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

Molecular Models02:00

Molecular Models

38.8K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.8K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Energy Diagrams, Transition States, and Intermediates02:13

Energy Diagrams, Transition States, and Intermediates

16.8K
Free-energy diagrams, or reaction coordinate diagrams, are graphs showing the energy changes that occur during a chemical reaction. The reaction coordinate represented on the horizontal axis shows how far the reaction has progressed structurally. Positions along the x-axis close to the reactants have structures resembling the reactants, while positions close to the products resemble the products.  Peaks on the energy diagram represent stable structures with measurable lifetimes, while...
16.8K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

922
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
922
¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

893
At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
893
Inductive Effects on Chemical Shift: Overview01:27

Inductive Effects on Chemical Shift: Overview

1.2K
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Determination of stable carbon isotope ratios for molecules in natural organic matter using ESI FT-ICR MS.

Science advances·2026
Same author

Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories.

Environmental science & technology·2026
Same author

Functional Group Distribution Shapes Chemical Properties of Degraded Terrestrial and Marine Dissolved Organic Matter.

Environmental science & technology·2025
Same author

Optimizing Machine Learning-Based Prediction of Terrestrial Dissolved Organic Matter in the Ocean Using Fluorescence and LC-FTMS Data.

ACS omega·2025
Same author

Constraining biorecalcitrance of carboxyl-rich alicyclic molecules in the ocean.

Science advances·2025
Same author

Temporal Dynamics and Intermediate Product Formation in DOM Phototransformation Revealed by Liquid Chromatography Ultrahigh-Resolution Mass Spectrometry.

Environmental science & technology·2025

Related Experiment Video

Updated: Jul 31, 2025

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

10.2K

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter.

Philipp Plamper1, Oliver J Lechtenfeld2,3, Peter Herzsprung4

  • 1Anhalt University of Applied Sciences, Department Computer Science and Languages, Lohmannstraße 23, Köthen 06366, Germany.

Environmental Science & Technology
|May 9, 2023
PubMed
Summary

This study introduces a temporal graph model to track molecular changes in dissolved organic matter (DOM) during photo-oxidation. The AI-driven approach reveals DOM transformations and reactivity patterns with unprecedented detail.

Keywords:
DOMcommunity detectioncomplex mixturescompositional networklink predictionmachine learningmolecular networkphoto-oxidationphotodegradationtemporal graphunsupervised clustering

More Related Videos

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.7K
Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems
09:38

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Published on: October 29, 2016

10.4K

Related Experiment Videos

Last Updated: Jul 31, 2025

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

10.2K
Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.7K
Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems
09:38

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Published on: October 29, 2016

10.4K

Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Computational Chemistry

Background:

  • Dissolved organic matter (DOM) is a complex environmental mixture undergoing constant molecular transformations, notably via photochemical reactions.
  • Ultrahigh resolution mass spectrometry (UHRMS) provides molecular-level data, but tracking DOM changes typically relies on mass peak intensity trends.
  • Graph data structures offer a powerful framework for modeling complex relationships and temporal processes, enhancing AI applications.

Purpose of the Study:

  • To develop and apply a novel temporal graph model for identifying DOM molecule transformations during photo-oxidation.
  • To leverage AI and link prediction for a deeper mechanistic understanding of DOM reactivity.
  • To overcome limitations in current data evaluation methods for studying DOM photochemistry.

Main Methods:

  • Utilized a temporal graph model with link prediction to analyze DOM transformations in a photo-oxidation experiment.
  • Developed a link prediction algorithm that considers both reactant depletion and product formation based on predefined transformation units (e.g., oxidation, decarboxylation).
  • Weighted transformations by intensity changes and clustered them on the graph to identify groups with similar reactivity.

Main Results:

  • The temporal graph model successfully identified molecular transformations within DOM during the photo-oxidation experiment.
  • The approach enabled the simultaneous consideration of reactant removal and product formation, providing a comprehensive view of chemical changes.
  • Clustering on the graph structure revealed groups of DOM molecules exhibiting similar reactivity patterns.

Conclusions:

  • The developed temporal graph approach effectively models DOM transformations and reactivity, overcoming previous data evaluation limitations.
  • This method leverages AI and graph theory to provide mechanistic insights into DOM photochemistry.
  • The study demonstrates the potential of temporal graphs for studying DOM reactivity using UHRMS data.