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

Cholinesterases: Distribution and Function01:22

Cholinesterases: Distribution and Function

982
Cholinesterases are a group of serine hydrolase enzymes that play a crucial role in the breakdown of choline esters. The two primary types of cholinesterases are acetylcholinesterases (AChEs) and butyrylcholinesterase (BuChEs), which differ in their distribution, function, and substrate specificity. AChEs, also known as true cholinesterases, specifically hydrolyze acetylcholine, while BuChEs, often referred to as pseudocholinesterases, can hydrolyze various choline esters, including...
982
Drug Distribution: Volume of Distribution01:25

Drug Distribution: Volume of Distribution

7.3K
The volume of distribution refers to the theoretical volume necessary to contain the entire amount of an administered drug at the same concentration observed in the blood plasma. The body's intracellular fluid compartment, which makes up two-thirds of the total body water, is contrasted with the extracellular fluid compartment—comprising plasma and interstitial fluid—that accounts for one-third. The volume of distribution can vary depending on the characteristics of the drug.
7.3K
Frequency-dependent Selection01:21

Frequency-dependent Selection

23.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
23.1K
F Distribution01:19

F Distribution

9.0K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
9.0K
Drug Dependence01:17

Drug Dependence

1.6K
Medications are typically administered to achieve therapeutic effects. Some drugs can modify an individual's mood and perception, frequently resulting in various enjoyable experiences. However, this can result in drug dependency, a condition marked by continuous drug use despite potential negative consequences. Drug dependency primarily falls into two categories: psychological and physical dependence. Psychological dependence occurs when the pleasurable feelings induced by the drug...
1.6K
Contact-dependent Signaling01:19

Contact-dependent Signaling

46.9K
Contact-dependent signaling, as the name suggests, requires that communicating cells be in direct contact with each other. This is achieved either through receptor-ligand interactions or by specialized cytoplasmic channels that allow the flow of small molecules between cells. In animal cells, channels called gap junctions facilitate contact-dependent signaling in certain tissues, whereas, plasmodesmata perform a similar function in plants.
Gap Junctions
In animal cells, gap junctions are formed...
46.9K

You might also read

Related Articles

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

Sort by
Same author

Defects and defect-mediated engineering of two-dimensional materials: challenges and open questions.

Beilstein journal of nanotechnology·2026
Same author

Diverse surface reconstructions in MAX phases.

Nanoscale·2025
Same author

Screening of Material Defects using Universal Machine-Learning Interatomic Potentials.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Valley-selective carrier transfer in SnS-based van der Waals heterostructures.

Nanoscale horizons·2024
Same author

Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities.

Journal of chemical information and modeling·2024
Same author

Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon.

Journal of the American Chemical Society·2024

Related Experiment Video

Updated: Jan 21, 2026

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording
09:58

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording

Published on: February 12, 2020

14.1K

pH-Dependent Distribution of Functional Groups on Titanium-Based MXenes.

Rina Ibragimova1, Martti J Puska1, Hannu-Pekka Komsa1

  • 1Department of Applied Physics , Aalto University , P.O. Box 11100, 00076 Aalto , Finland.

ACS Nano
|August 9, 2019
PubMed
Summary

MXene surfaces, like titanium carbides, exhibit mixed O, OH, and F terminations. This finding, based on multiscale modeling, suggests limited tunability of MXene properties, contrary to prior assumptions.

Keywords:
2D materialsMXenedensity functional theoryfunctional groupmultiscale simulation

More Related Videos

Solar-Driven Electrochemical Green Fuel Production from CO2 and Water Using Ti3C2Tx MXene-Supported CuZn and NiCo Catalysts
10:15

Solar-Driven Electrochemical Green Fuel Production from CO2 and Water Using Ti3C2Tx MXene-Supported CuZn and NiCo Catalysts

Published on: November 7, 2025

519
Using Flexible Gold-Titanium Reaction Cells to Simulate Pressure-Dependent Microbial Activity in the Context of Subsurface Biomining
13:11

Using Flexible Gold-Titanium Reaction Cells to Simulate Pressure-Dependent Microbial Activity in the Context of Subsurface Biomining

Published on: October 5, 2019

7.1K

Related Experiment Videos

Last Updated: Jan 21, 2026

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording
09:58

Fabrication of Ti3C2 MXene Microelectrode Arrays for In Vivo Neural Recording

Published on: February 12, 2020

14.1K
Solar-Driven Electrochemical Green Fuel Production from CO2 and Water Using Ti3C2Tx MXene-Supported CuZn and NiCo Catalysts
10:15

Solar-Driven Electrochemical Green Fuel Production from CO2 and Water Using Ti3C2Tx MXene-Supported CuZn and NiCo Catalysts

Published on: November 7, 2025

519
Using Flexible Gold-Titanium Reaction Cells to Simulate Pressure-Dependent Microbial Activity in the Context of Subsurface Biomining
13:11

Using Flexible Gold-Titanium Reaction Cells to Simulate Pressure-Dependent Microbial Activity in the Context of Subsurface Biomining

Published on: October 5, 2019

7.1K

Area of Science:

  • Materials Science
  • Surface Chemistry
  • Computational Modeling

Background:

  • MXenes are emerging 2D materials with tunable properties.
  • Surface functionalization (O, OH, F) is key to MXene properties.
  • Discrepancy exists between experimental and computational surface structure understanding.

Purpose of the Study:

  • Investigate mixed functionalization on Ti2C and Ti3C2 surfaces.
  • Explain the formation of mixed terminations using multiscale modeling.
  • Clarify the discrepancy between computational and experimental MXene surface studies.

Main Methods:

  • Multiscale modeling scheme.
  • Gibbs free energy of formation calculations.
  • Combination of electronic structure calculations, cluster expansion, and Monte Carlo simulations.

Main Results:

  • Formation of mixed O, OH, and F terminations on Ti2C and Ti3C2 surfaces.
  • Surface composition is dependent on pH, temperature, and work function.
  • Identified a limited stable range of surface compositions.

Conclusions:

  • Mixed terminations are inherent to titanium carbide MXene surfaces.
  • Environmental factors (pH, temperature) influence surface composition.
  • The tunability of MXene properties may be more limited than previously thought.