Accurate detection and early diagnosis of oral diseases such as dental caries and periodontitis, can be potentially achieved by detecting the secretion of volatile sulfur compounds (VSCs) in oral cavities. Current diagnostic approaches for VSCs can detect the existence and concentrations, yet are not capable of locating the dental lesion sites. Herein, the development of a unique approach for accurately locating dental lesion sites using a fluorescent mouthguard consisting of the zinc oxide–poly(dimethylsiloxane) (ZnO‐PDMS) nanocomposite to detect the local release of VSCs is reported. The ZnO‐PDMS mouthguard displays a highly sensitive and selective response to VSCs, and exhibits high fluorescent stability, good biocompatibility, and low biological toxicity in normal physiological environments. Then, the wearable ZnO‐PDMS mouthguard is demonstrated to be able to identify the precise locations of lesion sites in human subjects. Combined with image analysis, the mouthguards successfully uncover the precise locations of dental caries, allowing convenient screening of hidden dental lesion sites that are oftentimes omitted by dentists. Due to low cost, long‐term stability, and good patient compliance, the proposed wearable mouthguard is suitable for large‐scale production and enables widely applicable, preliminary yet accurate screening of dental lesions prior to dental clinics and routine physical examinations.

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A microfluidic hydrogel droplet platform was combined with deep learning for high-throughput screening of the antisolvent-crystallization conditions of active pharmaceutical ingredients.

In 3D (bio)printing, it is critical to optimize the printing conditions to obtain scaffolds with designed structures and good uniformities. Traditional approaches for optimizing the parameters oftentimes rely on the prior knowledge of the operators and tedious optimization experiments, which can be both time‐consuming and labor‐intensive. Moreover, with the rapid increase in the types of biomaterial inks and the geometrical complexities of the scaffolds to be fabricated, such a traditional strategy may prove less effective. To address the challenge, an artificial intelligence‐assisted high‐throughput printing‐condition‐screening system (AI‐HTPCSS) is proposed, which is composed of a programmable pneumatic extrusion (bio)printer and an AI‐assisted image‐analysis algorithm. Based on the AI‐HTPCSS, the printing conditions for obtaining uniformly structured hydrogel architectures are screened in a high‐throughput manner. The results show that the scaffolds printed under the optimized conditions demonstrate satisfying mechanical properties, in vitro biological performances, and efficacy in accelerating the diabetic wound healing in vivo. The unique AI‐HTPCSS is expected to offer an enabling platform technology on streamlining the manufacturing of tissue‐engineering scaffolds through 3D (bio)printing techniques in the future.

High-throughput screening is a potent technique to accelerate the discovery and development of new materials. By performing massive synthesis and characterization processes in parallel, it can rapidly discover materials with desired components, structures and functions. Among the various approaches for high-throughput screening, microfluidic platforms have attracted increasing attention. Compared with many current strategies that are generally based on robotic dispensers and automatic microplates, microfluidic platforms can significantly increase the throughput and reduce the consumption of reagents by several orders of magnitude. In this review, we first introduce current advances of the two types of microfluidic high-throughput platforms based on microarrays and microdroplets, respectively. Then the utilization of these platforms for screening different types of materials, including inorganic metals, metal alloys and organic polymers are described in detail. Finally, the challenges and opportunities in this promising field are critically discussed.