The COVID-19 pandemic has posed enormous challenges for existing diagnostic tools to detect and monitor pathogens. Therefore, there is a need to develop point-of-care (POC) devices to perform fast, accurate, and accessible diagnostic methods to detect infections and monitor immune responses. Devices most amenable to miniaturization and suitable for POC applications are biosensors based on electrochemical detection. We have developed an impedimetric immunosensor based on an interdigitated microelectrode array (IMA) to detect and monitor SARS-CoV-2 antibodies in human serum. Conjugation chemistry was applied to functionalize and covalently immobilize the spike protein (S-protein) of SARS-CoV-2 on the surface of the IMA to serve as the recognition layer and specifically bind anti-spike antibodies. Antibodies bound to the S-proteins in the recognition layer result in an increase in capacitance and a consequent change in the impedance of the system. The impedimetric immunosensor is label-free and uses non-Faradaic impedance with low nonperturbing AC voltage for detection. The sensitivity of a capacitive immunosensor can be enhanced by simply tuning the ionic strength of the sample solution. The device exhibits an LOD of 0.4 BAU/ml, as determined from the standard curve using WHO IS for anti-SARS-CoV-2 immunoglobulins; this LOD is similar to the corresponding LODs reported for all validated and established commercial assays, which range from 0.41 to 4.81 BAU/ml. The proof-of-concept biosensor has been demonstrated to detect anti-spike antibodies in sera from patients infected with COVID-19 within 1 h.

Smart-agriculture technologies comprise a set of management systems designed to sustainably increase the efficiency and productivity of farming. In this paper, we present a lab-on-a-chip device that can be employed...

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In this study, we present a microdevice for the capture and quantification of Sclerotinia sclerotiorum spores, pathogenic agents of one of the most harmful infectious diseases of crops, Sclerotinia stem rot. The early prognosis of an outbreak is critical to avoid severe economic losses and can be achieved by the detection of a small number of airborne spores. However, the current lack of simple and effective methods to quantify fungal airborne pathogens has hindered the development of an accurate early warning system. We developed a device that remedies these limitations based on a microfluidic design that contains a nanothick aluminum electrode structure integrated with a picoliter well array for dielectrophoresis-driven capture of spores and on-chip quantitative detection employing impedimetric sensing. Based on experimental results, we demonstrated a highly efficient spore trapping rate of more than 90% with an effective impedimetric sensing method that allowed the spore quantification of each column in the array and achieved a sensitivity of 2%/spore at 5 kHz and 1.6%/spore at 20 kHz, enabling single spore detection. We envision that our device will contribute to the development of a low-cost microfluidic platform that could be integrated into an infectious plant disease forecasting tool for crop protection.

Current approaches in targeted patient treatments often require the rapid isolation of specific rare target cells. Stream-based dielectrophoresis (DEP) based cell sorters have the limitation that the maximum number of sortable cell types is equivalent to the number of output channels, which makes upscaling to a higher number of different cell types technically challenging. Here, we present a microfluidic platform for selective single-cell sorting that bypasses this limitation. The platform consists of 10 000 nanoliter wells which are placed on top of interdigitated electrodes (IDEs) that facilitate dielectrophoresis-driven capture of cells. By use of a multisectorial design formed by 10 individually addressable IDE structures, our platform can capture a large number of different cell types. The sectorial approach allows for fast and straightforward modification to sort complex samples as different cell types are captured in different sectors and therefore removes the need for individual output channels per cell type. Experimental results obtained with a mixed sample of benign (MCF-10A) and malignant (MDA-MB-231) breast cells showed a target to nontarget sorting accuracy of over 95%. We envision that the high accuracy of our platform, in addition to its versatility and simplicity, will aid clinical environments where reliable sorting of varying complex samples is essential.