Two reviews from the Biomedical Applications Group at the Institute of Microelectronics of Barcelona, CSIC, summarise the latest efforts in organ-on-chip technologies to emulate in vitro microfluidic systems. These devices are an opportunity to evolve the fields of biofabrication and sensing technology.

The Review "Electrochemical Paper-Based Biosensor Devices for Rapid Detection of Biomarkers", signed by three Chemical Transducers Group members, published in the journal Sensors last year has been selected as Editor's Choice Article as noteworthy or likely to be of high interest to readers.

The study, published in Instrumentation Science & Technology, offers a low-cost analytical system with disposable sensors to follow the microbial metabolic activity.

New paper published in the journal IEEE Transactions on Industrial Electronics by the Power Devices and Systems Group (PDS).

The Advanced Thin Dielectric Films Group (ATDF), led by Francesca Campabadal, has just published the last paper of the year in Applied Physics Letters. "Current transient response and role of the internal resistance in HfOx-based memristors" is the last publication of an IMB-CNM group in 2020.

"Graphene active sensor arrays for long-term and wireless mapping of wide frequency band epicortical brain activity" is the new paper published in Nature Communications. It has the contribution of three Biomedical Applications Group (GAB) members.

Científicos del Instituto de Microelectrónica de Barcelona (IMB-CNM), y de la Universidad de Bath crean e introducen estos dispositivos en óvulos. El chip funciona como sensor mecánico y es extremadamente minúsculo: tiene una longitud tres veces más pequeña que el diámetro de un cabello humano y un grosor tres veces menor que el de un virus como el SARS-CoV-2.  El trabajo, publicado en Nature Materials, mide las fuerzas que reorganizan el interior del óvulo desde que se introduce el espermatozoide hasta que el embrión se divide en dos células.

Esta nueva tecnología podría facilitar la llegada de una nueva generación de interfaces cerebro-ordenador