Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Carrier Transport

Carrier Transport

The generation of electrical current in semiconductors is fundamentally driven by two mechanisms: drift and diffusion. These processes are essential for the functionality and performance of semiconductor-based devices.
Drift Current:
The drift of charge carriers is started by an external electric field (E). Charged particles, such as electrons and holes, experience an acceleration between collisions with lattice atoms. For electrons, this results in a drift velocity (vd) given by:

Basic Postulates of Kinetic Molecular Theory: Particle Size, Energy, and Collision

Basic Postulates of Kinetic Molecular Theory: Particle Size, Energy, and Collision

The ideal-gas equation, which is empirical, describes the behavior of gases by establishing relationships between their macroscopic properties. For example, Charles’ law states that volume and temperature are directly related. Gases, therefore, expand when heated at constant pressure. Although gas laws explain how the macroscopic properties change relative to one another, it does not explain the rationale behind it.

Reynolds Transport Theorem

Reynolds Transport Theorem

The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit mass.

Collisions in Multiple Dimensions: Introduction

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...

The Kinetic Model of Gases

The Kinetic Model of Gases

The kinetic model of gases explains the properties of a perfect gas using three main assumptions: molecules move in ceaseless random motion, their size is negligible compared to the distances between them, and they do not interact except during perfectly elastic collisions. The total energy of a gas is the sum of the kinetic energies of all its constituent molecules. The pressure exerted by the gas arises from the continual bombardment of the container walls by billions of colliding molecules.

Conservation of Linear Momentum for a System of Particles

Conservation of Linear Momentum for a System of Particles

In the dynamic realm of billiards, a fascinating interplay of forces governs the motion of cue balls and stationary balls. When the cue ball collides with a stationary ball, linear momentum is exchanged. The cue ball imparts a fraction of its linear momentum to the stationary ball, causing the cue ball to decelerate while initiating the motion of the stationary ball.
The impulsive force at play during this interaction is of extremely short duration, rendering its impulse negligible. When...

You might also read

Related Articles

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

Sort by

Same journal

MRI-based gross tumor volume delineation in high-grade glioma using a deep convolutional neural network.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same journal

Development and biodistribution of <sup>131</sup>I-radiolabeled silver-doped indium oxide nanoparticles as a novel nanoplatform for tumor targeting in tumor bearing mice.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same journal

Physics-informed, uncertainty-aware surrogate screening of gamma-ray attenuation in lead-free multicomponent oxide glasses.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same journal

Investigation of radiochemical procedures for the sequential separation of Pu, Am, and Cm in radioactive waste with alpha spectrometry.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same journal

Advances in computational anthropomorphic phantoms and Monte Carlo-based dosimetry: A review.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same journal

A new low uncertainty measurement of the <sup>111</sup>Ag half-life.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Search research articles

Related Experiment Video

Updated: May 26, 2026

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Common misconceptions in Monte Carlo particle transport.

Thomas E Booth¹

¹LANL, XCP-7, MS F663, Los Alamos, NM 87545, United States. teb@lanl.gov

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine

|December 16, 2011

Summary

This summary is machine-generated.

Monte Carlo particle transport simulations can be misunderstood when viewed solely through the lens of solving equations. Addressing these misconceptions helps avoid limiting the development of future Monte Carlo methods.

More Related Videos

Fabrication and Operation of a Nano-Optical Conveyor Belt

Fabrication and Operation of a Nano-Optical Conveyor Belt

Published on: August 26, 2015

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Related Experiment Videos

Last Updated: May 26, 2026

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Fabrication and Operation of a Nano-Optical Conveyor Belt

Fabrication and Operation of a Nano-Optical Conveyor Belt

Published on: August 26, 2015

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Area of Science:

Computational physics
Numerical methods

Background:

Monte Carlo methods are frequently presented as tools for solving linear integral equations, like the Boltzmann transport equation.
This perspective can lead to common misunderstandings about their capabilities and applications.

Purpose of the Study:

To clarify prevalent misconceptions surrounding Monte Carlo methods.
To differentiate the equation-based focus from the broader applicability of Monte Carlo techniques.

Main Methods:

Discussion of common misconceptions in Monte Carlo particle transport.
Analysis of the equation-centric viewpoint and its limitations.

Main Results:

Identified several widespread misconceptions about Monte Carlo methods.
Highlighted that these misconceptions often stem from an overemphasis on solving specific equations.

Conclusions:

Recognizing and correcting these misconceptions is crucial.
Avoiding an equation-based focus can prevent unnecessary restrictions on future Monte Carlo method development and application.