Non-Destructive Nanoscale Metrology for Advanced Materials
Screen wafers, films, crystals and manufactured surfaces for defects, topography, roughness and structural variation—with non-destructive optical capture and Holomoire analysis.
Label-free optical measurement
Surface geometry, roughness and deformation
Registered images, maps and analysis
Designed to complement SEM, TEM, AFM and Raman
Take a closer look at diamond.
Explore a diamond scan from NanoFraction’s image library. Open the interactive viewer to pan, zoom and move through the available detail views.
Select “Explore diamond scan” to load the interactive image.
Digital zoom enlarges the displayed image; it does not establish spatial resolution or validate a defect classification. See the case study for measurement context.
Our Services
Discuss sample suitability and a focused measurement question before arranging shipment.
diamond 200 µm
Semiconductors
Diamond, SiC and other semiconductor materials
DHOM optical capture and Holomoire analysis are scoped to the specimen, configuration and reference method. Candidate outputs include:
Surface geometry and bending
Calibrated roughness where supported
Defect-related optical signatures
Reference-declared displacement and strain
Stress requires material constants and a model. Published HRTEM analysis is not visible-light atomic imaging.
PETG 200 mm
Advanced Manufacturing
Polymers, composites and manufactured surfaces
We investigate geometry, surface variation and candidate defect signatures using a setup selected for the material and scale.
Shape and surface comparisons
Selected-region feature measurements
Reference-based deformation analysis
Repeatability and interpretation limits
Thermal-property and failure predictions require separate validation.
SARS-CoV-2 200 nm
Life Sciences
Exploratory biological and crystalline samples
We investigate optical signatures and computational analysis for suitable research specimens. Reconstructions and model-assisted visualizations must be distinguished from acquired optical data.
Research feasibility assessment
Source and derived-image comparison
Sample-specific calibration and controls
These examples do not establish validated diagnostics, universal live-cell nanoscale imaging or direct ion identification.
Experimental optics, computation and collaboration
NanoFraction combines experimental optics with a web-based workflow for sharing source images, derived results and measurement context. Automation and AI-assisted analysis are evaluated for each application; throughput, repeatability and uncertainty require setup-specific evidence.
Nano Salt Crystal
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This exploratory salt-crystal dataset illustrates source imaging and computational analysis. Image sampling and displayed detail do not establish angstrom-scale optical resolution; calibration, repeatability and independent reference measurements are required.
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Post-capture, model-assisted NaCl visualizations are interpreted outputs, not direct optical identification of individual ions. Atomic assignments and composition require independent validation; no ion-identification capability is established by these images alone.
Human Sweat
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Exploratory work examines optical signatures and particle or crystal formation in prepared sweat samples. Sample handling, drying and substrate effects must be controlled. These observations are not validated clinical diagnostics or proof of nanoscale measurements in untreated sweat.
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Observed structures require appropriate controls and independent identification. Image appearance alone does not establish a biological mechanism, molecular identity or diagnostic value. Suitability and validation are assessed for each research question.
Thermal Metrology
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Light-material interactions can produce signatures worth investigating. Inferring acoustic or thermal properties requires a defined excitation/detection setup, a material model and independent reference measurements; these images alone do not establish quantitative thermal metrology.
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Computational models help test interpretations against recorded data. Agreement depends on the assumptions, calibration and reference experiment; a model-assisted output does not by itself establish a general bridge from quantum behavior to classical mechanics.
Contact Us
Tell us what you want to measure or compare. We will review whether a focused study fits your sample and question.
We work on a services-first, hardware-second model.
Agree on a research question, suitable inputs, reference method and analysis workflow.
Consider hardware access when the application and operating requirements are established.