Theme 3 Publications

Results found: 45



h-index : 12


Citations by Year



Results found: 70



h-index : 14


Citations by Year



Results found: 24



h-index : 9


Citations by Year


NSF: DBI-0830117

NSF: SES-0938099

The Hierarchy of EHS Practices in the US Nanotechnology Workplace C.D. Engeman1,2,3, L. Baumgartner3,4, B.M. Carr3,4, A.M. Fish3,4, J.D. Meyerhofer3,4, T.A. Satterfield2,3,5,

P.A. Holden3,4, B.H. Harthorn*2,3,6 1 Sociology Dpt, UCSB; 2 Center for Nanotechnology in Society, UCSB; 3 UC CEIN; 4 Bren School of Environmental Science & Management, UCSB; 5

Institute for Resources, the Environment, & Sustainability, University of British Columbia; 6 Feminist Studies, Anthropology, & Sociology Dpt.’s, UCSB

*-corresponding author

Journal of Occupational and Environmental Hygiene

(forthcoming, 2013)

Go

vern

men

t re

com

men

ded

pra

ctic

es

Hierarchical approach to exposure controls:

Bey

on

d g

ove

rnm

ent

reco

mm

end

atio

ns

Cleaning practices

Waste management:

Product stewardship: • Advertise /disclose that products contain NMs • Providing nano-specific guidance to customers

regarding product safe use and/or disposal

• Disposing NMs as hazardous waste • Using separate disposal containers for NMs • Having a nano-specific waste handling program • Listing NMs separately on waste manifests

Recommend: Wet wiping, absorbent materials Avoid: Sweeping, use of household vacuum or

compressed air

Monitoring the workplace for nanoparticles

1. (Elimination or substitution of material) 2. Engineering controls 3. Administrative controls 4. Personal protective equipment (PPE) + Respiratory protection

Key findings:

● Practices span current government-recommended

hierarchical approach to MNM exposure controls

● Practices tailored to current MNM hazard and exposure

knowledge reported less frequently than general chemical

hygiene practices

● Product stewardship and waste management practices –

whose influences manifest much farther down the product

life cycle – reported less frequently

● Smaller companies more frequently identified

impediments to implementing nano-protective practices

Analysis based on responses of 45 US-based company participants in

a 2009-2010 international survey of private companies that use or

produce manufactured nanomaterials (MNMs).

NSF: DBI-0830117

Informal Science Education: Oil Spill Clean Up Activity

Christine Truong, Catherine Nameth, Hilary Godwin University of California Los Angeles

CEIN ’ s Education group developed an

environmental nanotechnology activity called

“Oil Spill Clean Up Simulation”. This activity

is designed for audiences ages 8 and older

and can be used as either a small group

activity or a 5-10 minute demonstration.

During the activity, participants learn how

nanotechnology can help clean up a

simulated oil spill (made with corn oil and

water) using “nano sand”. Each grain of

nano sand is coated with a 1-nanometer

thick layer of silicon compound (silicon

dioxide + trimethylhydroxysilane). This nano

coating gives the sand hydrophobic

properties and allows it to bind with other

hydrophobic substances, such as oil. After

the nano sand binds with the oil, the oil-

soaked sand falls to the bottom of the cup,

leaving cleaner water behind.

This simulation activity demonstrates how

nanotechnology can be applied to help the

environment. A demonstration video can be

viewed on YouTube:

http://youtu.be/ckQDg3WHPXw

uc cein research integration · (rf) nec = 223 ± 56 ppb isochrysis galbana miller et al. es&t....

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