Biosensors based on graphene field-effect transistors have become a promising tool for detecting a broad range of analytes. However, they lack the stability and reproducibility required to step into biotechnological and biomedical applications. In this work, we use a controlled in-vacuum physical method for the covalent functionalization of graphene to construct ultrasensitive aptamer-based biosensors (aptasensors) able to detect hepatitis C virus core protein. These devices are highly specific and robust, achieving attomolar detection of the viral protein target in human blood plasma. The improved sensitivity is rationalized by theoretical calculations showing that induced polarization at the graphene interface caused by the proximity of covalently bound probe molecule modulates the charge balance at the graphene/molecule interface. This charge balance causes a net shift of the Dirac cone providing enhanced sensitivity towards the attomolar detection of proteins. Such an unexpected effect paves the way for using this kind of graphene-based platform for real-time diagnostics of different diseases.

Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins and structured nucleic acids, are flexible molecules that can fold into alternative conformations for any given monomer sequence, as exemplified by the different three-dimensional structures adopted by RNA in solution. Therefore, the manual analysis of images visualized by AFM and other microscopy techniques becomes very laborious and time-consuming (and may also be inadvertently biased) when large populations of biomolecules are studied. Here we present a novel morphology clustering software, based on particle isolation and artificial neural networks, which allows the automatic image analysis and classification of biomolecules that can show alternative conformations. It has been tested with a set of AFM images of RNA molecules (a 574 nucleotides-long functinal region of the hepatitis C virus genome that contains its internal ribosome entry site element) structured in folding buffers containing 0, 2, 4, 6 or 10 mM Mg 2+. The developed software shows a broad applicability in the microscopy-based analysis of biopolymers and other complex biomolecules. INDEX TERMS Artificial neural networks, atomic force microscopy (AFM), biomolecules, growing cell structures (GCS), hepatitis C virus (HCV), Image analysis, internal ribosome entry site (IRES), ribonucleic acid (RNA), self-organizing maps (SOM).

An open‐and‐shut case: Cucurbit[8]uril adsorbs robustly on transition‐metal dichalcogenides (TMDs) letting the cavity open for complex formation with melatonin and allowing efficient electrochemical sensing. This situation is reversed on graphene where the Cucurbit[8]uril's portals are closed, hindering the host–guest recognition. More information can be found in the Research Article by C. Quintana, J. A. Martín‐Gago, and co‐workers. (DOI: 10.1002/chem.202203244).

Invited for the cover of this issue are two collaborating groups: one at the Universidad Autónoma de Madrid and the other at the Instituto de Ciencia de Materiales de Madrid. The image depicts Cucurbit[8]uril adsorbed on a transition metal dichalcogenide surface letting the cavity open for complex formation with melatonin and allowing efficient electrochemical sensing. Read the full text of the article at 10.1002/chem.202203244.