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.
Innovations in industrial automation, information and communication technology (ICT), renewable energy, monitoring and sensing fields have been paving the way for smart devices, which can acquire and convey information to the internet, in every aspect of our lives. Since there is ever-increasing demand for large yet affordable production volumes for such devices, printed electronics has been attracting great attention in both industrial and academic research. 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.
Nowadays, modern nanomaterial research is complemented by machine learning methods to reduce experimental costs and process time. With this motivation, here, we implemented artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) methods to predict the mechanical properties of threecomponent nanocomposite films consisting of polyvinyl alcohol (PVA) crosslinked 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidized cellulose nanofibers (TOCNFs) and either ammonium zirconium carbonate (AZC) or glyoxal (Gx) using the mechanical properties of mono-component TOCNF films and two-component nanocomposites containing PVA, AZC, or Gx-crosslinked TOCNF as the input of prediction system. Prediction methods were evaluated with performance indicators and experimental data. Overall, MLR performed with least accuracy, whereas ANN prediction displayed the lowest error followed closely by RF. Additionally, the physically or/and chemically crosslinked hybrid films with optimized amount of crosslinkers resulted in structures with a strength to rupture that was significantly higher than that of the pure nanocellulose films (increases of up to~90% in tensile strength and~70% in Young's modulus). POLYM. COM-POS., 40:4013-4022, 2019.
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.
In the present work, cost-effective strain gauges were fabricated by using inkjet printing and photonic curing on flexible and recyclable PET substrates. Ohmic resistance (a.k.a. DC resistance) (R0) and complex electrical impedance (Z) as a function of test frequency were characterized, respectively, with the state-of-theart electronic testing equipments. For the fabrication process, commercially available silver nanoparticle (AgNP) inks and substrates were used. In order to validate the in-house cantilever beam measurement setup and devices, first, commercially available metallic foil strain gauges (with the provided gauge factor GF=2.0 by the manufacturer) were tested at different locations. Thereafter, the printed strain gauges were investigated with several repetitions at different measurement locations. The measurement results demonstrated an affordable, rapid and tailorable design and repeatable fabrication approach for strain gauges with GFavg~6.6, which has potential applications in remote sensing and structural monitoring applications.
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