Poly(p-phenyleneethynylene)-based tongues discriminate fruit juices

Oligo(phenyleneethynylene)s: Shape-Tunable Building Blocks for Supramolecular Self-Assembly

Oligo(phenyleneethynylene)s (OPEs) have attracted widespread attention due to their remarkable (opto)electronic and photophysical properties, which have enabled numerous applications. The versatile functionalization possibilities of OPEs make them unique candidates to form various shape-persistent geometries, including linear, triangular, rectangular, hexagonal and macrocyclic. However, as a result of this structural variety, it is oftentimes challenging to correlate molecular design with self-assembly properties. In this minireview, we have classified OPEs based on their molecular shapes and correlated them with their self-assembly behavior in solution. Particularly, we provide important insights into the aggregation propensity of the different molecular shapes and how to tune the association strength using various non-covalent interactions. Our classification will enable a better understanding of the structure-property correlation in OPEs, which is key to develop supramolecular functional materials.

Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay

Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.

Machine Learning-Assisted Nanoenzyme/Bioenzyme Dual-Coupled Array for Rapid Detection of Amyloids

Array-based sensing methods offer significant advantages in the simultaneous detection of multiple amyloid biomarkers and thus have great potential for diagnosing early-stage Alzheimer's disease. Yet, detecting low concentrations of amyloids remains exceptionally challenging. Here, we have developed a fluorescent sensor array based on the dual coupling of a nanoenzyme (AuNPs) and bioenzyme (horseradish peroxidase) to detect amyloids. Various ss-DNAs were bound to the nanoenzyme for regulating enzymatic activity and recognizing amyloids. A simplified sensor array was generated from a screening model via machine learning algorithms and achieved signal amplification through a two-step enzymatic reaction. As a result, our sensing system could discriminate the aggregation species and aggregation kinetics at 200 nM with 100% accuracy. Moreover, AD model mice and healthy mice were distinguished with 100% accuracy through the sensor array, providing a powerful sensing platform for diagnosing AD.

A simple array integrating machine learning for identification of flavonoids in red wines

Bioactive flavonoids, the major ingredients of red wines, have been proven to prevent atherosclerosis and cardiovascular disease due to their anti-inflammatory and anti-oxidant activity. However, flavonoids have proven challenging to identify, even when multiple approaches are combined. Hereby, a simple array was constructed to detect flavonoids by employing phenylboronic acid modified perylene diimide derivatives (PDIs). Through multiple non-specific interactions (hydrophilic, hydrophobic, charged, aromatic, hydrogen-bonded and reversible covalent interactions) with flavonoids, the fluorescence of PDIs can be modulated, and variations in intensity can be used to create fingerprints of flavonoids. This array successfully discriminated 14 flavonoids of diverse structures and concentrations with 100% accuracy, based on patterns in fluorescence intensity modulation, via optimized machine learning algorithms. As a result, this array demonstrated the parallel detection of 8 different types and origins of red wines with a high accuracy, revealing the excellent potential of the sensor array in food mixtures detection.

Evaluation of Discrimination Performance in Case for Multiple Non-Discriminated Samples: Classification of Honeys by Fluorescent Fingerprinting

In this study we develop a variant of fluorescent sensor array technique based on addition of fluorophores to samples. A correct choice of fluorophores is critical for the successful application of the technique, which calls for the necessity of comparing different discrimination protocols. We used 36 honey samples from different sources to which various fluorophores were added (tris-(2,2'-bipyridyl) dichlororuthenium(II) (Ru(bpy)₃²⁺), zinc(II) 8-hydroxyquinoline-5-sulfonate (8-Ox-Zn), and thiazole orange in the presence of two types of deoxyribonucleic acid). The fluorescence spectra were obtained within 400-600 nm and treated by principal component analysis (PCA). No fluorophore allowed for the discrimination of all samples. To evaluate the discrimination performance of fluorophores, we introduced crossing number (CrN) calculated as the number of mutual intersections of confidence ellipses in the PCA scores plots, and relative position (RP) characterized by the pairwise mutual location of group centers and their most distant points. CrN and RP parameters correlated with each other, with total sensitivity (TS) calculated by Mahalanobis distances, and with the overall rating based on all metrics, with coefficients of correlation over 0.7. Most of the considered parameters gave the first place in the discrimination performance to Ru(bpy)₃²⁺ fluorophore.

