Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination.

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Enantiopure Corral[4]BINOLs as Ultrastrong Receptors for Recognition and Differential Sensing of Steroids.

The precise recognition and sensing of steroids, a type of vital biomolecules, hold immense practical value across various domains. In this study, we introduced corral[4]BINOLs (C[4]BINOLs), a pair of enantiomeric conjugated deep-cavity hosts, as novel synthetic receptors for binding steroids. Due to the strong hydrophobic effect of their deep nonpolar, chiral cavities, the two enantiomers of C[4]BINOLs demonstrated exceptionally high recognition affinities (up to 1012 M-1) for 16 important steroidal compounds as well as good enantioselectiviy (up to 15.5) in aqueous solutions, establishing them as the most potent known steroid receptors. Harnessing their ultrahigh affinity, remarkable enantioselectivity, and fluorescence emission properties, the two C[4]BINOL enantiomers were employed to compose a fluorescent sensor array which achieved discrimination and sensing of 16 structurally similar steroids at low concentrations.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Molecular Recognition with Macrocyclic Receptors for Application in Precision Medicine.

ConspectusMacrocyclic receptors can serve as alternatives to natural recognition systems as recognition tools. They provide effectively preorganized cavities to encapsulate guests via host-guest interactions, thereby affecting the physiochemical properties of the guests. Macrocyclic receptors exhibit chemical and thermal stabilities higher than those of natural receptors and thus are expected to resist degradation inside the body. This reduces the risk of harmful degradation byproducts and ensures optimal levels of effectiveness. Macrocyclic receptors have precise molecular weights and well-defined structures; this ensures their batch-to-batch reproducibility, which is critical for ensuring quality and effectiveness levels. Moreover, macrocyclic receptors exhibit broad modification tunabilities, rendering them adaptable to various guests. Molecular recognition is the basis of numerous biological processes. Macrocyclic receptors may display considerable potential for application in diagnosing and treating diseases, depending on the host-guest recognition of bioactive molecules. However, the binding affinities and selectivities of macrocyclic receptors toward bioactive molecules are generally insufficient, which may lead to problems such as low diagnosis accuracies, off-target leaking, and interference with normal functions. Therefore, addressing the challenge of the strong and specific complexation of bioactive molecules and macrocyclic receptors is imperative.To overcome this challenge, we proposed the innovative strategies of longitudinal cavity extension and coassembled heteromultivalent recognition for application in the recognition of small molecules and biomacromolecules, respectively. The deepened cavity provides a stronger hydrophobic effect and a larger interaction area while maintaining the framework rigidity. By coassembling two macrocyclic amphiphiles into one ensemble, we achieved the desired heteromultivalent recognition. This strategy affords the necessary binding properties while preventing the requirement of tedious steps and site mismatch in covalent synthesis. Using these two strategies, we achieved specific and strong binding of macrocyclic receptors to various bioactive molecules including biomarkers, drugs, and disease-related peptides/proteins. We then applied these macrocyclic receptor-based recognition systems in biosensing and bioimaging, drug delivery, and therapeutics.In this Account, we summarize the strategies we used in the recognition of small molecules and biomacromolecules. Thereafter, we discuss their applications in precision medicine, involving the (1) sensing of biomarkers and imaging of lesion sites, which are critical in the early screening of diseases and accurate diagnoses; (2) precise loading and targeted delivery of drugs, which are crucial in improving their therapeutic efficacies and reducing their side effects; and (3) capture and removal of disease-related biomacromolecules, which are significant for precise intervention in life processes. Finally, we propose recommendations for the further development of macrocyclic receptor-based recognition systems in biomedicine. Macrocyclic receptors exhibit considerable potential for research, and continued investigation may not only expand the applications of supramolecular chemistry but also open novel avenues for the development of precision medicine.

A heteromultivalent host-guest sensor array for cell recognition and discrimination.

We present a supramolecular sensor array based on a series of heteromultivalent macrocyclic coassemblies using amphiphilic calixarenes and cyclodextrin as the building blocks for cell recognition. The corresponding cross-reactivity between the coassemblies and cells served as the unique fingerprint for cell classification, and successfully identified the normal cell lines, cancerous cell lines, and cross-contaminated cells.

A facile way to construct sensor array library via supramolecular chemistry for discriminating complex systems.

Differential sensing, which discriminates analytes via pattern recognition by sensor arrays, plays an important role in our understanding of many chemical and biological systems. However, it remains challenging to develop new methods to build a sensor unit library without incurring a high workload of synthesis. Herein, we propose a supramolecular approach to construct a sensor unit library by taking full advantage of recognition and assembly. Ten sensor arrays are developed by replacing the building block combinations, adjusting the ratio between system components, and changing the environment. Using proteins as model analytes, we examine the discriminative abilities of these supramolecular sensor arrays. Then the practical applicability for discriminating complex analytes is further demonstrated using honey as an example. This sensor array construction strategy is simple, tunable, and capable of developing many sensor units with as few syntheses as possible.