In this paper, a simple, cost-effective, and efficient electrochemical sensor for molecular imprinting melatonin was established. The molecular imprinted films were formed by in situ electrochemical polymerization using molecular imprinting technology. The modification method, modification time and other parameters of the electrode were optimized. Under optimized conditions, the sensor responds to melatonin concentration in a linear range of 0–100 μM. The detection limit was 0.171 μM. In addition, the sensor has little response to interfering substances, such as uric acid, vitamin B6, vitamin C, and glucose, and can be tested in real samples. The recoveries were 98.73–101.60%.

Graphene-based sensors have emerged as significant tools for biosensing applications due to their unique electrical, mechanical, and thermal properties. In this study, we have developed an innovative and sensitive aptasensor based on the surfacemodified graphene for the detection of lung cancer biomarker CA125. The sensor leverages the combination of graphene surface and gold nanoparticles (AuNPs) electrodeposition to achieve a high level of sensitivity and selectivity for the biomarker detection. A noticeable decrease in electron transfer resistance was observed upon the AuNPs deposition, demonstrating the enhancement of electrochemical performance. Our experimental findings showed a strong linear relationship between the sensor response and CA125 concentrations, ranging from 0.2 to 15.0 ng/mL, with a detection limit of 0.085 ng/ mL. This study presents a novel approach to lung cancer detection, surpassing the traditional methods in terms of invasiveness, cost, and accuracy. The results from this work could pave the way for the development of graphene-based sensors in various other biosensing applications.

Sulfur and nitrogen co-doped carbon dots (NSCDs) were quickly synthesized by the microwave-assisted method from triammonium citrate and thiourea. NSCDs showed a quantum yield of 11.5% with excitation and emission bands at 355 and 432 nm, respectively. Also, a fluorescence quenching was observed in the presence of Pb(II) ions, and the as-synthesized CDs were used as a sensitive probe for detecting Pb(II) in water and food samples. The results showed the optimal conditions for Pb(II) determination were CDs concentration of 0.02 mg mL− 1 at pH 6.0–7.0 and an incubation time of 20 min. The relative fluorescence intensity of NSCDs was proportional to Pb(II) concentrations in the range of 0.029–2.40 and 2.40–14.4 μmol L− 1 with a correlation coefficient (R2) of 0.998 and 0.955, respectively, and a detection limit of 9.2 × 10– 3 μmol L− 1. Responses were highly repeatable, with a standard deviation below 3.5%. The suggested method demonstrates the potential of a green, fast, and low-cost approach for Pb(II) determination in water, tea, and rice samples with satisfying results.

In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

Wild birds, especially aquatic birds, are the natural reservoir of avian influenza virus (AIV), and many kinds of water body can be contaminated with feces of these birds. Seasonally, AIVs can be dissolved in the environmental water from the feces of the infected birds, and this water can be a target for viral detection and identification. In this study, we employed and tested three different filters for concentrating AIV, and it was shown that high concentration factor in terms of viral density could be achieved with viral samples diluted with natural water. Wild bird fecal samples containing low pathogenicity H5 AIVs were successfully concentrated with the adsorption and elution method using mixed cellulose esters membrane; the recovery rate of virus was 35.5 % and the concentration factor was about 50 on average. For the larger volume of water sample, we proved that an inline disposable filter with high surface area, 300 cm2, has a comparable concentration factor to the adsorption and elution method and the filter could be used in the field conveniently by being plugged into peristaltic pump. These validated methods for water sampling may be used as a supplementary for virological surveillance on wild migratory birds or during the epidemiological investigation on the environment near affected premises by AIV.

Highly luminescent carbon quantum dots (CQDs) are developed as fluorescent probes for selective detection of the heavy-ion Fe3+, where the CQDs exhibit excellent nontoxicity, functionalizability, sensitivity, and selectivity. Biomass-based CQDs and nitrogen-doped CQDs (N-CQDs) are synthesized for the selective detection of Fe3+ by using H2O2 as an oxidant and polyetherimide (PEI) as a nitrogen precursor by a green hydrothermal synthesis method. The prepared CQDs and N-CQDs exhibit an elliptical morphology and with an average particle size of 7 and 4 nm, respectively, and emit blue photoluminescence at 445 and 468 nm under excitation at 367 and 343 nm, respectively. The CQDs and N-CQDs exhibit good water solubility because of the abundant hydroxyl and carboxyl/carbonyl groups and graphic/pyrrolic/pyridinic nitrogen on the surfaces, giving rise to a quantum yield of about 24.2% and 30.7%, respectively. Notably, the Matrimony vine-PEI-based CQDs exhibit excellent Fe3+ selectivity and sensitivity relative to the Matrimony vine-based CQDs due to complexation of the numerous phenolic hydroxyl groups and nitrogen-containing groups with Fe3+, leading to increased fluorescence quenching, which greatly improves the sensitivity of detection. The minimum detection limit was 2.22 μmol L− 1 with a complexation constant of 44.7.

