Hydrophobic and Rapid-Response Sensor Inks: Array-Based Fingerprinting of Perfumes

MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.

Iridium(III) solvent complex-based electrogenerated chemiluminescence and photoluminescence sensor array for the discrimination of bases in oligonucleotides

Development of rapid and sensitive method for the discrimination of bases in oligonucleotides is of great importance in clinical diagnosis. Here, we demonstrate the first case of single iridium(III) solvent complex-based electrogenerated chemiluminescence (ECL) and photoluminescence (PL) sensor array for the discrimination of bases in oligonucleotides. One iridium (III) solvent complex ([Ir(ppy)₂(DMSO)Cl], ppy = 2-phenylpyridine, probe 1) was designed as both ECL and PL probe while five bases (guanine, adenine, cytosine, thymine and uracil) were chosen as analytes. Two-element sensor array was built for the discrimination of five bases based on the fingerprint response of probe 1 to bases via coordination interactions. The combination of unique ECL and PL variations with principal component analysis was applied for the quantitative analysis of five bases in a linear range of 1.0 μM-10 μM and for the effective discrimination of individual base, the mixture of bases and oligonucleotides. Moreover, the sensor array was successfully applied to discriminate different mismatched ss-DNAs from HIV gene (a fully-matched ss-DNA), even at single-base difference. This work demonstrates that the sensor array using single iridium (III) solvent complex is a promising approach for the discrimination of bases with good sensitivity and simpleness in clinical diagnosis.

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness

Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.