ABSTRACT
Rapid development of flexible pressure sensors is indispensable in electronic skin to have the sensing capability to static and dynamic pressures. Besides high sensitivity and low hysteresis, the high flexibility and stability of these sensors are of paramount importance owing to the application requirement of conformable pressure mapping and rugged structure. Here, we describe a novel approach for highly flexible capacitive pressure sensors with engineered stable interfaces employing PDMS-based substrates and a micropyramidal dielectric layer, Au electrodes, and molecular adhesive. The sensor/matrix stack consists of five interfaces with strong interfacial adhesion achieved using MPTMS molecular adhesive and a partially cured PDMS lamination layer. A highly flexible capacitive pressure sensor capable of a wide pressure sensing range (up to 550 kPa) is developed with a high sensitivity (46.6 MPa-1 in ≤1 kPa), capability to sense pressure as low as 27 Pa, low hysteresis (4.05%), and high stability for large pressures (11,400 cycles @ 250 kPa). The sensor is successfully demonstrated for arterial pulse signal acquisition and performing a press task when attached on the forefinger. A flexible pressure sensor matrix of 4 × 4 pixels is developed. It can be flexed or crumpled; hence, it is conformably attached on a planar surface and a non-planar 3D-printed surface for single-point and multipoint pressure sensing. The sensor exhibited a maximum shear strain of 2.27 N before breakage. These highly flexible pressure sensor and matrix are also compared with a semi-flexible IO-PET electrode-based pressure sensor and matrix to clearly bring out the flexibility and stability advantages. The proposed process is simple and scalable and offers a conformably stable pressure sensor matrix for electronic skin development.
ABSTRACT
Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.
