kb/data/en.wikipedia.org/wiki/Differential_dynamic_microscopy-0.md

title	chunk	source	category	tags	date_saved	instance
Differential dynamic microscopy	1/3	https://en.wikipedia.org/wiki/Differential_dynamic_microscopy	reference	science, encyclopedia	2026-05-05T10:04:12.670972+00:00	kb-cron

Differential dynamic microscopy (DDM) is an imaging-based optical technique that enables performing light scattering-like experiments by means of a conventional optical microscope. It uses time sequences of microscope images to extract dynamic information about microscopic fluctuations over a range of length scales and time delays, analogous to what is obtained in dynamic light scattering (DLS) experiments. DDM is suitable for typical soft materials such as liquids, gels, suspensions of colloids and polymers, liquid crystals and it has been applied to various biological systems including bacteria and cells.

== History and developments == The DDM technique was introduced in 2008 by Cerbino & Trappe as a way to probe wavevector-resolved dynamics of colloidal particles using a microscope equipped with a high-speed camera, demonstrating that dynamic information similar to that obtained in DLS can be extracted from time-lapse images taken in real space. This foundational paper has accumulated several citations, establishing the unique capability of DDM to combine imaging capabilities with scattering-based wavevector analysis. Shortly thereafter, Giavazzi et al. discussed extensions to other imaging modalities, also based on fluorescence, and clarified the connections of DDM to related imaging-based scattering methods (see below the section Relationship with other imaging-based scattering methods). Subsequent studies applied DDM to characterize bacterial motility and dynamics of microorganisms, showing how population-averaged swim speed and other motility parameters can be obtained without single-particle tracking. DDM was also extended to confocal fluorescence microscopy to characterize concentrated, multiply scattering and actively driven fluorescent systems, broadening the range of accessible samples and imaging modalities. In 2014, Giavazzi & Cerbino provided a broader perspective on digital Fourier microscopy for soft-matter dynamics, placing DDM within a wider family of Fourier-space approaches that extract scattering and dynamical information from microscopy image sequences. As the technique matured, comprehensive reviews consolidated the field. In 2017, Cerbino & Cicuta published a perspective article highlighting DDM's ability to extract multi-scale activity in complex fluids and biological systems, establishing it as a bias-free tool for quantifying dynamics across different length scales. Later, in 2022, Cerbino, Giavazzi & Helgeson reviewed the application of DDM specifically for polymer systems, detailing its use in characterizing polymer solutions, gels, and composites. Pedagogical introductions and reproducible workflows have been developed to support adoption of DDM across disciplines. A recent Perspective article reviewed DDM as an emerging approach for measuring diffusion coefficients of macromolecules and particles using standard microscopy videos, discussing practical constraints and opportunities for spatially resolved diffusion measurements. A tutorial-style presentation of DDM, including two example datasets and analysis scripts, is provided by Germain et al. in the American Journal of Physics. In 2025, a comprehensive tutorial article titled The Hitchhiker's guide to differential dynamic microscopy was published, providing step-by-step guidance on experiment design, data acquisition and analysis, and emphasizing reproducible workflows supported by open software and example datasets.

== DDM in a nutshell == DDM starts from a microscope movie, i.e. a time sequence of images

    I
    (
    
      r
    
    ,
    t
    )
  

{\displaystyle I(\mathbf {r} ,t)}

acquired at a fixed plane in the sample. The key assumption is that the recorded intensity fluctuations are related to fluctuations of a physical field of interest (often the number density of particles, or fluorescence-labeled structures), possibly blurred by the microscope point spread function (PSF). To separate spatial scales, each image is analysed in Fourier space: fluctuations are decomposed into modes labelled by a wavevector

      q
    
  

{\displaystyle \mathbf {q} }

, corresponding to a characteristic length scale

    ∼
    2
    π
    
      /
    
    q
  

{\displaystyle \sim 2\pi /q}

. Dynamics is then quantified by comparing images separated by a time delay

    Δ
    t
  

{\displaystyle \Delta t}

. In the most common implementation, one computes the Fourier power spectrum of the difference between two images separated by

    Δ
    t
  

{\displaystyle \Delta t}

, and averages this quantity over many pairs to improve statistics. This yields the image structure function

    D
    (
    q
    ;
    Δ
    t
    )
  

{\displaystyle D(q;\Delta t)}

, which increases from a noise/background level at short delays to a plateau at long delays as the fluctuations decorrelate. Under standard conditions,

    D
    (
    q
    ;
    Δ
    t
    )
  

{\displaystyle D(q;\Delta t)}

can be related to the normalized intermediate scattering function

    f
    (
    q
    ;
    Δ
    t
    )
  

{\displaystyle f(q;\Delta t)}

, i.e. the same dynamical correlation function that is central to DLS. Consequently, by fitting the

    Δ
    t
  

{\displaystyle \Delta t}

-dependence at each

    q
  

{\displaystyle q}

with appropriate models (diffusion, advection/ballistic motion, mixtures of processes), DDM yields quantitative parameters such as diffusion coefficients, characteristic speeds, or relaxation times.

== Advantages and limitations of DDM == DDM complements traditional dynamic light scattering (DLS) and multiple particle tracking (MPT) by offering distinct trade-offs suited to specific experimental scenarios.

ADVANTAGES OVER DLS

Wavevector range: DDM accesses lower q-values than conventional DLS (typically 0.1–5 μm−1), offering advantages when characterizing larger particles. Spatial selection: DDM enables region-of-interest (ROI) selection, allowing measurement of spatially heterogeneous samples, and localized dynamics that are challenging or impossible in ensemble DLS measurements. Optical flexibility: DDM is compatible with various microscope contrast mechanisms (bright-field, phase-contrast, fluorescence, confocal, polarized light, dark-field), whereas DLS typically requires laser illumination. Dust robustness: DDM is tolerant of stationary dust particles (e.g. on the cell surfaces) and handles multiply-scattering samples well; conventional DLS is highly sensitive to dust and requires stray-light suppression. Sample volume: DDM requires only microliters of sample; DLS typically requires milliliters.

ADVANTAGES OVER MULTIPLE PARTICLE TRACKING

Statistical power: DDM provides superior ensemble statistics by analyzing all the particles in the field of view simultaneously; MPT requires user-intensive trajectory reconstruction. Sub-resolution sensitivity: DDM does not require individual particles to be optically resolved, enabling measurements in concentrated and optically dense systems where MPT struggles.

LIMITATIONS AND PRACTICAL CONSTRAINTS