Functional Polymers and Polymer-Dye Composites for Food Sensing

The sensitive, safe, and portable detection of food spoilage is becoming unprecedentedly important because it is closely related to the public health and economic development, particularly given the globalization of food supply chain. However, the existing approaches for food monitoring are still limited to meet these requirements. To address this challenge, much research has been done to develop an ideal food sensor that can indicate food quality in real-time in a sensitive and reliable way. So far, many sensors such as time-temperature indicators, smart trademarks, colorimetric tags, electronic noses, and electronic tongues, have been developed and even commercialized. In this feature article, the recent progress of food sensors based on functional polymers, including the molecular design of polymer structures, sensing mechanisms, and relevant processing techniques to fabricate a variety of food sensor devices is reviewed.

A colorimetric sensor array based on natural pigments for the discrimination of saccharides

A colorimetric sensor array based on natural pigments was developed to discriminate between various saccharides. Anthocyanins, pH-sensitive natural pigments, were extracted from fruits and flowers and used as components of the sensor array. Variation in pH, due to the reaction between saccharides and boronic acids, caused obvious colour changes in the natural pigments. Only by observing the difference map with the naked eye could 11 common saccharides be divided into independent individuals. In conjunction with pattern recognition, the sensor array clearly differentiated between sugar and sugar alcohol with highly accuracy and allowed rapid quantification of different concentrations of maltitol and fructose. This sensor array for saccharides is expected to become a promising alternative tool for food monitoring. The link between anthocyanin and saccharide detection opened a new guiding direction for the application of anthocyanins in foods.

Simple and robust polymer-based sensor for rapid cancer detection using serum

We report a polymer-based sensor that rapidly detects cancer based on changes in serum protein levels. Using three ratiometric fluorescence outputs, this simple system identifies early stage and metastatic lung cancer with a high level of accuracy exceeding many biomarker-based assays, making it an attractive strategy for point-of-care testing.

Upconversion fluorescent nanoparticles based-sensor array for discrimination of the same variety red grape wines

A fluorescent sensor array composed of upconversion nanomaterials to distinguish the same variety of red grape wines was constructed.

Detecting Counterfeit Brandies

A hypothesis-free sensor array (optoelectronic tongue) composed of an anionic, a cationic and two neutral poly(para-aryleneethynylene)s (PAE) at pH 3, 7 and 13 discriminate more than 30 spirits (including brandy, Branntwein, Cognac, Spirituose, and Weinbrand). Counterfeits (made by mixing of low-quality spirits and caramel colour) and different batches of identical brands of brandies are discriminated. The sensor array works without sample preparation or great instrumental cost, and is superior to conventional methods with respect to sample need (10-20 μL), time and effort. The discrimination stems from differential fluorescence quenching of the PAE-array by the complex mixture of the beverages' colourants, from the oak barrels or added caramel colour. The collected quenching data were analysed by linear discriminant analysis (LDA) and principal component analysis (PCA) to achieve successful discrimination.

Poly(para-phenyleneethynylene)-Sensor Arrays Discriminate 22 Different Teas

Two nine-element sensor arrays, consisting of either three cationic poly(para-phenyleneethynylene)s (PPE) or the same PPEs complexed by cucurbituril[8] (CB[8]) at pH 3, 7, and 13 in water, discriminate 22 different teas and some of their small molecule components, including caffeine, theobromine and theophylline. Both arrays distinguish all of the black, green and oolong teas. The discrimination occurs by differential fluorescence modulation of the components of the sensor array and the treatment of the collected data by linear discriminant analysis. The signal is generated by either simple quenching (PPE only array) or the disruption of the PPE/CB[8] complex and quenching of the complex's or the PPEs' fluorescence through the polyphenolic colorants of the teas. Added amino acids, theobromine, theophylline, and caffeine give a fluorescence turn on of the PPE-CB[8] array, due to the disruption of the self-assembled complex, while for the PPE-alone tongue only caffeine, theobromine, and theophylline elicited useful fluorescence response. Both tongues discriminate different teas without any problem.

A Simple Optoelectronic Tongue Discriminates Amino Acids

A self-assembled nine-element optoelectronic tongue consisting of a positively charged water-soluble poly(para-phenyleneethynylene) and three metal ions (Fe²⁺ , Co²⁺ , and Cu²⁺ ) at three different pH values (7, 10, and 13) discriminates all of the 20 natural amino acids in water. Unknown identification was not ideal. Addition of a highly positively charged green fluorescent protein in the presence of Fe²⁺ , Co²⁺ , and Cu²⁺ increased the unknown identification to above 86 %. Linear discriminant analysis (LDA) orders the responses according to the amino acid type, that is, hydrophobic, polar, anionic, or cationic.