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

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

5.6K
A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
5.6K
Autonomic Nervous System01:22

Autonomic Nervous System

13.0K
The autonomic nervous system (ANS) is a critical component of the peripheral nervous system, primarily responsible for regulating involuntary bodily functions and maintaining homeostasis. It functions in tandem with the central nervous system (CNS) to seamlessly coordinate various physiological processes without the need for conscious control.
The ANS comprises two main divisions: the sympathetic and parasympathetic divisions. These divisions function antagonistically to maintain a dynamic...
13.0K
Leaky Scanning02:28

Leaky Scanning

5.7K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.7K
Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Autonomic Nervous System: Overview01:26

Autonomic Nervous System: Overview

7.6K
The human nervous system is divided into two main parts: the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS is composed of the brain and spinal cord, while the PNS contains nerve cells, clusters of nerve cells, and the sensory receptors that are outside the CNS. The PNS has two types of nerve cells: sensory (afferent) and motor (efferent). Sensory cells send signals to the CNS from receptors, and motor cells carry signals from the CNS to organs, muscles, and...
7.6K
Disorders of the Autonomic Nervous System01:18

Disorders of the Autonomic Nervous System

1.6K
The autonomic nervous system (ANS) is an intricate network of nerves that controls functions such as the regulation of heart rate, digestion, and blood pressure regulation. When this system malfunctions, it can lead to various disorders that affect multiple bodily functions. One common feature of many autonomic disorders is the involvement of smooth blood vessels, which play a crucial role in regulating blood flow throughout the body.
Raynaud's disease, also known as Raynaud's...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Electronic Control of Silicon Surface Atomic Structures with Two-Probe Scanning Tunneling Microscopy.

ACS nano·2025
Same author

Structural Control of Atomic Silicon Wires.

ACS nano·2025
Same author

Atomically Precise Manufacturing of Silicon Electronics.

ACS nano·2024
Same author

Ohmic Contact to Two-Dimensional Nanofabricated Silicon Structures with a Two-Probe Scanning Tunneling Microscope.

ACS nano·2021
Same author

Ionic charge distributions in silicon atomic surface wires.

Nanoscale·2021
Same author

Detecting and Directing Single Molecule Binding Events on H-Si(100) with Application to Ultradense Data Storage.

ACS nano·2019
Same journal

Reconfigurable 2D Floating-Gate Field-Effect Transistors with Graphene-Induced Interfacial Polarization for Unified Memory-Logic Integration.

ACS nano·2026
Same journal

Bioinstructive Hybrid Scaffold Integrating Phosphoinositide 3-Kinase-Akt and Complementary Survival Pathways for Kidney Regeneration.

ACS nano·2026
Same journal

Robust Quantum Cutting via Halide-Bearing Ligand Passivation and Gradient Halide Reconstruction for Ultrabroadband Ultraviolet-to-Near-Infrared Photodetection and Imaging.

ACS nano·2026
Same journal

Engineering Interferon-γ-Enhanced Chimeric Antigen Receptor Macrophages via Lipid-Assisted Polymeric Nanoparticles for Cancer Immunotherapy.

ACS nano·2026
Same journal

Self-Assembly of Dual-Metal-Substituted Polyoxometalates into Two-Dimensional Superstructures for Highly Selective Electrocatalytic Imine Synthesis.

ACS nano·2026
Same journal

Dual-Function Halide Exchange Strategy for Simultaneous Sn<sup>4+</sup> Elimination and Stability Enhancement in Pb-Sn Mixed Perovskite Solar Cells.

ACS nano·2026
See all related articles

Related Experiment Video

Updated: Feb 10, 2026

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
13:58

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Published on: September 28, 2016

12.3K

Autonomous Scanning Probe Microscopy in Situ Tip Conditioning through Machine Learning.

Mohammad Rashidi1,2, Robert A Wolkow1,2,3

  • 1Department of Physics , University of Alberta , Edmonton , Alberta T6G 2J1 , Canada.

ACS Nano
|May 24, 2018
PubMed
Summary
This summary is machine-generated.

We developed machine learning methods to automatically check and fix scanning tunneling microscope probes. This ensures accurate atomic-scale characterization and manipulation for advanced materials science research.

Keywords:
convolutional neural networkhydrogen terminated siliconin situ tip conditioningmachine learningscanning probe microscopysurface dangling bonds

More Related Videos

Oral Biofilm Analysis of Palatal Expanders by Fluorescence In-Situ Hybridization and Confocal Laser Scanning Microscopy
09:44

Oral Biofilm Analysis of Palatal Expanders by Fluorescence In-Situ Hybridization and Confocal Laser Scanning Microscopy

Published on: October 20, 2011

16.6K
Quantitative Autonomic Testing
11:40

Quantitative Autonomic Testing

Published on: July 19, 2011

58.7K

Related Experiment Videos

Last Updated: Feb 10, 2026

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
13:58

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

Published on: September 28, 2016

12.3K
Oral Biofilm Analysis of Palatal Expanders by Fluorescence In-Situ Hybridization and Confocal Laser Scanning Microscopy
09:44

Oral Biofilm Analysis of Palatal Expanders by Fluorescence In-Situ Hybridization and Confocal Laser Scanning Microscopy

Published on: October 20, 2011

16.6K
Quantitative Autonomic Testing
11:40

Quantitative Autonomic Testing

Published on: July 19, 2011

58.7K

Area of Science:

  • Materials Science
  • Surface Science
  • Nanotechnology

Background:

  • Scanning probe microscopy requires atomically sharp probes for atomic-scale characterization and manipulation.
  • Maintaining probe quality is crucial for reliable experimental results in surface science.

Purpose of the Study:

  • To develop automated machine learning methods for detecting and reconditioning scanning probe microscopy probe quality.
  • To improve the efficiency and reliability of atomic-scale surface characterization techniques.

Main Methods:

  • Utilized machine learning, specifically convolutional neural networks, for automated probe quality assessment.
  • Trained the network on a hydrogen-terminated silicon surface model system, focusing on surface dangling bond abnormalities.
  • Implemented majority voting with multiple comparison points to enhance detection accuracy.

Main Results:

  • A convolutional neural network achieved 97% accuracy in identifying degraded scanning tunneling microscope tips.
  • The accuracy of probe quality detection was improved to over 99% using ensemble methods like majority voting.
  • Demonstrated the effectiveness of automated methods for maintaining probe integrity in atomic-scale surface studies.

Conclusions:

  • Automated machine learning methods can reliably detect and improve scanning probe microscopy probe quality.
  • These techniques enhance the precision and efficiency of atomic-scale characterization and manipulation.
  • The developed approach offers a significant advancement for nanotechnology and materials science research.