Flexible Label-Free Platinum and Bio-PET-Based Immunosensor for the Detection of SARS-CoV-2

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.

High versatility of polyethylene terephthalate (PET) waste for the development of batteries, biosensing and gas sensing devices

Continuously growing adoption of electronic devices in energy storage, human health and environmental monitoring systems increases demand for cost-effective, lightweight, comfortable, and highly efficient functional structures. In this regard, the recycling and reuse of polyethylene terephthalate (PET) waste in the aforementioned fields due to its excellent mechanical properties and chemical resistance is an effective solution to reduce plastic waste. Herein, we review recent advances in synthesis procedures and research studies on the integration of PET into energy storage (Li-ion batteries) and the detection of gaseous and biological species. The operating principles of such systems are described and the role of recycled PET for various types of architectures is discussed. Modifying the composition, crystallinity, surface porosity, and polar surface functional groups of PET are important factors for tuning its features as the active or substrate material in biological and gas sensors. The findings indicate that conceptually new pathways to the study are opened up for the effective application of recycled PET in the design of Li-ion batteries, as well as biochemical and catalytic detection systems. The current challenges in these fields are also presented with perspectives on the opportunities that may enable a circular economy in PET use.

Reusable graphite-based electrochemical sensors for L-dopa and dopamine detection

A fully reusable electrochemical device is proposed for the first time made from laser cutting and a homemade conductive ink composed of carbon and nail polish. As a sensor substrate, we applied polymethyl methacrylate, which allows the surface to be renewed by simply removing and reapplying a new layer of ink. In addition to the ease of renewing the sensor's conductive surface, the design of the device has allowed for the integration of different forms of analysis. The determination of L-Dopa was performed using DPV, which presented a linear response range between 5.0 and 1000.0 μmol L-1, and a LOD of 0.11 μmol L-1. For dopamine, a flow injection analysis system was employed, and using the amperometric technique measurements were performed with a linear ranging from 2.0 to 100.0 μmol L-1 and a LOD of 0.26 μmol L-1. To demonstrate its applicability, the device was used in the quantification of analytes in pharmaceutical drug and synthetic urine samples.