↓ Skip to main content

Sensor Selection and Chemo-Sensory Optimization: Toward an Adaptable Chemo-Sensory System

Overview of attention for article published in Frontiers in Neuroengineering, January 2012
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
36 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Sensor Selection and Chemo-Sensory Optimization: Toward an Adaptable Chemo-Sensory System
Published in
Frontiers in Neuroengineering, January 2012
DOI 10.3389/fneng.2011.00019
Pubmed ID
Authors

Alexander Vergara, Eduard Llobet

Abstract

Over the past two decades, despite the tremendous research on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors), environment monitoring (widely distributed sensor networks), and security/threat detection (chemo/bio warfare agents), simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro- and nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change. The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to "adapt" in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the field may evolve.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 6%
Sweden 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 28%
Professor 4 11%
Student > Doctoral Student 3 8%
Researcher 3 8%
Student > Bachelor 2 6%
Other 5 14%
Unknown 9 25%
Readers by discipline Count As %
Engineering 12 33%
Computer Science 4 11%
Chemistry 3 8%
Agricultural and Biological Sciences 2 6%
Medicine and Dentistry 1 3%
Other 3 8%
Unknown 11 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2012.
All research outputs
#18,313,878
of 22,675,759 outputs
Outputs from Frontiers in Neuroengineering
#61
of 82 outputs
Outputs of similar age
#195,972
of 244,088 outputs
Outputs of similar age from Frontiers in Neuroengineering
#11
of 17 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 82 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 244,088 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.