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Related Concept Videos

Propagation of Waves01:07

Propagation of Waves

When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
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Updated: Jun 16, 2026

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
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Published on: March 13, 2013

Interpolation of refractive index data.

B W Morrissey, C J Powell

    Applied Optics
    |February 4, 2010
    PubMed
    Summary
    This summary is machine-generated.

    The cubic spline method offers superior interpolation for refractive index data, outperforming Sellmeier and polynomial equations. This technique provides the most accurate fits with minimal errors for materials like sapphire and polystyrene.

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    Area of Science:

    • Materials Science
    • Optics
    • Computational Chemistry

    Background:

    • Accurate refractive index (RI) data is crucial for optical material characterization.
    • Various models exist for interpolating RI data, each with limitations.

    Purpose of the Study:

    • To compare the effectiveness of different interpolation techniques for refractive index data.
    • To identify the most accurate and reliable method for simple interpolation tasks.

    Main Methods:

    • Evaluated interpolation methods: Sellmeier equation (3-term), Lorentz-Lorenz equation (6-term), polynomial fits (3rd & 5th order), and cubic-spline.
    • Applied methods to refractive index data of Czochralski sapphire, cyclohexane, and polystyrene in cyclohexane.
    • Quantified accuracy using root-mean-square (RMS) and average absolute residuals.

    Main Results:

    • Cubic-spline interpolation demonstrated the smallest RMS and average absolute residuals.
    • The cubic-spline method exhibited no systematic errors in the fits.
    • Other methods showed larger residuals and potential systematic deviations.

    Conclusions:

    • Cubic-spline interpolation is highly valuable for accurate and simple interpolation of refractive index data.
    • This method provides superior performance compared to traditional Sellmeier and polynomial approaches.
    • The findings support the use of cubic splines for reliable optical data analysis.