Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

2.4K
Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
2.4K
Sampling Plans01:23

Sampling Plans

233
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...
233
Atomic Absorption Spectroscopy: Lab01:21

Atomic Absorption Spectroscopy: Lab

517
For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
 Solutions containing organic solvents, such as low-molecular-mass alcohols, esters, or ketones, enhance absorbances by increasing...
517
Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle01:19

Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle

776
Inductively coupled plasma (ICP) is the most widely used plasma source in atomic emission spectroscopy (AES), also known as Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The ICP source, or torch, consists of three concentric quartz tubes with argon gas flowing through them. A spark from a Tesla coil initiates the ionization of argon, generating a high-temperature plasma.
The ions and electrons produced interact with the fluctuating magnetic field created by a water-cooled...
776
Atomic Absorption Spectroscopy: Atomization Methods01:25

Atomic Absorption Spectroscopy: Atomization Methods

599
Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...
599
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

221
AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
221

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MINT32: A Minimum-Image INT32 Coordinate Representation for Fast and Accurate Molecular Dynamics on GPUs.

Journal of chemical information and modeling·2026
Same author

Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.

Journal of chemical information and modeling·2026
Same author

A Relative Binding Free Energy Framework for Structurally Dissimilar Molecules.

Journal of chemical information and modeling·2026
Same author

SAMTI: Sampling Adaptive Thermodynamic Integration for Alchemical Free Energy Calculations.

The journal of physical chemistry. B·2025
Same author

Recent Developments in Amber Biomolecular Simulations.

Journal of chemical information and modeling·2025
Same author

Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.

The journal of physical chemistry. B·2025
Same journal

Complementing Onsager's Conductivity Theory by Grotthuss Mechanism Mitigation via Ion-Induced Depletion of Hydrogen-Bond-Donating Water.

Journal of chemical theory and computation·2026
Same journal

Microscopic Stress in Biomembranes: A Perspective on Key Concepts, Methods, and Applications.

Journal of chemical theory and computation·2026
Same journal

Analytic Nuclear Gradients Including Oriented External Electric Fields in a Molecule-Fixed Frame.

Journal of chemical theory and computation·2026
Same journal

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.

Journal of chemical theory and computation·2026
Same journal

Generalizable Protein Folding Pathway Exploration with DA2-GRASP: Extending Beyond Miniproteins.

Journal of chemical theory and computation·2026
Same journal

Improving PCM in Protic Media: Markov State Models for TD-DFT Calculations.

Journal of chemical theory and computation·2026
See all related articles

Related Experiment Video

Updated: Aug 14, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.3K

ACES: Optimized Alchemically Enhanced Sampling.

Tai-Sung Lee1, Hsu-Chun Tsai1, Abir Ganguly1

  • 1Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey08854, United States.

Journal of Chemical Theory and Computation
|January 11, 2023
PubMed
Summary
This summary is machine-generated.

We developed an alchemical enhanced sampling (ACES) method for molecular dynamics (MD) simulations. ACES overcomes limitations of traditional MD and other methods, accurately predicting molecular properties and conformational changes.

More Related Videos

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

6.3K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.3K

Related Experiment Videos

Last Updated: Aug 14, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.3K
Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

6.3K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.3K

Area of Science:

  • Computational chemistry
  • Molecular dynamics simulations
  • Free energy calculations

Background:

  • Enhanced sampling methods are crucial for overcoming kinetic barriers in molecular simulations.
  • Traditional methods and some existing enhanced sampling techniques struggle with complex systems.
  • Accurate free energy calculations are essential for understanding molecular interactions and drug design.

Purpose of the Study:

  • To introduce and validate a novel alchemical enhanced sampling (ACES) method.
  • To demonstrate ACES's superiority over traditional MD and REST2-like methods.
  • To improve the accuracy and efficiency of free energy calculations and conformational sampling.

Main Methods:

  • Implementation of ACES within the GPU-accelerated AMBER MD engine.
  • Utilizing an "enhanced sampling state" by modifying potential energy terms.
  • Employing Hamiltonian replica exchange molecular dynamics (HREMD) with alchemical pathways and softcore potentials.
  • Leveraging the dual topology framework in AMBER for counterbalancing HREMD networks.

Main Results:

  • ACES successfully reproduced hydration free energies for acetic acid, independent of starting conformation.
  • Accurate predictions for edge-case molecules from the FreeSolv database, showing closer agreement with experimental data.
  • Reliable reproduction of side chain distributions (V111χ1) in T4-lysozyme under various conditions.
  • Demonstrated robustness and superiority over REST2-like methods in complex test cases.

Conclusions:

  • ACES is a robust and effective enhanced sampling method for molecular dynamics.
  • The method significantly improves the accuracy of free energy calculations and conformational sampling.
  • ACES offers a superior alternative to existing methods for challenging molecular simulation problems.