Bipolar silica nanochannel array confined electrochemiluminescence for ultrasensitive detection of SARS-CoV-2 antibody.

Ultrasensitive, specific, and early identification of Coronavirus Disease (2019) (COVID-19) infection is critical to control virus spread and remains a global public health problem. Herein, we present a novel solid-state electrochemiluminescence (ECL) platform targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody with rapidity and ultrahigh sensitivity, in which a bipolar silica nanochannel array (bp-SNA) is fabricated on indium tin oxide (ITO) electrode for the first time to stably confine the ECL probe of tris(2,2'-bipyridyl) ruthenium (Ru(bpy)32+) under dual electrostatic force. The bp-SNA consists of tightly packed bilayer silica nanochannel array (SNA) with asymmetric surface charges, namely an inner negatively charged SNA (n-SNA) and an outer positively charged SNA (p-SNA), serving as an "electrostatic lock" to enrich and stabilize the cationic Ru(bpy)32+ probe without leakage from the electrode surface. The detection of SARS-CoV-2 IgG antibody could be realized via immobilization of SARS-CoV-2 spike protein on the utmost of Ru(bpy)32+-confined solid-state ECL platform (Ru@bp-SNA). Upon the capture of target SARS-CoV-2 IgG by immune recognition, the formed immunocomplex will block the nanochannel, leading to the hindered diffusion of the co-reactant (tri-n-propylamine, TPrA) and further producing a decreased ECL signal. The developed solid-stated ECL immunosensor is able to determine SARS-CoV-2 IgG with a wide linear range (5 pg mL-1 to 1 µg mL-1), a low limit-of-detection (2.9 pg mL-1), and a short incubation time (30 min). Furthermore, accurate analysis of SARS-CoV-2 IgG in real serum samples is also obtained by the sensor.

An integrated multi-criteria decision making approach with linguistic hesitant fuzzy sets for E-learning website evaluation and selection.

Network teaching has been widely developed under the influence of COVID-19 pandemic to guarantee the implementation of teaching plans and protect the learning rights of students. Selecting a particular website for network teaching can directly affects end users' performance and promote network teaching quality. Normally, e-learning website selection can be considered as a complex multi-criteria decision making (MCDM) problem, and experts' evaluations over the performance of e-learning websites are often imprecise and fuzzy due to the subjective nature of human thinking. In this article, we propose a new integrated MCDM approach on the basis of linguistic hesitant fuzzy sets (LHFSs) and the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method to evaluate and select the best e-learning website for network teaching. This introduced method deals with the linguistic assessments of experts based on the LHFSs, determines the weights of evaluation criteria with the best-worst method (BWM), and acquires the ranking of e-learning websites utilizing an extended TODIM method. The applicability and superiority of the presented linguistic hesitant fuzzy TODIM (LHF-TODIM) approach are demonstrated through a realistic e-learning website selection example. Results show that the LHF-TODIM model being proposed is more practical and effective for solving the e-learning website selection problem under vague and uncertain linguistic environment.