3D DNAzyme walker based electrochemical biosensor for attomolar level microRNA-155 detection.

Herein, an ultrasensitive electrochemical biosensor for microRNA-155 (miR-155) detection based on the powerful catalytic and continuous walking signal amplification capability of 3D DNAzyme walker and the gold nanoparticles/graphene aerogels carbon fiber paper-based (AuNPs/GAs/CFP) flexible sensing electrode with excellent electrochemical performance was successfully constructed. In a proof-of-concept experiment, in the presence of miR-155, the DNAzyme strands anchored on the streptavidin-modified magnetic beads (MBs) silenced by locked strands can be activated, thus generating the walking arm of the 3D DNAzyme walker. Meanwhile, the substrate strands modified with Fe-MOF-NH2 nanoparticles were evenly distributed on the surface of MBs and served as tracks of the 3D DNAzyme walker. Once the DNAzyme strand was activated, the catalytic site in the substrate strand can be cleaved in the presence of Mn2+, and a large number of stumps modified with Fe-MOF-NH2 nanoparticles (output@Fe-MOF-NH2) will be generated during the continuous and efficient walking cleavage of the DNAzyme walker, driving the recognition-catalysis-release cycle process for signal amplification. Immediately afterwards, the signal was read out through the base complementary pairing of capture probe (PS) immobilized on the surface of the paper-based flexible sensing electrode AuNPs/GAs/CFP and signal probes output@Fe-MOF-NH2, thus achieving the quantitative detection of miR-155. Under optimal experimental conditions, the designed 3D DNAzyme walker-based biosensor exhibited a relatively lower limit of detection (LOD) of 56.23 aM, with a linear range of 100 aM to 100 nM. Overall, the proposed 3D DNAzyme walker biosensor exhibited good interference and reproducibility, demonstrating a promising future in the field of clinical disease diagnosis.

An electrochemical sensor based on FeCo bimetallic single-atom nanozyme for sensitive detection of H2O2.

Efficient catalytic decomposition of H2O2 is accompanied by electron transfer through Fe-Nx active sites of hemin in the human body. Inspired by this reaction process, the Fe SAs/Co CNs were successfully synthesized by combined Co atomic sites with nitrogen-carbon doped Fe single-atom active sites (SAs). The synergy between transition metals not only reduces agglomeration during synthesis but also improves its own electrical conductivity due to the interaction between Fe and Co that promotes the formation of graphite surface. Crucially, the synergistic effect of the Co site significantly enhanced the peroxidase activity of the Fe SAs and the reaction rate of the Fenton-like reaction, resulting in an efficient detection of H2O2. Catalytic kinetic calculations and enzymatic kinetic calculations were used to verify the electron transfer rate and catalytic performance of their constructed electrochemical sensing interfaces. The results showed that Fe SAs/Co CNs@GCE showed better detection performance than Fe SAs CNs@GCE. It was applied successfully to detect H2O2 released from cells in real-time as well. The linear detection range of Fe SAs/Co CNs@GCE for H2O2 was 1-16664 µM, and the detection limit was as low as 0.25 µM. Furthermore, an electrochemical sensing chip was constructed using Fe SAs/Co CNs@SPE and the prepared microfluidic channel. The constructed portable FeSAs/Co CNs@SPE had a linear range of 1-400 µM and a detection limit of 0.36 µM and achieved the recovery detection of H2O2 in serum. The electrochemical sensing interfaces constructed based on Fe SAs/Co CNs all have efficient catalytic performance and excellent real-time hydrogen peroxide detection performance, which have practical application potential for human oxidative stress level detection. And it provides a novel approach to the trace detection of bioactive small molecules.

Mechanism study of how lipid vesicle electroformation is suppressed by the presence of sodium chloride.

Giant lipid vesicles (GLVs) are usually adopted as models of cell membranes and electroformation is the most commonly used method for GLV formation. However, GLV electroformation are known to be suppressed by the presence of salt and the mechanism is not clear so far. In this paper, the lipid hydration and GLV electroformation were investigated as a function of the concentration of sodium chloride by depositing the lipids on the bottom substrates and top substrates. In addition, the electrohydrodynamic force generated by the electroosmotic flow (EOF) on the lipid phase was calculated with COMSOL Multiphysics. It was found that the mechanisms for the failure of GLV electroformation in salt solutions are: 1) the presence of sodium chloride decreases the membrane permeability to aqueous solution by accelerating the formation of well-packed membranes, suppressing the swelling and detachment of the lipid membranes; 2) the presence of sodium chloride decreased the electrohydrodynamic force by increasing the medium conductivity.

Capillarity-based preparation system for optical colorimetric sensor arrays.

In recent years, optical colorimetric sensor arrays have demonstrated beneficial features, including rapid response, high selectivity, and high specificity; as a result, it has been extensively applied in food inspection and chemical studies, among other fields. There are instruments in the current market available for the preparation of an optical colorimetric sensor array, but it lacks the corresponding research of the preparation mechanism. Therefore, in connection with the main features of this kind of sensor array such as consistency, based on the preparation method of contact spotting, combined with a capillary fluid model, Washburn equation, Laplace equation, etc., this paper develops a diffusion model of an optical colorimetric sensor array during its preparation and sets up an optical colorimetric sensor array preparation system based on this diffusion model. Finally, this paper compares and evaluates the sensor arrays prepared by the system and prepared manually in three aspects such as the quality of array point, response of array, and response result, and the results show that the performance index of the sensor array prepared by a system under this diffusion model is better than that of the sensor array of manual spotting, which meets the needs of the experiment.

Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis.

Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.

A novel device based on a fluorescent cross-responsive sensor array for detecting lung cancer related volatile organic compounds.

In this paper, a novel, simple, rapid, and low-cost detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. For this task, a sensor array based on cross-responsive mechanism was designed. A special gas chamber was made to insure sensor array exposed to VOCs sufficiently and evenly, and FLUENT software was used to simulate the performance of the gas chamber. The data collection and processing system was used to detect fluorescent changes of the sensor arrays before and after reaction, and to extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene, and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze data. The results showed that the methods could 100% discriminate the four VOCs. What is more, combined with artificial neural network, the correct rate of quantitative detection was up to 100%, and the device obtained responses at concentrations below 50 ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer.