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The local surface plasmon resonance (LSPR) effect has been widely used in various nanophotonic applications. However, because the LSPR effect is highly sensitive to the structure and geometry, it is desirable to efficiently search viable geometries for predefined local field enhancement spectrum. Herein we present a generative adversarial network-based LSPR nanoantenna design scheme. By encoding the antenna structure information into an red-green-blue (RGB) color image, the corresponding nanoantenna structure can be inverse-designed to achieve the required enhancement spectrum of the local field. The proposed scheme can accurately offer the multiple geometry layout for the customized specific spectrum in seconds, which could be beneficial for fast design and fabrication of plasmonic nanoantenna.
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In the background of the rapid development of artificial intelligence, big data, IoT, 5G/6G, and other technologies, electrochemical sensors pose higher requirements for high-throughput detection. In this study, we developed a workstation with up to 10 channels, which supports both parallel signal stimulation and online electrochemical analysis functions. The platform was wired to a highly integrated Bluetooth chip used for wireless data transmission and can be visualized on a smartphone. We used this electrochemical test platform with carbon-graphene oxide/screen-printed carbon electrodes (CB-GO/SPCE) for the online analysis of L-tyrosine (Tyr), and the electrochemical performance and stability of the electrodes were examined by differential pulse voltammetry (DPV). The CB-GO-based screen-printed array electrodes with a multichannel electrochemical platform for Tyr detection showed a low detection limit (20 µM), good interference immunity, and 10-day stability in the range of 20-200 µM. This convenient electrochemical analytical device enables high-throughput detection and has good economic benefits that can contribute to the improvement of the accuracy of electrochemical analysis and the popularization of electrochemical detection methods in a wide range of fields.
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To cope with the easy transmissibility of the avian influenza A virus subtype H1N1, a biosensor was developed for rapid and highly sensitive electrochemical immunoassay. Based on the principle of specific binding between antibody and virus molecules, the active molecule-antibody-adapter structure was formed on the surface of an Au NP substrate electrode; it included a highly specific surface area and good electrochemical activity for selective amplification detection of the H1N1 virus. The electrochemical test results showed that the BSA/H1N1 Ab/Glu/Cys/Au NPs/CP electrode was used for the electrochemical detection of the H1N1 virus with a sensitivity of 92.1 µA (pg/mL)-1 cm2, LOD of 0.25 pg/ml, linear ranges of 0.25-5 pg/mL, and linearity of (R 2 = 0.9846). A convenient H1N1 antibody-based electrochemical electrode for the molecular detection of the H1N1 virus will be of great use in the field of epidemic prevention and raw poultry protection. Supplementary Information: The online version contains supplementary material available at 10.1007/s11581-023-04944-w.
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The rapid development of industrial technologies continuously increases the heavy metal pollution of water resources. Recently, portable electrochemical analysis-based devices for detecting heavy metal ions have attracted much attention due to their excellent performance and low fabrication costs. However, it has proven difficult to accommodate complex testing needs in a cost-effective manner. To address these limitations, we propose a new system for the in situ detection of heavy metals in wastewater using an organic light-emitting diode-based panel to display data in real time and Bluetooth to transmit data to a smartphone for rapid analysis. The fabricated device integrates an in situ signal analysis circuit, a Bluetooth chip, a photocured 3D-printed shell, and an electrode sleeve interface. In addition, a fully screen-printed functional electrode plate containing chitosan/PANi-Bi nanoparticle@graphene oxide multi-walled carbon nanotubes is utilized for the rapid detection of heavy metal ions. This device can perform wireless data transmission and analysis and in situ signal acquisition and processing. The sensor exhibits a high sensitivity (Hg2+: 88.34 µA ppm-1 cm-2; Cu2+: 0.956 µA ppm-1 cm-2), low limit of detection (Hg2+: 10 ppb, Cu2+: 0.998 ppm) and high selectivity during the detection of copper and mercury ions in tap water under non-laboratory conditions, and the results of real-time tests reveal that parameters measured in the field and laboratory environments are identical. Hence, this small, portable, electrochemical sensor with a screen-printed electrode can be effectively used for the real-time detection of copper and mercury ions in complex water environments.
