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

Weak Base Solutions03:21

Weak Base Solutions

25.3K
Some compounds produce hydroxide ions when dissolved by chemically reacting with water molecules. In all cases, these compounds react only partially and so are classified as weak bases. These types of compounds are also abundant in nature and important commodities in various technologies. For example, global production of the weak base ammonia is typically well over 100 metric tons annually, being widely used as an agricultural fertilizer, a raw material for chemical synthesis of other...
25.3K
Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

49.3K
Calculating pH for Titration Solutions: Weak Acid/Strong Base
For the titration of 25.00 mL of 0.100 M CH3CO2H with 0.100 M NaOH, the reaction can be represented as:
49.3K
Titration of a Weak Acid with a Weak Base01:08

Titration of a Weak Acid with a Weak Base

4.9K
Weak acids and bases do not undergo dissociation completely, and titrations between these two are rarely studied. When such studies are performed, say, for the titration of a weak acid with a weak base, the titration curve plots the change in pH as a function of the volume of base added. Take the titration of acetic acid with ammonia, for instance. During the titration, these two species form ammonium acetate and water, but the pH change is slow and gradual.
As a result, there is no simple...
4.9K
Relative Strengths of Conjugate Acid-Base Pairs02:29

Relative Strengths of Conjugate Acid-Base Pairs

52.5K
Brønsted-Lowry acid-base chemistry is the transfer of protons; thus, logic suggests a relation between the relative strengths of conjugate acid-base pairs. The strength of an acid or base is quantified in its ionization constant, Ka or Kb, which represents the extent of the acid or base ionization reaction. For the conjugate acid-base pair HA / A−, the ionization equilibrium equations and ionization constant expressions are
52.5K
Weak Acid Solutions04:02

Weak Acid Solutions

43.2K
Few compounds act as strong acids. A far greater number of compounds behave as weak acids and only partially react with water, leaving a large majority of dissolved molecules in their original form and generating a relatively small amount of hydronium ions. Weak acids are commonly encountered in nature, being the substances partly responsible for the tangy taste of citrus fruits, the stinging sensation of insect bites, and the unpleasant smells associated with body odor. A familiar example of a...
43.2K
Crossed Aldol Reaction Using Weak Bases01:14

Crossed Aldol Reaction Using Weak Bases

2.7K
This lesson deals with the crossed aldol reaction using weak bases. The self-condensation of an aldehyde having α hydrogen is prevented by adding it slowly to a mixture of formaldehyde and weak bases like hydroxide and alkoxide. Upon slow addition of the aldehyde, the base deprotonates the α carbon of the aldehyde to form the corresponding enolate. The enolate subsequently attacks the formaldehyde to form a single crossed product. Figure 1 depicts the aforementioned reaction.
2.7K

You might also read

Related Articles

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

Sort by
Same author

Metamaterial robotics.

Science robotics·2025
Same author

Detection of patient metadata in published articles for genomic epidemiology using machine learning and large language models.

medRxiv : the preprint server for health sciences·2025
Same author

Comparative study of the experimentally observed and GAN-generated 3D microstructures in dual-phase steels.

Science and technology of advanced materials·2024
Same author

Text-to-Microstructure Generation Using Generative Deep Learning.

Small (Weinheim an der Bergstrasse, Germany)·2024
Same author

De-icing performance evolution with increasing hydrophobicity by regulating surface topography.

Science and technology of advanced materials·2024
Same author

Mapping stress inside living cells by atomic force microscopy in response to environmental stimuli.

Science and technology of advanced materials·2023
Same journal

Current status of room temperature magnetic compensation in impurity-doped Mn<sub>4</sub>N epitaxial thin films.

Science and technology of advanced materials·2026
Same journal

Group 8 metallocenes as single-source precursors for the synthesis of light-element-stabilized FCC phases under extreme conditions.

Science and technology of advanced materials·2026
Same journal

Reproducible chiroptical activity from aggregated chiral thienopyrroledione-fluorene π‑conjugated polymers.

Science and technology of advanced materials·2026
Same journal

Wet etching of (-102) β-Ga<sub>2</sub>O<sub>3</sub> with tetramethylammonium hydroxide (TMAH).

Science and technology of advanced materials·2026
Same journal

A novel approach to micro-fabricated thermoelectric generators with SrTiO<sub>3</sub>.

Science and technology of advanced materials·2026
Same journal

Probing the Hall anomaly and electronic structure in kagome metal RbV<sub>3</sub>Sb<sub>5</sub> under hydrostatic pressure.

Science and technology of advanced materials·2026
See all related articles

Related Experiment Video

Updated: Feb 4, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.8K

Relation extraction with weakly supervised learning based on process-structure-property-performance reciprocity.

Takeshi Onishi1, Takuya Kadohira2, Ikumu Watanabe3

  • 1Toyota Technological Institute at Chicago, Chicago, IL, USA.

Science and Technology of Advanced Materials
|September 25, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a computer-aided material design system that extracts knowledge from scientific texts. It uses machine learning to create design charts, enabling faster new material development.

Keywords:
404 Materials informatics / Genomics60 New topics/OthersNatural language processingknowledge extractionmaterials informaticsrelation extractionweakly supervised learning

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
Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain
06:13

Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain

Published on: March 1, 2024

1.8K

Related Experiment Videos

Last Updated: Feb 4, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.8K
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
Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain
06:13

Author Spotlight: Insights into Remotely Supervised Neuromodulation Procedure for Phantom Limb Pain

Published on: March 1, 2024

1.8K

Area of Science:

  • Materials Science
  • Computer Science
  • Natural Language Processing

Background:

  • Material design relies on extracting knowledge from vast scientific literature.
  • Existing methods for knowledge extraction are often manual and time-consuming.
  • The process-structure-property-performance (PSPP) reciprocity is a key framework in materials science.

Purpose of the Study:

  • To develop a computer-aided material design system for automated knowledge extraction.
  • To represent extracted knowledge using a graph format aligned with design charts.
  • To facilitate efficient discovery and development of new materials.

Main Methods:

  • Training a machine learning model on a weakly labeled text corpus.
  • Extracting relationships between scientific concepts (e.g., annealing, grain size, strength).
  • Representing extracted knowledge as a graph formatted into design charts.

Main Results:

  • A system capable of semantically searching scientific literature for material design knowledge.
  • Knowledge representation through design charts illustrating process-property relationships.
  • Demonstration of extracting relationships like {annealing, grain size, strength}.

Conclusions:

  • The developed system enables efficient knowledge extraction from scientific texts.
  • Design charts derived from extracted knowledge offer intuitive insights into material properties.
  • This approach can accelerate the development of novel materials.