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Related Concept Videos

Olfaction01:25

Olfaction

48.1K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
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Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

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The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
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Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
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Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Related Experiment Video

Updated: Jan 16, 2026

Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase
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Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase

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Entropy Reduction Across Odor Fields.

Hugo Magalhães1, Lino Marques1

  • 1Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, Portugal.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

Cognitive Odor Source Localization (OSL) strategies can be simplified. Analyzing information gain globally reveals high-gain regions for efficient, bio-inspired searching, reducing computational cost.

Keywords:
cognitive decision-makingentropymobile roboticsodor source localization

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Environmental Science

Background:

  • Cognitive Odor Source Localization (OSL) uses Bayesian inference for turbulent environments.
  • Current OSL methods are computationally expensive for real-time applications.

Purpose of the Study:

  • To investigate simpler, bio-inspired strategies for OSL.
  • To identify global patterns of information gain for navigational cues.

Main Methods:

  • Analysis of spatial distribution of entropy reductions across the entire search area.
  • Mapping information gain over the full environment, not just local neighborhoods.

Main Results:

  • High-gain regions for information gain identified near the source and plume borders.
  • Expected entropy reduction is significantly influenced by prior belief and sensor data.
  • Global analysis reveals spatial patterns missed by local approaches.

Conclusions:

  • Hybrid OSL strategies can maintain effectiveness while reducing computational demands.
  • Bio-inspired approaches based on global information gain show promise for efficient OSL.