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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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A disposable amperometric biosensor based on inkjet-printed Au/PEDOT-PSS nanocomposite for triglyceride determination

TL;DR: In this paper, the authors presented a new and highly sensitive disposable amperometric triglyceride (TG) biosensor based on Au/PEDOT-PSS nanocomposite inkjet-printed on screen-printed carbon electrodes (SPCEs) and co-immobilization of lipase, glycerol kinase and glyceroline-3-phosphate oxidase.
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Inkjet-printed graphene-poly(3,4-ethylenedioxythiophene):poly(styrene-sulfonate) modified on screen printed carbon electrode for electrochemical sensing of salbutamol

TL;DR: In this paper, a simple and sensitive inkjet-printed graphene poly(3,4-ethylenedioxythiophene):poly(styrene-sulfonate) (GP-PEDOT:PSS) on screen printed carbon electrode (SPCE) is developed for detection of salbutamol (SAL), a prohibited drug in sport.
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Ultra-sensitive and highly selective H2 sensors based on FSP-made Rh-substituted SnO2 sensing films

TL;DR: In this article, the authors synthesize SnO 2 nanoparticles doped with 0.1-2% rhodium (Rh) by flame spray pyrolysis and systematically investigated for H 2 -sensing applications.
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Flow injection based microfluidic device with carbon nanotube electrode for rapid salbutamol detection

TL;DR: The proposed combination of the efficient CNT electrode and miniaturized lab-on-a-chip is a powerful platform for beta-agonists detection for in-channel detection of salbutamol.
Proceedings ArticleDOI

Wireless black box using MEMS accelerometer and GPS tracking for accidental monitoring of vehicles

TL;DR: In this work, wireless black box using MEMS accelerometer and GPS tracking system is developed for accidental monitoring and can detect linear fall, non-linear fall and normal ride with high accuracy.