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Supercharge your products with BNNTs
400 m2/g
4
B = 767 barn, N = 1.9 barn
950 C
1.3 TPa
Insulating (5.5 ev bandgap)
Excellent
1000 W/mK
Nanomaterial handling is easy and safe
Disrupting markets, not your processes
Improved mechanical and
thermal performance and more
Compatible with pultrusion,
fiber extrusion, etc..
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Compatible with x,y,z process
Learn MoreImprove heat transfer and
cooling even at extreme temperatures
Compatible with mineral oils,
water based solutions,
flurinated fluids and more
Improve elongation and
blah blah
Compatible with x,y,z process
Ground Breaking Performance
4.2x and 12.1x tensile strength and
Young's modulus improvement
over neat PVA
Tensile Strength 7 GPa
Young's Modulus 600 MPa
Loading Percentage 1%
15x and 4x thermal conductivity
improvement over neat TPU
In Plane Thermal Conductivity 15W/mK
Out of Plane Thermal Conductivity 2.25 W/mK
Loading Percentage 1%
Super hydrophobicity
Extreme Acid Resistance
Loading Percentage 2.5%
60% YS and 60% CS
improvement over neat Al
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
62% CS improvement
over neat Ti
Compressive Strength 1400 MPa
Loading Percentage 4%
BNNT Coated PP Separators
Safe Operation over 200 C
Superior Thermal Conductivity
BNNT Coated PP Separators
Improved Ionic Conductivity
Improved Energy Density
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Thermal Conductivity Improved 33%
Thermal Conductivity ~0.195 W/mK at 365 K
Loading Percentage 0.005%
Tensile Strength and Thermal Conductivity
improved 40% and 124%
Tensile Strength 330 MPa
Thermal Conductivity 4.25 W/mK
Loading Percentage 2.5%
Unlocking Boron Nitride Nanotubes for mass market use
Processes
Purity
Scale
Repeatability
Quality
High Temperature
- Pressure Laser Method
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The EPIC Process
Chemical Vapor Deposition
Continuous Improvement, Powered by AI
We used AI to guide our experimentation using feedback from
AI annotated microscopy to make the world's best BNNT process even better.
Now we are using this process to further customize and optimize our nanomaterials.
Verifying BN Content
by Photography
Verifying BNNT Content
by Spectroscopy
Verifying Length and Diameter by Microscopy
Unmatched Quality,
Unmatched Quality Assurance
We have automated the analysis of BNNT quality to give the highest granularity information on every lot
Extensive Testing with Every Lot
Customize Structure -> Properties -> Performance
Performance scales with loading %
Boron Nitride Nanotubes chemically inert and thermal stability allow
them to be reusable in a number of material systems.
Supply chain yada yada yada
We use AI and our nanomaterial expertise to ensure
maximum performance improvements and seamless integration
Nanoarmor's Next Generation Ceramics
>3000 C
<5% Post Sintering
Excellent
1400-1500 C
Excellent
Excellent
Higher than traditional
carbide ceramics
Versatile Delivery for multiple applications
subtitle here
Safer Nuclear Plants
Shielding Vehicles from Extreme Temperatures
Improve heat resistance and life of high
temperature furnaces
Use in rocket nozzles,
heat exchangers, and combustion chambers
Ground Breaking Performance
using Ultra-High Temperature Ceramics
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Tensile Strength 4.2x
Young's Modulus 12.1x
Loading Percentage 1%
Metal Superalloys
Maximum operating
temperature of ~1500˚C
Not resistant to oxidation
Difficult to homogenously
disperse strengtheners and additives
Material properties are heavily dependent on processing parameters
Thermal expansion mismatch between bulk parts and protection coatings
Carbon Composites
Strict processing requirements that typically require many labor-intensive steps
Not resistant to oxidation
Carbon is an ablative material, meaning it consumes itself as it dissipates energy
Continuous fibers don’t allow for additive manufacturing technologies
Susceptible to material erosion
Traditional Ceramics
Highly brittle, low toughness
Low thermal shock resistance
Difficult to form net-shape parts of intricate geometries
Lower Processing Temperatures, closer net shape, higher performance
Near net-shape pre-sintering, low shrinkage (<5%) post-sintering
Fibers, fillers, and nanostructures are easily dispersed for additional customization and reinforcement
Near theoretical densities (>95%) in single-step process (no reinfiltration or densification required)
Sintering can be performed at ambient pressure and low temperature (<1450˚C)
Can produce graded structures to aid in adhesion to vehicle underbody as a thermal protection coating
Now selling the secret sauce
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Use machine learning analyze and optimize your nanomaterials faster, cheaper and more reliably
Register Now for FREE
Make better use of your experts. Lower the time and cost required to optimize processes.
Analyze complex variable spaces
Understand and optimize the tradeoff between objectives
Understand process stability and repeatability
Optimize Processes and Visualize Analysis
Determine variables, metrics for success and input existing data.
Your machine learning model suggests new trials, first to sample the input space, then to explore and exploit
Upload completed trials to train your machine learning model
Determine variable relationships,
tradeoffs in outputs, process progression and more
Continue trials until you have met your goal or model uncertainty is low and get inspired for the next experiment
Free self-service use in beta. Paid consulting services available.
Upload data and models. Compare and analyze data, all for free in beta.
Register for FReeShare your data for a chance to have our experts build public models.
Start uploading dataEpic is using automated process optimize to make the most its small but growing team to bring nanomaterials to the mainsteam
“ Quote from Rodney.”
Evaluate nanomaterials in seconds, not hours.
Build reproducible, quantifiable quality assessment workflows
Solving Nanomaterial Quality
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Use machine learning to analyze and optimize nanomaterials faster, cheaper and more reliably
Register Now for FREEMake better use of your experts. Lower the time and cost required to analyze nanomaterials.
Use public models to get started quickly. Many models already exist to aid in your analysis.
Codify your experts's knowledge into ML models. Develop your own or let our experts help.
Share and analyze results with stakeholders and colleagues.
Analyze and share data, results and models
Organize similar data points in dataset for easy analysis, comparison and sharing.
Upload data points to a dataset and choose models to evaluate them.
Compare different data points and evaluate entire dataset quality and variance
Unlimited free self-service use in beta. Paid model consulting services. Share data with the community.
Upload data and models. Compare and analyze data, all for free in beta.
Register for FReeShare your data for a chance to have our experts build public models.
Start uploading dataEpic is using automated nanomaterial analysis to quantify quality more efficiently and improve processes faster to new levels of control and performance
“To get the level of detail of analysis our models can achieve takes seconds compared with hours. This reduces our cycle times and allows us to make the world's best BNNT process even better.”
Evaluate nanomaterials in seconds, not hours.
Build reproducible, quantifiable quality assessment workflows
