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In this work, the conjugation of molecularly imprinted polymers (MIPs) to quantum dots (QDs) was successfully applied in the assembly of an imprinted cellulose membrane [hydroxy ethyl cellulose (HEC)/MIP@QDs] for the specific recognition of the cardiac biomarker myoglobin (Myo) as a sensitive, user-friendly, and portable system with the potential for point-of-care (POC) applications. The concept is to use the MIPs as biorecognition elements, previously prepared on the surface of semiconductor cadmium telluride QDs as detection particles. The fluorescent quenching of the membrane occurred with increasing concentrations of Myo, showing linearity in the interval range of 7.39-291.3 pg/mL in a1000-fold diluted human serum. The best membrane showed a linear response below the cutoff values for myocardial infarction (23 ng/mL), a limit of detection of 3.08 pg/mL, and an imprinting factor of 1.65. The incorporation of the biorecognition element MIPs on the cellulose substrate brings an approach toward a portable and user-friendly device in a sustainable manner. Overall, the imprinted membranes display good stability and selectivity toward Myo when compared with the nonimprinted membranes (HEC/NIP@QDs) and have the potential to be applied as a sensitive system for Myo detection in the presence of other proteins. Moreover, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical label-free detection method, reaching concentration levels with clinical significance.

This work focused on the combination of CdTe and AgInS 2 quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS 2 combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R 2 P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)