IEEE Transactions on NanoBioscience最新文献

Human sperm functioning is crucial for maintaining natural reproduction, but its sterility is enhanced by variations in environmental conditions. Because of these agitating properties, powerful computer-aided devices are required, but their precision is inadequate, particularly when it comes to samples with low sperm concentrations. Therefore, for the first time, this article introduces the sulfide material-based structure for the detection of human sperm samples using the prism-based surface plasmon resonance sensor (SPR) Nano-biosensor. The proposed structure is designed on the basis of a prism-based Kretschmann configuration and includes silver, silicon, a sulfide layer, black phosphorus, and a sensing medium. This work takes advantage of the excitement of surface plasmons and evanescent waves in the metal dielectric region. For the detection process, seven sperm samples are taken, with their concentration, mobility, and refractive index measured by the refractometer. The proposed structure provides a maximum sensitivity of 409.17°/RIU, QF of 97.45RIU^-1 and a DA of 1.37. The results provide a substantial improvement in comparison to the reported work in the literature.

The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.

Here, based on the characteristics of Graphene oxide(GO) and SYBR Green I(SGI) dye, an enzyme-free and label-free fluorescent biosensor with signal amplification through DNA strand reaction is proposed for the detection of Aflatoxin B1(AFB1) in food safety. Firstly, without the addition of AFB1, the substrate in the system includes a double stranded Apt-S with a long sticky end and two hairpins H1 and H2. Although the complementary pairing of bases may exhibit fluorescence due to the insertion of SGI dyes, the use of GO, which is highly capable of adsorbing single stranded parts and quenching fluorescence, cleverly reduces the background fluorescence. Adding the target AFB1 triggers DNA inter chain reactions, generating a large amount of long double stranded DNA H1-H2, thereby generating strong fluorescence signals under the action of SGI. More importantly, logical theory verification and computer simulation were conducted before biological experiments, providing a theoretical basis for the implementation of the biosensor. After analysis, the fluorescence biosensor exhibits a good linear relationship with AFB1 concentration in the range of 5-50nM, with a detection limit of 0.76nM. It also has good specificity, anti-interference ability, and practical application ability, and has broad application prospects in the field of food safety.