Microchimica Acta最新文献

African swine fever virus (ASFV) is a highly infectious and pathogenic DNA virus with a mortality rate of nearly 100%. Here, we developed a flexible biosensor based on a AgNWs/hydrogel/PS(polystyrene)/Fe₃O₄ composite film for the detection of ASFV P72 protein gene fragments. A large number of carboxyl groups in the hydrogel provide modification sites for capture probes (cp) simplifying the preparation process of the biosensor. Combined with the excellent magnetic properties of Fe₃O₄, the excellent electrical conductivity of silver nanowires (AgNWs) and the unique optical properties of two-dimensional photonic crystals (2DPC), the flexible biosensor can convert biological signals into optical signals and electrical signals, and the micro-deformation of the film can be verified by simple optical methods. In order to improve the sensitivity, we introduce Fe₃O₄ and apply an external magnetic field to amplify the thin film micro-deformation. The concentration of P72 protein gene fragment and the relative change rate of resistance showed a good linear relationship, and the linear equation is y = − 0.00126x − 0.31729; the detection limit (LOD) is as low as 0.208 μM. The composite film was used to detect real serum samples, and the recovery of the composite film fluctuated in the range 91.89 to 103.19%, indicating that the composite film has practical application potential in clinical detection of ASFV. In addition, the biosensor also shows good biocompatibility, stability, and specificity.

Advances in micro-nano fabrication technology have enabled flexible electrochemical sensors to utilize micro-nanostructures and nanomaterials. Herein, a 3D AZO@ZnONRs core–shell nanostructure was synthesized using atomic layer deposition and hydrothermal techniques. The structure was employed to fabricate a high-sensitivity glucose sensor capable of precise detection of blood glucose levels and glucose content in sugary beverages. The sensor demonstrated a highly linear response (0–12.5 mM), with a sensitivity of approximately 6.49 µA·mM⁻¹·cm⁻² and a detection limit of 1.561 µM. Under ultraviolet light, the sensitivity increased by 1.83-fold. In the presence of interferents such as potassium chloride, sodium chloride, lactic acid, urea, and uric acid, the sensor maintained excellent specificity. Compared to conventional nanorods, this 3D core–shell material preserved the advantages of a high specific surface area while demonstrating enhanced electron transfer capabilities and photosensitivity, enabling reliable detection of glucose at extremely low concentrations. This study systematically analyzed the characteristics of the core–shell nanomaterial and its photocatalytic mechanisms, advancing photocatalytic electrochemical sensing technology.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in a dynamic concentration range. Traditionally, the saturation of sensor response limits accurate prediction at high analyte concentrations, although this is seldom discussed in the literature. To address this research gap, we thematically report physics-informed analytical treatments with machine learning (PIML) on a paper-based electrochemical biosensor, taking advantage of low cost, flexibility, low sample volume, and ease of deployment. Owing to the well-known biosensing performances, ZnO nanoflowers, synthesized in-house with a hydrothermal procedure, are utilized for transduction purposes with three voltametric techniques: CV, DPV, and SWV. The exceptional surface coverage and high IEP of ZnO have contributed towards the realization of high sensitivity, and the monoclonal antibody-based bioreceptors ensured the enormous selectivity of the platform. Nevertheless, the traditional calibration approach for CV considers the peak current as the sensor parameter, which gets flattened at higher concentrations, thereby limiting reliability. Therefore, this issue is addressed by strategic analytical development by extracting the charge associated with a CV scan and employing this physics-informed feature in the machine learning (ML) model. Combining features generated from different electrochemical techniques in the ML model enhances data diversity by including comprehensive information. This unique approach towards data analysis led to achieving 100% accuracy and AUC scores for identifying cardiac troponin-I and T multiclass concentrations. We strongly believe that the proposed methodologies have a substantial potential for translation to any other related sensor applications.