Evidence map›Paper›PMID 33681147›Full record

ReviewFrontiers in chemistry2021

Nanoengineering Approaches Toward Artificial Nose.

Sanggon Kim, Jacob Brady, Faraj Al-Badani, Sooyoun Yu, Joseph Hart, Sungyong Jung, Thien-Toan Tran, Nosang V Myung

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 31 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Nonequilibrium sensing of volatile compounds using active and passive analyte delivery.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  6. Humidity Effect on Low-Temperature NHSensors (Basel, Switzerland) · 2023
    Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Sanggon KimDepartment of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States.
Jacob BradyDepartment of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States.
Faraj Al-BadaniDepartment of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States.
Sooyoun YuDepartment of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States.
Joseph HartDepartment of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States.
Sungyong JungDepartment of Electrical Engineering, University of Texas at Arlington, Arlington, TX, United States.
Thien-Toan TranDepartment of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, United States.
Nosang V MyungDepartment of Chemical and Environmental Engineering, University of California-Riverside, Riverside, CA, United States.
University of Notre Dame · USUniversity of California, Riverside · USThe University of Texas at Arlington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significant scientific efforts have been made to mimic and potentially supersede the mammalian nose using artificial noses based on arrays of individual cross-sensitive gas sensors over the past couple decades. To this end, thousands of research articles have been published regarding the design of gas sensor arrays to function as artificial noses. Nanoengineered materials possessing high surface area for enhanced reaction kinetics and uniquely tunable optical, electronic, and optoelectronic properties have been extensively used as gas sensing materials in single gas sensors and sensor arrays. Therefore, nanoengineered materials address some of the shortcomings in sensitivity and selectivity inherent in microscale and macroscale materials for chemical sensors. In this article, the fundamental gas sensing mechanisms are briefly reviewed for each material class and sensing modality (electrical, optical, optoelectronic), followed by a survey and review of the various strategies for engineering or functionalizing these nanomaterials to improve their gas sensing selectivity, sensitivity and other measures of gas sensing performance. Specifically, one major focus of this review is on nanoscale materials and nanoengineering approaches for semiconducting metal oxides, transition metal dichalcogenides, carbonaceous nanomaterials, conducting polymers, and others as used in single gas sensors or sensor arrays for electrical sensing modality. Additionally, this review discusses the various nano-enabled techniques and materials of optical gas detection modality, including photonic crystals, surface plasmonic sensing, and nanoscale waveguides. Strategies for improving or tuning the sensitivity and selectivity of materials toward different gases are given priority due to the importance of having cross-sensitivity and selectivity toward various analytes in designing an effective artificial nose. Furthermore, optoelectrical sensing, which has to date not served as a common sensing modality, is also reviewed to highlight potential research directions. We close with some perspective on the future development of artificial noses which utilize optical and electrical sensing modalities, with additional focus on the less researched optoelectronic sensing modality.

Indexed as

artificial noseelectrical sensor arrayelectronic nosegas sensornanomaterialsoptical sensoroptoelectrical sensor

Identifiers

PMID33681147
PMCPMC7935515
OpenAlexW3132657456

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.