• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.213-218
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• 2023

Triboelectric nanogenerators (TENGs) have emerged as a highly promising energy harvesting technology capable of harnessing mechanical energy from various environmental vibrations. Their versatility in material selection and efficient conversion of mechanical energy into electric energy make them particularly attractive. TENGs can serve as a valuable technology for self-powered sensor operation in preparation for the IoT era. Additionally, they demonstrate potential for diverse applications, including energy sources for implanted medical devices (IMDs), neural therapy, and wound healing. In this review, we summarize the potential use of this universally applicable triboelectric energy harvesting technology in the disinfection and blocking of pathogens. By integrating triboelectric energy harvesting technology into human clothing, masks, and other accessories, we propose the possibility of blocking pathogens, along with technologies for removing airborne or waterborne infectious agents. Through this, we suggest that triboelectric energy harvesting technology could be an efficient alternative to existing pathogen removal technologies in the future.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.5
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• pp.355-361
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the treatment of swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent is recycled to the pigsty. This system significantly removes offensive smells (at both the pigsty and the treatment plant), BOD and others, and may be cost effective for small-scale farms. The most dominant heterotrophic were, in order, Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp., while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through the use of neural networks. In this study, we tried to model the treatment process for each tank in the system (influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) based upon the population densities of the heterotrophic and lactic acid bacteria. Principal component analysis(PCA) was first applied to identify a relationship between input and output. The input would be microbial densities and the treatment parameters, such as population densities of heterotrophic and lactic acid bacteria, suspended solids(SS), COD, NH$_4$(sup)+-N, ortho-phosphorus (o-P), and total-phosphorus (T-P). then multi-layer neural networks were employed to model the treatment process for each tank. PCA filtration of the input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of imput. Neural network independently trained for each treatment tank and their subsequent combined data analysis allowed a successful prediction of the treatment system for at least two days.

• Korean Journal of Food Science and Technology
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• v.31 no.1
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• pp.1-6
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• 1999

A piezoelectric (PZ) biosensor system detecting Salmonella spp. was developed. The system consisted of an oscillator, a frequency counter and an antibody-immobilized quartz crystal. An anti-Salmonella antibody was immobilized on one gold. surface of the quartz crystal with protein A. Salmonella detection was made by measuring resonant frequency shift owing to a mass change by specific binding of microbial cells to the gold surface of the PZ crystal. The PZ antibody sensor was operated optimally at 0.1M phosphate buffer, pH 7.2 and $35^{\circ}C$. The sensor was quite specific to Salmonella spp. The obtained frequency shift was correlated with the Salmonella concentration in the range of $10^5{\sim}10^6\;CFU/mL$. The frequency shift increased further by addition of polystyrene beads. The Salmonella detection which is indicated by a steady-state microbial adsorption to the quartz crystal was accomplished within 50min.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.35-41
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• 2014

Microbes have been used extensively in various fields of researches and industries but has not been used widely for microfluidic biosensor applications because it is difficult to immobilize properly to a small space. Therefore, we developed a microbial immobilization method for microfluidic devices using single-walled nanotubes and dielectrophoretic force. Single-walled nanotubes and Escherichia coli were aligned between two cantilever electrodes by a positive dielectrophoretic force resulting in a film of single-walled nanotubes with attached Escherichia coli. The optimal condition of film formation without a cell lysis was investigated. Diameter of single-walled nanotubes and electric field (intensity and duration of application) had an effect on the cell viability. On the other hand, the cell concentration of the suspension did not affect the cell viability. Paraoxon was detected using single-walled nanotubes film with attached Escherichia coli that expressed organophosphorus hydrolase. This film which is suspended from the substrate showed faster response time than sensors that are not suspended from the substrate.

• ETRI Journal
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• v.32 no.3
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• pp.440-446
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• 2010

Array-based gas sensors now offer the potential of a robust analytical approach to odor measurement for medical use. We are developing a fast reliable method for detection of microbial infection by monitoring the headspace from the infected wound. In this paper, we present initial results obtained from wound-state monitoring for burn patients using an electronic nose incorporating an automated solid-phase microextraction (SPME) desorption system to enable the system to be used for clinical validation. SPME preconcentration is used for sampling of the headspace air and the response of the sensor module to variable concentrations of volatiles emitted from SPME fiber is evaluated. Gas chromatography-mass spectrometry studies prove that living bacteria, the typical infectious agents in clinical practice, can be distinguished from each other by means of a limited set of key volatile products. Principal component analysis results give the first indication that infected patients may be distinguished from uninfected patients. Microbial laboratory analysis using clinical samples verifies the performance of the system.

