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

Quantitative Analysis01:12

Quantitative Analysis

1.5K
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
1.5K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.1K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
2.1K
Machines01:19

Machines

579
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...
579
Exponential Functions with Base e01:30

Exponential Functions with Base e

256
Exponential functions with base e are essential for modeling continuous processes of growth and decay. The constant e, approximately 2.718, naturally arises in systems where change occurs proportionally to the current value. A positive exponent represents continuous growth, while a negative exponent represents continuous decay. These functions are especially useful for describing situations where change happens smoothly over time rather than in discrete steps.One clear example of exponential...
256
Machines: Problem Solving II01:30

Machines: Problem Solving II

673
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
673
Machines: Problem Solving I01:22

Machines: Problem Solving I

716
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
716

You might also read

Related Articles

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

Sort by
Same author

Lenvatinib Combined with New FP Hepatic Arterial Infusion Chemotherapy for Unresectable Hepatocellular Carcinoma: Clinical Efficacy, Vascular Remodeling, and Implications for Immuno-Oncology-Systemic Combination Therapy.

Current oncology (Toronto, Ont.)·2026
Same author

Aplastic or twig-like middle cerebral artery with the RNF213 variant: illustrative cases.

Journal of neurosurgery. Case lessons·2026
Same author

Delayed surgical management of extensive bilateral multilobar congenital pulmonary airway malformation.

BMJ case reports·2026
Same author

Genomic profiling of meiotic errors and early malignant transformation events in ovarian mature teratoma.

Reproduction (Cambridge, England)·2026
Same author

Pleuropulmonary Blastoma Presenting as an Oncologic Emergency.

Cureus·2026
Same author

The role of the tryptophan-rich allosteric network and sodium egress in GPCR activation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Strategic Design and Engineering of CRISPR/Cas-Powered Sensing Platforms for Enhanced Nucleic Acid Detection.

ACS sensors·2026
Same journal

Broad-Temperature Polymerase in Nucleic Acid Amplification-Based Diagnostics: From Thermal Precision to Dynamic Conditions.

ACS sensors·2026
Same journal

Fluidic Lipid-Bilayer-Enhanced Iontronic Nanopore: Machine-Learning-Driven Ultrasensitive MicroRNA Detection in Cancer Diagnostics.

ACS sensors·2026
Same journal

Plant-Plant Communication for Systemic Acquired Resistance under Biotic Stress Spatiotemporally Tracked by an <i>In Situ</i> Surface-Enhanced Raman Spectroscopy Aerosol Spraying Analyzer.

ACS sensors·2026
Same journal

Modulating Electronic Structure via Bimetallic D<i>-</i>Band Engineering toward an Ultrasensitive Sensor Platform for Caffeic Acid in Food.

ACS sensors·2026
Same journal

Indiscriminate <i>T</i><i>rans</i>-Cleavage Activity of CRISPR/SuCas12a2 Enables Sensitive Detection of SARS-CoV-2.

ACS sensors·2026
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis.

Kota Shiba, Ryo Tamura1,2, Takako Sugiyama

  • 1Research and Services Division of Materials Data and Integrated System , National Institute for Materials Science , 1-2-1 Sengen , Tsukuba , Ibaraki 305-0047 , Japan.

ACS Sensors
|August 16, 2018
PubMed
Summary
This summary is machine-generated.

This study uses nanomechanical sensing and machine learning to accurately analyze complex odor mixtures. Material design optimization significantly improved the prediction of individual chemical concentrations in a multi-component system.

Keywords:
MSSmachine learningnanomechanical sensingnanoparticleodorquantificationsensor arraysurface functionality

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

Related Experiment Videos

Last Updated: Feb 6, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

Area of Science:

  • Nanotechnology
  • Chemical Sensing
  • Machine Learning

Background:

  • Odor sensing signals contain rich information but require advanced analysis.
  • Accurate analysis of complex mixtures, especially with similar chemical structures, remains challenging.

Purpose of the Study:

  • To develop a quantitative odor analysis method using nanomechanical sensing and machine learning.
  • To optimize material design for enhanced sensing accuracy in a ternary mixture of water, ethanol, and methanol.

Main Methods:

  • Synthesized six types of functionalized nanoparticles for sensor receptor coatings.
  • Employed a Gaussian process regression machine learning model trained on diverse concentration data.
  • Iteratively optimized nanoparticle composition based on machine learning feedback.

Main Results:

  • Octadecyl-modified nanoparticles improved water concentration prediction.
  • Combined octadecyl and aminopropyl groups enhanced ethanol and methanol prediction accuracy.
  • Material design optimization led to significant improvements in predictive performance.

Conclusions:

  • Systematic material design coupled with machine learning enables accurate quantitative odor analysis.
  • This data-driven approach effectively refines sensor performance for complex chemical mixtures.
  • The study provides a framework for addressing critical challenges in gas-phase sensing.