Photon Distribution Analysis
Photon Distribution Analysis - Related Videos
08:44
Fabrication and Testing of Photonic Thermometers
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Cited by 1 •
2018
We describe the process of fabrication and testing of photonic thermometers.
11:08
Fabrication And Characterization Of Photonic Crystal Slow Light Waveguides And Cavities
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Cited by 14 •
2012
Use of photonic crystal slow light waveguides and cavities has been widely adopted by the photonics community in many differing applications. Therefore fabrication and characterization of these devices are of great interest. This paper outlines our fabrication technique and two optical characterization methods, namely: interferometric (waveguides) and resonant scattering (cavities).
04:32
Two-Photon In Vivo Imaging of the Mouse Retina
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2025
Source: Wang, Z., et al. Transpupillary Two-Photon In Vivo Imaging of the Mouse Retina. J. Vis. Exp. (2021).This video demonstrates a technique for in vivo retinal imaging using two-photon microscopy. Anesthetized mice are prepared for imaging by securing their heads, Applying eye lubricant, and placing a coverslip over the eye. Retinal ganglion cells (RGCs) expressing fluorescent proteins are first visualized using epifluorescence for alignment. Finally, two-photon imaging is employed,...
13:02
Fabrication of 1-D Photonic Crystal Cavity on a Nanofiber Using Femtosecond Laser-induced Ablation
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Cited by 3 •
2017
We present a protocol for fabricating 1-D photonic crystal cavities on subwavelength diameter silica fibers (optical nanofibers) using femtosecond laser-induced ablation.
08:46
Analysis and Specification of Starch Granule Size Distributions
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Cited by 2 •
2021
Presented here is a procedure for reproducible and statistically valid determinations of starch granule size distributions, and for specifying the determined granule lognormal size distributions using a two-parameter multiplicative form. It is applicable to all granule sizing analyses of gram-scale starch samples for plant and food science research.
FAQs
The analytical dimension determines which biological feature becomes measurable. Spatial distributions can show where optical signals occur across pixels, temporal distributions can reveal how signals change during an experiment, and intensity distributions can characterize the strength of detected events. Selecting the relevant dimension helps relate photon-count patterns to molecular localization, concentration, or reaction behavior.
Background variation can resemble or obscure a molecular signal, making raw photon counts difficult to interpret. Comparing the distribution of detected photons helps researchers separate consistent optical patterns from incidental fluctuations. This distinction is important for deciding whether an observed feature reflects a biochemical event rather than ordinary variation in the measured sample or imaging field.
Researchers compare the statistical patterns of detected photon counts across pixels, time intervals, intensity levels, or experimental conditions. Differences between distributions can indicate changes in signal strength, location, or timing. This approach supports quantitative interpretation without relying only on a single measurement, allowing optical responses from biochemical samples to be evaluated systematically.
A typical workflow begins by recording photon counts from a biochemical sample with a detector. The measurements are then organized according to space, time, or intensity and examined statistically. Researchers compare the resulting distributions across samples or conditions, using the observed patterns to evaluate signal variation and connect optical measurements with molecular behavior.
Photon-count patterns provide quantitative information that can be related to how much signal is present and where it appears. In fluorescence imaging, spatial distributions can help assess molecular localization, while differences in signal levels can support concentration measurements. The analysis therefore adds statistical structure to optical data rather than treating an image as only a visual result.
When photon counts are examined across successive time intervals, their distributions can show how an optical signal changes during a biochemical process. Such temporal patterns help researchers investigate reaction dynamics and distinguish changing molecular activity from background fluctuation. The same framework can be applied to signals produced through fluorescence or chemiluminescence, depending on the experiment.