Innovations in industrial automation, information and communication technology (ICT), renewable energy as well as monitoring and sensing fields have been paving the way for smart devices, which can acquire and convey information to the Internet. Since there is an ever-increasing demand for large yet affordable production volumes for such devices, printed electronics has been attracting attention of both industry and academia. In order to understand the potential and future prospects of the printed electronics, the present paper summarizes the basic principles and conventional approaches while providing the recent progresses in the fabrication and material technologies, applications and environmental impacts.

At-home rapid antigen test (RAT) kits for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are valuable public health tools during the present coronavirus disease (COVID-19) pandemic. They provide fast identification of coronavirus infection, which can help to reduce the transmission rates and burden on the healthcare system. However, they have lower sensitivity compared to the reverse transcription polymerase chain reaction (RT-PCR) tests. One of the reasons for the lower sensitivity is due to the RAT color indicators being indistinct or invisible to the naked eye after the measurements. For this reason, we present a proof of concept of a novel approach, through which we investigated anonymously provided at-home RAT kit results by using our in-house open-source image processing scripts developed for affordable Raspberry Pi computer and Raspberry Pi HQ camera systems. Therefore, we aimed at minimizing the human-related analysis errors for such kits and believe that the present computer vision-based assessment framework can contribute to reducing delayed quarantines of infected individuals and the spread of the current infectious disease.

The continually growing demand for electronic devices and consequently the increase in electronic waste have caused the need for more environmentally friendly materials and methods used when manufacturing the devices. In this paper, a coplanar waveguide-fed square slot antenna was aerosol jet printed onto acetylated cellulose nanofiber (ACNF) substrates and a poly(ethylene terephthalate) (PET) reference substrate, and the effects of photodegradation on these were studied. The functionality and print quality of the antennas were examined before and after photodegradation. In addition, changes in the color of the substrate and antenna were analyzed during the photodegradation process. Interestingly, the color of the ACNF substrate became whiter after photodegradation, while there were no significant changes in the color of the PET substrate, and the printed antennas became notably darker. The printed dimensions were quite accurate compared to the planned dimensions; there were, however, small differences in the horizontal and vertical directions. Additionally, the printed pattern on the PET substrate showed significantly more gaps in the pattern compared to the printed pattern on the ACNF substrate. The electrical properties of the antenna on the ACNF substrate were almost as good as those of the PET substrate. The photodegradation treatment significantly deteriorated the electrical properties of the antenna on the PET substrate, whereas the antenna on the ACNF substrate did not show any notable changes. Hence, this study shows that ACNF would be a suitable substitute for plastic as a substrate for printed electronics.

At-home rapid antigen test (RAT) kits for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are valuable public health tools during the present coronavirus disease (COVID-19) pandemic. They provide fast identification of coronavirus infection, which can help to reduce the transmission rates and burden on the healthcare system. However, they have lower sensitivity when compared with the reverse transcription polymerase chain reaction (RT-PCR) tests. One of the reasons for the lower sensitivity is due to the RAT color indicators being indistinct or invisible to the naked eye after the measurements. For this reason, we propose a systematic approach, through which we investigated anonymously provided at-home RAT kit results by using our in-house open source image processing scripts developed for affordable Raspberry Pi computer and Raspberry Pi HQ camera systems (available at https://github.com/kmiikki/ratcv). Therefore, we aimed at minimizing the human-related analysis errors for such kits. We believe that our framework can contribute to reduced the delayed quarantines of infected individuals and spreading of the current infectious disease.