Free Spectral Range
Free Spectral Range - Related Videos
00:59
Range
0 Views •
2023
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda: 15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5 Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
07:11
ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Cited by 1 •
2021
This protocol introduces Franck-Condon Lineshape Analyses (FCLSA) of emission spectra and serves as a tutorial for the use of ARL Spectral Fitting software. The open-source software provides an easy and intuitive way to perform advanced analysis of emission spectra including excited state energy calculations, CIE color coordinate determination, and FCLSA.
04:16
Using Spectral Reflectometry to Determine Myelinated Axon Diameters in a Fixed Mouse Brain Slice
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2025
Source: Kwon, J., et al., Spectral Reflectometric Microscopy on Myelinated Axons In Situ. J. Vis. Exp. (2018) This video demonstrates the use of spectral reflectometry with a hyperspectral confocal microscope to analyze interference patterns and determine myelinated axon diameters in fixed brain tissue slices.
01:27
¹H NMR: Long-Range Coupling
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2024
The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling. In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene π orbitals.
11:37
RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
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Cited by 31 •
2017
An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.
FAQs
The approximate relation c/(2nL) shows that FSR decreases as either refractive index or cavity length increases. A longer optical path produces more closely spaced resonances, while a shorter path produces wider spacing. These dependencies allow bioengineering researchers to adjust resonator geometry or account for material properties when designing measurements and interpreting spectral patterns.
Each round trip adds optical phase to the circulating light. Constructive interference occurs when the accumulated optical path supports an integer number of wavelengths, producing a resonant peak. The next resonance appears when this condition advances by one integer step. Consequently, the cavity’s optical path determines how frequently its transmission pattern repeats across frequency or wavelength.
FSR indicates the interval over which a resonator’s transmission spectrum repeats, so it sets the spacing available between neighboring resonant peaks. If relevant spectral features approach that spacing, repeated peaks can complicate interpretation. Considering FSR helps researchers judge whether a measurement range can distinguish the features of interest rather than confusing them with adjacent cavity resonances.
Changing cavity length or the refractive index changes the optical path and therefore shifts the resonance spacing predicted by c/(2nL). Increasing the path narrows the separation, whereas decreasing it widens the separation. This relationship provides a design basis for selecting a resonator configuration that better matches the spectral range and feature spacing required for a measurement.
Researchers can first estimate FSR from the cavity length and refractive index, then compare that spacing with the intended measurement range. The estimate helps identify how many repeated resonance intervals may appear and whether adjacent peaks could interfere with interpretation. In this way, FSR supports choices about spectral coverage and the ability to distinguish measured features.
In fiber-optic sensors and interferometers, FSR provides a reference for understanding the spacing of resonant or interference-related spectral features. Researchers use it to design optical paths and interpret repeated peaks while monitoring changes in the measured spectrum. Its value is especially important when sensor outputs must be separated from neighboring resonances within a selected measurement range.
Resonant biosensors can respond to changes in refractive index or to biomolecular binding at the sensing region. Because refractive index contributes to the optical path, such changes can alter the resonance pattern and its spacing. Comparing observed spectral behavior with the expected FSR helps researchers interpret these responses and distinguish sensor-related changes from the cavity’s repeating structure.