Assuntos
Metais Pesados , Nanotubos de Carbono , Eletrodos , Íons , EsgotosRESUMO
A resistive memory device based on the Ag/Bi1+δ(Fe0.95Zn0.05)O3/SRO/Pt/TiO2/SiO2/Si(100) structure was prepared using radio frequency magnetron sputtering. The composition of the thin film element was analyzed by X-ray photoelectron spectroscopy and the thickness of the thin film was characterized by scanning electron microscope. Through the electrical test, we found that the device exhibited low operating voltage, which included VSET of about 0.1 V, VRESET of about -0.1 V, and VF of about 0.25 V. This facilitated the perfect integration of the device with the circuit design. Testing for 10,000 s at a substrate temperature of 85 °C, the device showed excellent retention. The I-V fitting curves of the resistive devices were analyzed. The low resistance state was in line with the ohmic mechanism and the high resistance state was in accordance with the Space Charge Limited Current mechanism. The resistance change of the device was attributed to the formation of Ag conductive filaments.
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It is essential and of great significance to impart high mechanical performance, environmental stability, and high sensitivity to emerging flexible temperature sensors. In this work, polymerizable deep eutectic solvents are designed and prepared by simply mixing N-cyanomethyl acrylamide (NCMA) containing an amide group and a cyano group in the same side chain with lithium bis(trifluoromethane) sulfonimide (LiTFSI), and obtain supramolecular deep eutectic polyNCMA/LiTFSI gels after polymerization. These supramolecular gels exhibit excellent mechanical performance (tensile strength of 12.9 MPa and fracture energy of 45.3 kJ m-2 ), strong adhesion force, high-temperature responsiveness, self-healing ability, and shape memory behavior due to the reversible reconstruction ability of amide hydrogen bonds and cyano-cyano dipole-dipole interactions in the gel network. In addition, the gels also demonstrate good environmental stability and 3D printability. To verify its application potential as a flexible temperature sensor, the polyNCMA/LiTFSI gel-based wireless temperature monitor is developed and displays outstanding thermal sensitivity (8.4%/K) over a wide detection range. The preliminary result also suggests the promising potential of PNCMA gel as a pressure sensor.
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Early diagnosis of pathological markers can significantly shorten the rate of viral transmission, reduce the probability of infection, and improve the cure rate of diseases. Therefore, analytical techniques for identifying pathological markers and environmental toxicants have received considerable attention from researchers worldwide. However, the most popular techniques used in clinical settings involve expensive precision instruments and complex detection processes. Thus, a simpler, more efficient, rapid, and intelligent means of analysis must be urgently developed. Electrochemical biosensors have the advantages of simple processing, low cost, low sample preparation requirements, rapid analysis, easy miniaturization, and integration. Thus, they have become popular in extensive research. Machine learning is widely used in material-assisted synthesis, sensor design, and other fields owing to its powerful data analysis and simulation learning capabilities. In this study, a machine learning-assisted carbon black-graphene oxide conjugate polymer (CB-GO/CP) electrode, in conjunction with a flexible wearable device, is proposed for the smart portable detection of tyrosine (Tyr). Input feature value data are obtained for the artificial neural network (ANN) and support vector machines (SVM) model learning via multiple data collections in artificial urine and by recording the pH and temperature values. The results reveal that a machine-learning model that integrates multiple external factors is more accurate for the prediction of Tyr concentration.
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Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 µg mL-1 for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.
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Análise Espectral Raman , Vinho , Benzimidazóis , Máquina de Vetores de Suporte , Vinho/análiseRESUMO
In this paper, bovine serum albumin (BSA)-imprinted calcium alginate (CaAlg) hydrogel membrane was prepared using BSA as a template, sodium alginate (NaAlg) as a functional monomer, and CaCl2 as a cross-linker. The thickness of the CaAlg membrane was controlled by a glass rod enlaced with brass wires (the diameter was 0.1, 0.2, 0.3, 0.4, and 0.5 mm). The swelling properties of the CaAlg membranes prepared with different contents of NaAlg were researched. Circular dichroism indicated that the conformation of BSA did not change during the preparing and eluting process. The thinner the CaAlg hydrogel membrane was, the larger the adsorption capacity and the higher the imprinting efficiency of the CaAlg. The maximum adsorption capacity of molecularly imprinted polymer (MIP) and non-imprinted CaAlg hydrogel membrane (NIP) was 38.6 mg·g-1 and 9.2 mg·g-1, respectively, with an imprinting efficiency of 4.2. The MIP was loaded on the electrode to monitor the selective adsorption of BSA by voltammetry curve.