The organ-on-a-chip (OOAC) is in the list of top 10 emerging technologies and refers to a physiological organ biomimetic system built on a microfluidic chip. Through a combination of cell biology, engineering, and biomaterial technology, the microenvironment of the chip simulates that of the organ in terms of tissue interfaces and mechanical stimulation. This reflects the structural and functional characteristics of human tissue and can predict response to an array of stimuli including drug responses and environmental effects. OOAC has broad applications in precision medicine and biological defense strategies. Here, we introduce the concepts of OOAC and review its application to the construction of physiological models, drug development, and toxicology from the perspective of different organs. We further discuss existing challenges and provide future perspectives for its application.

Theranostic sonosensitizers with combined sonodynamic and near infrared (NIR) imaging modes are required for imaging guided sonodynamic therapy (SDT). It is challenging, however, to realize a single material that is simultaneously endowed with both NIR emitting and sonodynamic activities. Herein, we report the design of a class of NIR-emitting sonosensitizers from a NIR phosphorescent carbon dot (CD) material with a narrow bandgap (1.62 eV) and long-lived excited triplet states (11.4 μs), two of which can enhance SDT as thermodynamically and dynamically favorable factors under low-intensity ultrasound irradiation, respectively. The NIR-phosphorescent CDs are identified as bipolar quantum dots containing both p- and n-type surface functionalization regions that can drive spatial separation of e⁻–h⁺ pairs and fast transfer to reaction sites. Importantly, the cancer-specific targeting and high-level intratumor enrichment of the theranostic CDs are achieved by cancer cell membrane encapsulation for precision SDT with complete eradication of solid tumors by single injection and single irradiation. These results will open up a promising approach to engineer phosphorescent materials with long-lived triplet excited states for sonodynamic precision tumor therapy.

Over the past decade, the richness of electronic properties of graphene has attracted enormous interest for electrically detecting chemical and biological species using this two-dimensional material. However, the creation of practical graphene electronic sensors greatly depends on our ability to understand and maintain a low level of electronic noise, the fundamental reason limiting the sensor resolution. Conventionally, to reach the largest sensing response, graphene transistors are operated at the point of maximum transconductance, where 1/f noise is found to be unfavorably high and poses a major limitation in any attempt to further improve the device sensitivity. We show that operating a graphene transistor in an ambipolar mode near its neutrality point can markedly reduce the 1/f noise in graphene. Remarkably, our data reveal that this reduction in the electronic noise is achieved with uncompromised sensing response of the graphene chips and thus significantly improving the signal-to-noise ratio—compared to that of a conventionally operated graphene transistor for conductance measurement. As a proof-of-concept demonstration of the usage of the aforementioned new sensing scheme to a broader range of biochemical sensing applications, we selected an HIV-related DNA hybridization as the test bed and achieved detections at picomolar concentrations.

In data-driven materials design where the target materials have limited data, the transfer machine learning from large known source materials, becomes a demanding strategy especially across different crystal structures. In this work, we proposed a deep transfer learning approach to predict thermodynamically stable perovskite oxides based on a large computational dataset of spinel oxides. The deep neural network (DNN) source domain model with “Center-Environment” (CE) features was first developed using the formation energy of 5329 spinel oxide structures and then was fine-tuned by learning a small dataset of 855 perovskite oxide structures, leading to a transfer learning model with good transferability in the target domain of perovskite oxides. Based on the transferred model, we further predicted the formation energy of potential 5329 perovskite structures with combination of 73 elements. Combining the criteria of formation energy and structure factors including tolerance factor (0.7 < t ≤ 1.1) and octahedron factor (0.45 < μ < 0.7), we predicted 1314 thermodynamically stable perovskite oxides, among which 144 oxides were reported to be synthesized experimentally, 10 oxides were predicted computationally by other literatures, 301 oxides were recorded in the Materials Project database, and 859 oxides have been first reported. Combing with the structure-informed features the transfer machine learning approach in this work takes the advantage of existing data to predict new structures at a lower cost, providing an effective acceleration strategy for the expensive high-throughput computational screening in materials design. The predicted stable novel perovskite oxides serve as a rich platform for exploring potential renewable energy and electronic materials applications.