본 연구는 다양한 식재료가 섞여있는 식품으로부터 노로바이러스를 효과적으로 검출하기 위한 시험법 개발에 관한 것이다. 각 식품이 가진 매트릭스가 매우 다르므로 모든 식품에 공통적으로 적용할 수 있는 표준화된 검출법이 현재로서는 없다. 본 연구에서는 발효식품(농후발효유,된장), 절임식품(깻잎장아찌, 단무지)과 생식제품을 대상으로 실험을 진행하였다. PBS, beef extract, 아미노산-NaCl 용액 등을 이용하여 바이러스에 오염된 대상식품들로 부터 바이러스의 탈리율을 비교하였다. 이로부터 다양한 매트릭스가 혼합된 식품들에 적용 가능한 탈리용액을 선별하였다. 식품의약품안전처가 제안하여 현재 국내에서 굴, 야채, 과일 등을 대상으로 바이러스 농축에 널리 사용되고 있는 식중독 바이러스 검출법인 EPCP공정(탈리-PEG 침전-클로르포름 처리-PEG 침전)과 PEG 침전과정을 한번으로 줄인 수정된 ECP공정(탈리-클로르포름 처리-PEG침전)의 효능을 비교해 본 결과 ECP공정은 EPCP공정과 비슷하거나 나은 효율로 바이러스를 농축할 수 있었다. 또 바이러스 농축 후 QIAamp® Viral RNA Mini kit를 이용하여 RNA를 추출할 경우 식품에 존재하는 RT-PCR방해 물질들이 효과적으로 제거되어 추가적인 처리가 더 필요하지 않음을 알 수 있었다. 수정된 공정을 이용하여 무를 추가한 6가지 식품을 대상으로 검출한계를 조사해 본 바 10-25 g 식품으로부터 3.125-12.5 RT-PCR unit까지 검출이 가능하였다. 이는 기존에 보고된 방법들의 검출한계보다 더 우수한 것으로 장차 다양한 식품을 대상으로 효과적인 바이러스 검출이 가능할 것으로 기대된다.

In this study, highly sensitive hydrogen micro gas sensors of the multi-layer and micro-heater type were designed and fabricated using the micro electro mechanical system (MEMS) process and palladium catalytic metal. The dimensions of the fabricated hydrogen gas sensor were about 5mm×4mm and the sensing layer of palladium metal was deposited in the middle of the device. The sensing palladium films were modified to be nano-honeycomb and nano-hemisphere structures using an anodic aluminum oxide (AAO) template and nano-sized polystyrene beads, respectively. The sensitivities (Rs), which are the ratio of the relative resistance were significantly improved and reached levels of 0.783% and 1.045 % with 2,000 ppm H2 at 70˚C for nano-honeycomb and nano-hemisphere structured Pd films, respectively, on the other hand, the sensitivity was 0.638% for the plain Pd thin film. The improvement of sensitivities for the nano-honeycomb and nano-hemisphere structured Pd films with respect to the plain Pd-thin film was thought to be due to the nanoporous surface topographies of AAO and nano-sized polystyrene beads.

Deformed wing virus (DWV) is a serious pathogen of the honeybee, Apis mellifera L., vectored by the parasitic mite Varroa destructor. The virus is associated with wing deformity in symptomatic bees, and premature death and reduced colony performance in asymptomatic bees. In present study a novel micro PCR-based detection method, termed as ultra-rapid real-time PCR (UR-RT PCR), was developed for the fast and quantitative detection of DWV in honeybee. A specific detection primer set (DWV-UR-F3/R3) was used for the amplification of an unique 133-bp DNA fragment of DWV with a rapid real -time PCR system, GenSpector® TMC-1000, which proceed the cycling with fast heating and cooling rates and a small reaction volume. We showed that this method is able to detect DWV with DNA conditions, artificial recombinant DNA, pBX-DWV479 as well as with virus-infected honeybee samples. In application to a DWV-infected honey bee, the minimum detection time was 8 min 50 seconds under 30 cycles and 10min 11 seconds including melting temperature analysis. This optimizing detection method is one of the fastest real-time PCR-based diagnostic tools and is available to be applied to use for the detection in the field and of various persistency pathogens.