• IMMUNE NETWORK
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• v.19 no.1
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• pp.1.1-1.13
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• 2019

Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease characterized by production of autoantibodies to various nuclear antigens and overexpression of genes regulated by IFN-I called IFN signature. Genetic studies on SLE patients and mutational analyses of mouse models demonstrate crucial roles of nucleic acid (NA) sensors in development of SLE. Although NA sensors are involved in induction of antimicrobial immune responses by recognizing microbial NAs, recognition of self NAs by NA sensors induces production of autoantibodies to NAs in B cells and production of IFN-I in plasmacytoid dendritic cells. Among various NA sensors, the endosomal RNA sensor TLR7 plays an essential role in development of SLE at least in mouse models. CD72 is an inhibitory B cell co-receptor containing an immunoreceptor tyrosine-based inhibition motif (ITIM) in the cytoplasmic region and a C-type lectin like-domain (CTLD) in the extracellular region. CD72 is known to regulate development of SLE because CD72 polymorphisms associate with SLE in both human and mice and CD72^-/- mice develop relatively severe lupus-like disease. CD72 specifically recognizes the RNA-containing endogenous TLR7 ligand Sm/RNP by its extracellular CTLD, and inhibits B cell responses to Sm/RNP by ITIM-mediated signal inhibition. These findings indicate that CD72 inhibits development of SLE by suppressing TLR7-dependent B cell response to self NAs. CD72 is thus involved in discrimination of self-NAs from microbial NAs by specifically suppressing autoimmune responses to self-NAs.

• Analytical Science and Technology
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• v.20 no.1
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• pp.1-9
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• 2007

The BOD (biochemical oxygen demand) sensor was fabricated by covering a dissolved oxygen (DO) probe with a microbe-impregnated membrane and a dialysis membrane. Various microorganisms isolated from the soils, water and activated sludge have been evaluated for measuring biochemical oxygen demand (BOD); Bacillus species HN24 and HN93 were selected as they exhibited rapid oxygen consumption and fast recovery. Improved BOD sensor could be prepared by using mixed microbes (Bacillus subtilis, Bacillus sp. HN24 and Bacillus sp. NH93) and silicon rubber gas-permeable membrane for DO probe, and by bubbling 50% $O_2$ ($N_2$ valence) through background buffer solution. This system exhibited excellent analytical performance resulting in good linearity ($r^2=0.9986$) from 0 to 100 mg/L level of BOD.

• Korean Journal of Food Science and Technology
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• v.30 no.1
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• pp.22-34
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• 1998

The purpose of this study is to develop biosensor for determination of glucose, lactate, and ethanol in foods and food-stuffs simultaneously. The multiple cathode system was prepared with an oxygen electrode having one anode and hexagonal cathode. Glucose oxidase, mutarotase, lactate oxidase, alcohol oxidase and catalase were used for immobilization to determine glucose, lactate, and ethanol. These components including ethanol were simultaneously determined by the immobilized enzymes in the multiple cathode system. The determination of the components by enzyme sensor was based on the maximum slope of oxygen consumption from enzyme reaction of each sensor part. The response time for analysis was 1 min. The optimum condition for glucose, lactate and ethanol sensor was found to be 0.1 M potassium phosphate buffer, pH 7.0 at $40^{\circ}C$. Interferences of various sugars and organic acids were investigated. Less than 10% of error was found in determination of the components except organic acids. This difference was compensated by the modified equation. This system was confirmed by conventional methods. It was concluded that the multiple cathode system of this study is for an effective method to determine sugar, organic acid, ethanol simultaneously in foods.

• Journal of Biosystems Engineering
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• v.33 no.4
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• pp.276-281
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• 2008

A flow injection analysis method (FIA), which analyzes sample conditions after injecting a sample and reagents into a continuous stream, are recognized as the most adequate analyzing method according to the increase of sampling frequency, the decrease of measuring time and the diversity of measuring targets. Specially, the FIA is considered to be used effectively for the control of a fermentation process to produce fermentation food and useful microbial production by activation of a fermentation industry for development of biological materials. In this study, a flow injection analysis sensor unit was developed for on-line monitoring of the fermentation process. The performance was verified by on-line measuring the concentration of glucose of the fermentation process of wine. The glucose concentrations of the samples were measured every 12 hours during the whole fermentation process and compared with those by a HPLC. The concentration relative errors of glucose on the fermentation process of wine showed below 30% within 72 hours and over 50% after the 72 hours. The sensor unit had potential to on-line monitoring of the fermentation process but some problems to overcome for an commercial application.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.301-313
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• 2017

Purpose: This study was conducted to discriminate between normal hulled barley and Fusarium (Fusarium asiaticum and Fusarium graminearum) infected hulled barley by using the near-infrared spectroscopy (NIRS) technique. Methods: Fusarium asiaticum and Fusarium graminearum were artificially inoculated in hulled barley and the reflectance spectrum of the barley spike was obtained by using a near-infrared spectral sensor with wavelength band in the range 1,175-2,170 nm. After obtaining the spectrum of the specimen, the hulled barley was cultivated in a greenhouse and visually inspected for infections. Results: From a partial least squares discriminant analysis (PLS-DA) prediction model developed from the raw spectrum data of the hulled barley, the discrimination accuracy for the normal and infected hulled barley was 99.82% (563/564) and 100% (672/672), respectively. Conclusions: NIRS is effective as a quick and nondestructive method to detect whether hulled barley has been infected with Fusarium. Further, it expected that NIRS will be able to detect Fusarium infections in other grains as well.