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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used.
Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview01:19

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview

In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then passed on to...

You might also read

Related Articles

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

Sort by
Same author

Mechanisms of Ibuprofen Retention and Release in Dual-Responsive P(NIPAM-co-AAc) Nanogels: Coupling of Mesh Sieving and Affinity Switching.

Gels (Basel, Switzerland)·2026
Same author

Revealing shared molecular markers and mechanisms in colorectal cancer and COVID-19 through bioinformatics and machine learning.

Briefings in bioinformatics·2026
Same author

Chiral Polyproline Enables Functional Stealth for Simultaneous Long Circulation and Endoplasmic Reticulum-Targeted Antigen Presentation.

ACS nano·2026
Same author

Multimodal Mass Spectrometry Imaging with a Metal-Based Alignment Tool Enables Targeted Single-Cell Boronophenylalanine Uptake and Metabolic Profiling in Glioblastoma Tumors.

Analytical chemistry·2025
Same author

XASDB: a new database of experimental interactive X-ray absorption spectra.

Journal of synchrotron radiation·2025
Same author

A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML.

Journal of synchrotron radiation·2025
Same journal

Kinetic and Mechanistic Insights into H-Abstraction and Subsequent Isomerization and Decomposition of Monoglyme and Key Combustion Intermediates.

The journal of physical chemistry. A·2026
Same journal

First-Principles Analysis of Protonation-Induced Electronic Effects in Tetrakis(<i>p</i>-aminophenyl)porphyrin (TAPP).

The journal of physical chemistry. A·2026
Same journal

Exploring the Reactivity of the CH Radical toward Nitrous Oxide in the Context of the Interstellar Medium.

The journal of physical chemistry. A·2026
Same journal

Infrared Photodissociation Spectroscopy of Benzene-V<sup>+</sup>(CO)<sub>n</sub> "Piano Stool" Cations.

The journal of physical chemistry. A·2026
Same journal

Correction to "Solvent-Dependent Ultrafast Photochemical Dynamics of <i>N</i>-Methyl Oxindole Overcrowded Alkene Molecular Motors".

The journal of physical chemistry. A·2026
Same journal

Accelerating the Discovery of Superhalogens via Physics-Informed Graph Neural Networks.

The journal of physical chemistry. A·2026
See all related articles

Related Experiment Video

Updated: May 31, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced

Yihe Pang1,2, Lin Li1,3, Xiangwen Deng1,4

  • 1Multi-disciplinary Research Division, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China.

The Journal of Physical Chemistry. A
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

Advanced photon sources generate vast data, overwhelming traditional analysis. IPSBrain, an AI platform, automates multimodal synchrotron data interpretation, accelerating scientific discovery.

More Related Videos

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy
07:56

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

Published on: July 29, 2021

Related Experiment Videos

Last Updated: May 31, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy
07:56

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

Published on: July 29, 2021

Area of Science:

  • Materials Science
  • Data Science
  • Physics

Background:

  • Advanced photon sources produce large, complex datasets from multimodal experiments.
  • Traditional data analysis methods struggle to keep pace, creating a bottleneck for scientific discovery.
  • Artificial intelligence (AI) offers potential for automated analysis of experimental data.

Purpose of the Study:

  • To present IPSBrain, a unified AI-powered platform for automated analysis of multimodal synchrotron experimental data.
  • To address the growing gap between data acquisition and interpretation at large-scale research facilities.
  • To lower the technical barrier for scientists in interpreting complex experimental data.

Main Methods:

  • Development of a unified AI platform (IPSBrain) integrating novel machine-learning models.
  • Implementation of automated data analysis workflows for techniques including diffraction, scattering, X-ray absorption, and tomography.
  • Creation of an intuitive web interface for accessible data interpretation.

Main Results:

  • IPSBrain enables automated, end-to-end analysis of multimodal synchrotron data.
  • The platform supports high-throughput, reproducible, and scalable analysis.
  • Demonstrated practical and extensible framework for AI-driven synchrotron data analysis.

Conclusions:

  • IPSBrain provides a transformative solution for analyzing large-scale experimental data.
  • The platform accelerates scientific discovery by bridging the gap between data acquisition and insight.
  • IPSBrain establishes a transferable paradigm for intelligent data analysis in other large-scale research infrastructures.