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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

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Dynamic sampling for SAXSTT: towards real-time measurement adaptation.

Sici Wang1,2, Leonard C Nielsen3, Marianne Liebi1,2

  • 1Center for Photon Science, Paul Scherrer Institut (PSI), Villigen, Switzerland.

Journal of Synchrotron Radiation
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

We developed a dynamic sampling strategy for small-angle X-ray scattering tensor tomography (SAXSTT) to significantly reduce scan times. This adaptive method cuts data acquisition needs by up to sevenfold while preserving nanoscale structure reconstruction quality.

Keywords:
progressive samplingsmall-angle X-ray scatteringspherical samplingtensor tomography

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Area of Science:

  • Materials Science
  • Nanotechnology
  • X-ray Physics

Background:

  • Small-angle X-ray scattering tensor tomography (SAXSTT) enables non-destructive 3D nanoscale characterization.
  • Current SAXSTT methods require extensive scanning, resulting in lengthy acquisition times.
  • Long acquisition times are a significant bottleneck for synchrotron-based experiments.

Purpose of the Study:

  • To develop a time-efficient acquisition strategy for SAXSTT.
  • To reduce the number of projections needed for accurate 3D nanoscale reconstruction.
  • To introduce an adaptive measurement approach compatible with existing systems.

Main Methods:

  • Proposed a dynamic sampling strategy with an online error feedback mechanism.
  • Implemented adaptive termination of measurements based on real-time reconstruction quality.
  • Applied the strategy to experimental SAXSTT datasets.

Main Results:

  • Achieved up to a sevenfold reduction in the number of required projections.
  • Maintained high reconstruction quality despite reduced data acquisition.
  • Demonstrated sample-dependent reduction potential and confirmed strategy robustness via quantitative evaluations.

Conclusions:

  • The proposed dynamic sampling strategy significantly enhances SAXSTT efficiency.
  • This adaptive approach offers a practical solution for time-limited synchrotron experiments.
  • The method is compatible with current beamline infrastructure, facilitating broader adoption.