Machine learning-assisted Te-CdS@Mn3O4 nano-enzyme induced self-enhanced molecularly imprinted ratiometric electrochemiluminescence sensor with smartphone for portable and visual monitoring of 2,4-D.

Here, a novel and portable machine learning-assisted smartphone-based visual molecularly imprinted ratiometric electrochemiluminescence (MIRECL) sensing platform was constructed for highly selective sensitive detection of 2,4-Dichlorophenoxyacetic acid (2,4-D) for the first time. Te doped CdS-coated Mn3O4 (Te-CdS@Mn3O4) with catalase-like activity served as cathode-emitter, while luminol as anode luminophore accompanied H2O2 as co-reactant, and Te-CdS@Mn3O4 decorated molecularly imprinted polymers (MIPs) as recognition unit, respectively. Molecular models were constructed and MIP band and binding energies were calculated to elucidate the luminescence mechanism and select the best functional monomers. The peroxidase activity and the large specific surface area of Mn3O4 and the electrochemical effect can significantly improve the ECL intensity and analytical sensitivity of Te-CdS@Mn3O4. 2,4-D-MIPs were fabricated by in-situ electrochemical polymerization, and the rebinding of 2,4-D inhibits the binding of H2O2 to the anode emitter, and with the increase of the cathode impedance, the ECL response of Te-CdS@Mn3O4 decreases significantly. However, the blocked reaction of luminol on the anode surface also reduces the ECL response. Thus, a double-reduced MIRECL sensing system was designed and exhibited remarkable performance in sensitivity and selectivity due to the specific recognition of MIPs and the inherent ratio correction effect. Wider linear range in the range of 1 nM-100 µM with a detection limit of 0.63 nM for 2,4-D detection. Interestingly, a portable and visual smartphone-based MIRECL analysis system was established based on the capture of luminescence images by smartphones, classification and recognition by convolutional neural networks, and color analysis by self-developed software. Therefore, the developed MIRECL sensor is suitable for integration with portable devices for intelligent, convenient, and fast detection of 2,4-D in real samples.

Deep learning-assisted smartphone-based molecularly imprinted electrochemiluminescence detection sensing platform: Protable device and visual monitoring furosemide.

A novel, portable, and smartphone-based molecularly imprinted polymer electrochemiluminescence (MIP-ECL) sensing platform was constructed for sensitive and selective determination of furosemide (FSM). In this platform, MoSe2 nanoparticles/starch-derived biomass carbon (MoSe2/BC) nanocomposites as imprinted material, lucigenin (Luc) as the energy donor, CdS quantum dots (CdS QDs) were used as the luminophore (energy acceptor), and molecularly imprinted polymer (MIP) as the specificity recognition element to construct a MIP-ECL sensing system based on electroluminescence resonance energy transfer (ECL-RET) mechanism, which enhanced the sensitivity and the specificity of this system. Imprinted materials were characterized by SEM, TEM, XRD, FT-IR, etc. and the recognition performance of MIP was characterized using CV, EIS, and ECL methods. The elution and re-sorption of template molecules can be used as a switch to control ECL based on the signal that can be quenched by FSM. Interestingly, deep learning based on convolutional neural networks realizes batch processing of ECL signals. Additionally, this developed MIP-ECL method was established by using the traditional ECL analyzer detector for the assay of FSM with a detection limit of 4 nM in the range of 0.010 µM-100 µM. Besides, the consumer smartphone sensing platform based on deep learning showed an outstanding linear response between the R-value of the picture and the concentration of furosemide in the range of 1-70 µM with a detection limit of 0.25 µΜ, which is much lower than that the reported for other detection methods. More importantly, due to the transferability of deep learning, the smartphone-based MIP-ECL systems can facilitate the real-time monitoring of biochemical analytes in multiple fields.