The present study highlights a sensing approach for opiates using acyclic cucurbituril (aCBs) sensors comprising four glycouril units terminated on both ends with naphthalene fluorophore walls. The connectivity between the glycourils and naphthalene rings largely defines the opening size of the cucurbituril cavity and its diameter. The large hydrophobic binding cavity is flexible and is able to adapt to guests of various size and topology. The recognition event between the aCBs and guests results in modification of the fluorescence of the terminal walls, a fluorescence response that can be used to sense the drugs of abuse morphine, heroin, and oxycodone as well as their metabolites. Molecular dynamics is employed to understand the nature of the binding interactions. A simple three sensor cross-reactive array enables the determination of drugs and their metabolites in water with high fidelity and low error. Quantitative experiments performed in urine using a new three-way calibration model allows for determination of drugs and their metabolites using one sensor from a single fluorescence reading.

Expanded calixpyrrole-type macrocycles, calix[2]benzo[4]pyrroles, bearing fluorescent moieties attached via conjugated vinyl spacers, have been synthesized from the corresponding formyl derivatives through Knoevenagel condensation. The anion-binding properties of the resulting fluorescent macrocycles have been studied by means of NMR, UV/Vis, and fluorescence spectroscopies. Our main focus has been on dicarboxylates matching the size of the binding cavity of the calix[2]benzo[4]pyrrole skeleton. The observed anion-binding properties were compared with those of the regular calix[4]pyrroles bearing identical fluorophores. Surprisingly, the parent calix[4]pyrroles appear to be equally efficient, if not more so, for sensing anions such as dicarboxylates. Affinity constants determined for various anions and dianions show the sensors S1-S5 to be highly cross-reactive. The cross-reactivity of the sensors was utilized in a microchip-based array, which showed perfect (100 %) classification of 18 analytes utilizing only five sensors. Finally, the same array was used to quantitatively analyze dicarboxylates such as oxalate and malonate. The data from the array were subjected to linear regression, allowing the determination of various concentrations of dianions with low error (<2 %).

Accurate monitoring of sugar levels is essential for many fields from food industry to human health. Here, we developed FRET-based dual chromophore sensors for saccharides that form oxazolidine boronate and may be employed as a noninvasive method for monitoring of sugar levels in biological fluids, namely, urine. The saccharide-binding properties of the sensors were studied using fluorescence spectroscopy and utilized in the determination of saccharides in a high-throughput manner.Here, two fluorescent sensors were successful in the classification of nine different monosaccharides and disaccharides with 100% correct classification. Furthermore, the dual chromophore self-assembled sensors were successfully utilized for the quantitative determination of important carbohydrates such as glucose in the presence of competitive saccharides (fructose) and in complex media (urine) without sample pretreatment. The present fluorescent sensors allow for quantification of glucose in a concentration range of 0−60 mM, which matches the concentration range of frequently used urinalysis test strips.

Controlling the morphology of colloidal semiconductor nanocrystals (NCs) remains to be a challenging task. Traditional growth strategies employ a high concentration of monomers to promote particle nucleation, which tends to oversaturate the solution with reactive species. This leads to secondary nucleation events and other dispersion-broadening processes. Here, we explore monomer-deprived synthetic conditions as a bilateral strategy for tuning both the shape and the surface-ligand chemistry of semiconductor colloids. Rather than controlling the nucleation phase, the present method employs a postsynthetic treatment based on low-temperature digestive ripening, where small particles grow at the expense of larger ones. The feasibility of the present approach was demonstrated by observing a 4-fold reduction in the CdSe nanoparticle size dispersion during the digestive ripening reaction, which was induced by high concentrations of L-type (amines) or X-type (oleic acid) ion-solubilizing ligands. In the latter case, a classically forbidden L → X ligand exchange was enabled by the concurrent process of the surface ion diffusion. The size-focusing capacity of the technique were subsequently demonstrated by ripening ZnSe NCs, which shape homogeneity is generally difficult to achieve.

The newly prepared fluorescent carboxyamidoquinolines (1-3) and their Zn(II) complexes (Zn@1-Zn@3) were used to bind and sense various phosphate anions utilizing a relay mechanism, in which the Zn(II) ion migrates from the Zn@1-Zn@3 complexes to the phosphate, namely adenosine 5'-triphosphate (ATP) and pyrophosphate (PPi), a process accompanied by a dramatic change in fluorescence. Zn@1-Zn@3 assemblies interact with adenine nucleotide phosphates while displaying an analyte-specific response. This process was investigated using UV-vis, fluorescence, and NMR spectroscopy. It is shown that the different binding selectivity and the corresponding fluorescence response enable differ-entiation of adenosine 5'-triphosphate (ATP), adenosine 5'diphosphate (ADP), pyrophosphate (PPi), and phosphate (Pi). The cross-reactive nature of the carboxyamidoquinolines-Zn(II) sensors in conjunction with linear discriminant analysis (LDA) was utilized in a simple fluorescence chemosensor array that allows for the identification of ATP, ADP, PPi, and Pi from 8 other anions including adenosine 5'-monophosphate (AMP) with 100 % correct classification. Furthermore, the support vector machine algorithm, a machine learning method, allowed for highly accurate quantitation of ATP in the range of 5-100 μM concentration in unknown samples with error < 2.5 %.