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Adisorn Tuantranont

Researcher at Thailand National Science and Technology Development Agency

Publications -  330
Citations -  9008

Adisorn Tuantranont is an academic researcher from Thailand National Science and Technology Development Agency. The author has contributed to research in topics: Graphene & Engineering. The author has an hindex of 44, co-authored 291 publications receiving 7104 citations. Previous affiliations of Adisorn Tuantranont include NECTEC & Asian Institute of Technology.

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Semiconducting metal oxides as sensors for environmentally hazardous gases

TL;DR: In this paper, the authors extensively review the development of semiconductor metal oxide gas sensors for environmentally hazardous gases including NO2, NO, N2O, H2S, CO, NH3, CH4, SO2 and CO2.
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MEMS-based micropumps in drug delivery and biomedical applications

TL;DR: There is still a need to incorporate various categories of micropumps in practical drug delivery and biomedical devices and this will continue to provide a substantial stimulus for micropump research and development in future.
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Metamaterial-based microfluidic sensor for dielectric characterization

TL;DR: In this article, a microfluidic sensor is implemented from a single split-ring resonator (SRR), a fundamental building block of electromagnetic metamaterials, which is capable of sensing liquid flowing in the channel with a cross-sectional area as small as (0.001 λ 0 ) 2.
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Multiplex Paper-Based Colorimetric DNA Sensor Using Pyrrolidinyl Peptide Nucleic Acid-Induced AgNPs Aggregation for Detecting MERS-CoV, MTB, and HPV Oligonucleotides

TL;DR: A paper-based colorimetric assay for DNA detection based on pyrrolidinyl peptide nucleic acid (acpcPNA)-induced nanoparticle aggregation is reported as an alternative to traditional colorimetry approaches.
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Portable electronic nose based on carbon nanotube-SnO2 gas sensors and its application for detection of methanol contamination in whiskeys

TL;DR: In this paper, a portable electronic nose (E-nose) based on hybrid carbon nanotube-SnO2 gas sensors is described, which employs feature extraction techniques including integral and primary derivative, which lead to higher classification performance as compared to the classical features.