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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

2.1K
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...
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Random Sampling Method01:09

Random Sampling Method

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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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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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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...
2.0K
Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sampling Theorem01:15

Sampling Theorem

1.2K
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.
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Sampling Plans01:23

Sampling Plans

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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...
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Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
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Enhanced Surface Hopping Method for Sampling Rare Nonadiabatic Events.

Yangqiu Liu1, Wei-Hai Fang1,2, Ganglong Cui1,2

  • 1Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P.R. China.

Journal of Chemical Theory and Computation
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

We developed a novel nonadiabatic event sampling method combining fewest switches surface hopping (FSSH) with enhanced sampling. This approach efficiently captures rare transition events and decay channels, accelerating simulations and reducing computational costs.

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Area of Science:

  • Computational Chemistry
  • Chemical Dynamics
  • Theoretical Chemistry

Background:

  • Nonadiabatic events are crucial in chemical reactions but challenging to simulate.
  • Standard methods like fewest switches surface hopping (FSSH) struggle with rare events and high-energy barriers.

Purpose of the Study:

  • To develop an enhanced nonadiabatic event sampling method.
  • To improve the efficiency and accuracy of simulating rare nonadiabatic transitions and decay channels.

Main Methods:

  • Integration of enhanced sampling techniques with the fewest switches surface hopping (FSSH) method.
  • Introduction of a biasing function to guide trajectories across energy barriers.
  • Development of two biasing functions based on bond length differences and root-mean-square deviation.

Main Results:

  • The new method effectively samples rare nonadiabatic transition events.
  • It successfully captures various decay channels between reactants and products.
  • Biasing functions accelerate simulations and reduce computational expense.

Conclusions:

  • The combined FSSH and enhanced sampling method provides a powerful tool for studying complex chemical dynamics.
  • This approach overcomes limitations of standard FSSH for simulating rare events.
  • It offers a computationally efficient way to explore diverse reaction pathways.