• Proceedings of the Korean Vacuum Society Conference
• /
• 2008.08a
• /
• pp.148-148
• /
• 2008

• Proceedings of the Optical Society of Korea Conference
• /
• 2006.07a
• /
• pp.399-400
• /
• 2006

Etched fiber Bragg grating, Sensitivity of fiber-Bragg-grating sensors to index of surrounding, Hybridization of DNA

• Proceedings of the KIEE Conference
• /
• 1987.07a
• /
• pp.490-492
• /
• 1987

Urea-Sensor was fabricated by immobilizing urease on ISFET's gate using BSA(bovine serum albumin) and glutaraldehyde, and its characteristics were examined. This sensor showed approximately linear characteristic in the urea concentration range of $3{\times}10^{-5}-10^{-9}$ (g/ml). Fast response time was obtained and minute amounts of expensive enzyme were used in comparison to general electrode type biosensors.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.11a
• /
• pp.314-316
• /
• 2000

Urea is detected as an indicator of renal disease in the human body. For these reasons, many biosensors for urea have been developed based on the enzymatic reaction of urea hydrolysis catalyzed by urease. Potentiometric method is applied reversible reaction system. But urea hydrolysis reaction may not has a reversible reaction mechanism in electrode surface. Therefore we applied to voltammtricmethod to obtain a sensitivity curve. The sensitivity of sensors was 34 ${\mu}$A/decade.

• Polymer Science and Technology
• /
• v.26 no.1
• /
• pp.9-15
• /
• 2015

• Journal of Sensor Science and Technology
• /
• v.30 no.1
• /
• pp.1-9
• /
• 2021

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.5
• /
• pp.1036-1057
• /
• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Analytical Science and Technology
• /
• v.29 no.1
• /
• pp.35-42
• /
• 2016

A new nano-composite carbon ink for the development of disposable dopamine (DA) biosensors based on screen-printed carbon electrodes (SPCEs) is introduced. The method developed uses SPCEs coupled with a tyrosinase modified nano-composite carbon ink. The ink was prepared by an “in-house” procedure with reduced graphene oxide (rGO), Pt nanoparticles (PtNP), and carbon materials such as carbon black and graphite. The rGO-PtNP carbon composite ink was used to print the working electrodes of the SPCEs and the reference counter electrodes were printed by using a commercial Ag/AgCl ink. After the construction of nano-composite SPCEs, tyrosinase was immobilized onto the working electrodes by using a biocompatible matrix, chitosan. The composite of nano-materials was characterized by X-ray photoelectron spectroscopy (XPS) and the performance characteristics of the sensors were evaluated by using voltammetric and amperometric techniques. The cyclic voltammetry results indicated that the sensors prepared with the rGO-PtNP-carbon composite ink revealed a significant improvement in electro-catalytic activity to DA compared with the results obtained from bare or only PtNP embedded carbon inks. Optimum experimental parameters such as pH and operating potential were evaluated and calibration curves for dopamine were constructed with the results obtained from a series of amperometric detections at −0.1 V vs. Ag/AgCl. The limit of detection was found to be 14 nM in a linear range of 10 nM to 100 µM of DA, and the sensor’s sensitivity was calculated to be 0.4 µAµM⁻¹cm⁻².

• Proceedings of the Korean Vacuum Society Conference
• /
• 2016.02a
• /
• pp.378.2-378.2
• /
• 2016

Biosensors currently suffer from severe non-specific adsorption of proteins, which causes false positive errors in detection through overestimation of the affinity value. Overcoming this technical issue motivates our research. Polyethylene glycol (PEG) is well known for its ability to reduce the adsorption of biomolecules; hence, it is widely used in various areas of medicine and other biological fields. Likewise, amine functionalized surfaces are widely used for biochemical analysis, drug delivery, medical diagnostics and high throughput screening such as biochips. As a result, many coating techniques have been introduced, one of which is plasma polymerization - a powerful coating method due to its uniformity, homogeneity, mechanical and chemical stability, and excellent adhesion to any substrate. In our previous works, we successfully fabricated plasmapolymerized PEG (PP-PEG) films [1] and amine functionalized films [2] using the plasma enhanced chemical vapor deposition (PECVD) technique. In this research, an amine functionalized PP-PEG film was fabricated by using the plasma co-polymerization technique with PEG 200 and ethylenediamine (EDA) as co-precursors. A biocompatible amine functionalized film was surface characterized by X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FT-IR). The density of the surface amine functional groups was carried out by quantitative analysis using UV-visible spectroscopy. We found through surface plasmon resonance (SPR) analysis that non-specific protein adsorption was drastically reduced on amine functionalized PP-PEG films. Our functionalized PP-PEG films show considerable potential for biotechnological applications such as biosensors.

• Journal of Microbiology and Biotechnology
• /
• v.27 no.4
• /
• pp.838-843
• /
• 2017

Sepsis is a major health problem worldwide, with an extremely high rate of morbidity and mortality, partly due to delayed diagnosis during early disease. Currently, sepsis diagnosis requires bacterial culturing of blood samples over several days, whereas PCR-based molecular diagnosis methods are faster but lack sensitivity. The use of biosensors containing nucleic acid aptamers that bind targets with high affinity and specificity could accelerate sepsis diagnosis. Previously, we used the systematic evolution of ligands by exponential enrichment technique to develop the aptamers Antibac1 and Antibac2, targeting the ubiquitous bacterial peptidoglycan. Here, we show that these aptamers bind to four gram-positive and seven gram-negative bacterial sepsis agents with high binding efficiency. Thus, these aptamers could be used in combination as biological recognition elements in the development of biosensors that are an alternative to rapid bacteria detection, since they could provide culture and amplification-free tests for rapid clinical sepsis diagnosis.